From d078b7599b02fe58405a101b8aadc3b21b86c903 Mon Sep 17 00:00:00 2001 From: Sharvin J Date: Wed, 28 May 2025 14:40:58 +0530 Subject: [PATCH 01/37] Add grpo job example (#3589) * Add grpo job example * Replace client info with generics * Add missing copyright * newline at end of aml_setup.py * Fix black formatting issues and reduce dataset * Update sdk/python/jobs/grpo/src/grpo_trainer_rewards.py Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> * Update sdk/python/jobs/grpo/src/BldDemo_Reasoning_Train.py Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> * Update sdk/python/jobs/grpo/src/BldDemo_Reasoning_Train.py Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> * Fix duplicated cell * Add to CODEOWNERS * Fix agenda image and dataset string * Replace demo with example * Update sdk/python/jobs/grpo/launch_grpo_command_job-med-mcqa-commented.ipynb Co-authored-by: Gayatri Penumetsa <181455625+gpenumetsa-msft@users.noreply.github.com> * Update sdk/python/jobs/grpo/launch_grpo_command_job-med-mcqa-commented.ipynb Co-authored-by: Gayatri Penumetsa <181455625+gpenumetsa-msft@users.noreply.github.com> * Update sdk/python/jobs/grpo/launch_grpo_command_job-med-mcqa-commented.ipynb Co-authored-by: Gayatri Penumetsa <181455625+gpenumetsa-msft@users.noreply.github.com> * Update sdk/python/jobs/grpo/launch_grpo_command_job-med-mcqa-commented.ipynb Co-authored-by: Gayatri Penumetsa <181455625+gpenumetsa-msft@users.noreply.github.com> * Address deployment related comments * Add README * Update sdk/python/jobs/grpo/launch_grpo_command_job-med-mcqa-commented.ipynb Co-authored-by: Gayatri Penumetsa <181455625+gpenumetsa-msft@users.noreply.github.com> * Remove duplicated model info section * Update sdk/python/jobs/grpo/launch_grpo_command_job-med-mcqa-commented.ipynb Co-authored-by: Gayatri Penumetsa <181455625+gpenumetsa-msft@users.noreply.github.com> * Update sdk/python/jobs/grpo/launch_grpo_command_job-med-mcqa-commented.ipynb Co-authored-by: Gayatri Penumetsa <181455625+gpenumetsa-msft@users.noreply.github.com> * Change dataset description --------- Co-authored-by: Sharvin Jondhale Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> Co-authored-by: Gayatri Penumetsa <181455625+gpenumetsa-msft@users.noreply.github.com> --- .github/CODEOWNERS | 1 + sdk/python/jobs/grpo/README.md | 13 + sdk/python/jobs/grpo/aml_setup.py | 183 ++++++ .../jobs/grpo/datasets/med_mcqa/test.jsonl | 50 ++ .../jobs/grpo/datasets/med_mcqa/train.jsonl | 50 ++ .../grpo/datasets/med_mcqa/validation.jsonl | 50 ++ sdk/python/jobs/grpo/environment/Dockerfile | 19 + .../jobs/grpo/environment/requirements.txt | 159 +++++ sdk/python/jobs/grpo/images/agenda.png | Bin 0 -> 86680 bytes sdk/python/jobs/grpo/images/deepspeed.png | Bin 0 -> 213723 bytes .../grpo/images/deepspeed_config_explain.png | Bin 0 -> 210070 bytes sdk/python/jobs/grpo/images/grpo_trainer.png | Bin 0 -> 130732 bytes sdk/python/jobs/grpo/images/metrics.png | Bin 0 -> 93549 bytes sdk/python/jobs/grpo/images/reward_func.png | Bin 0 -> 36082 bytes .../jobs/grpo/images/reward_weights.png | Bin 0 -> 59281 bytes sdk/python/jobs/grpo/images/training_loop.png | Bin 0 -> 139652 bytes sdk/python/jobs/grpo/images/vllm.png | Bin 0 -> 56512 bytes ..._grpo_command_job-med-mcqa-commented.ipynb | 590 ++++++++++++++++++ .../jobs/grpo/src/BldDemo_Reasoning_Train.py | 215 +++++++ .../src/deepspeed_stage3_zero_config.json | 44 ++ .../jobs/grpo/src/grpo_trainer_callbacks.py | 111 ++++ .../jobs/grpo/src/grpo_trainer_config.yaml | 76 +++ .../jobs/grpo/src/grpo_trainer_rewards.py | 167 +++++ 23 files changed, 1728 insertions(+) create mode 100644 sdk/python/jobs/grpo/README.md create mode 100644 sdk/python/jobs/grpo/aml_setup.py create mode 100644 sdk/python/jobs/grpo/datasets/med_mcqa/test.jsonl create mode 100644 sdk/python/jobs/grpo/datasets/med_mcqa/train.jsonl create mode 100644 sdk/python/jobs/grpo/datasets/med_mcqa/validation.jsonl create mode 100644 sdk/python/jobs/grpo/environment/Dockerfile create mode 100644 sdk/python/jobs/grpo/environment/requirements.txt create mode 100644 sdk/python/jobs/grpo/images/agenda.png create mode 100644 sdk/python/jobs/grpo/images/deepspeed.png create mode 100644 sdk/python/jobs/grpo/images/deepspeed_config_explain.png create mode 100644 sdk/python/jobs/grpo/images/grpo_trainer.png create mode 100644 sdk/python/jobs/grpo/images/metrics.png create mode 100644 sdk/python/jobs/grpo/images/reward_func.png create mode 100644 sdk/python/jobs/grpo/images/reward_weights.png create mode 100644 sdk/python/jobs/grpo/images/training_loop.png create mode 100644 sdk/python/jobs/grpo/images/vllm.png create mode 100644 sdk/python/jobs/grpo/launch_grpo_command_job-med-mcqa-commented.ipynb create mode 100644 sdk/python/jobs/grpo/src/BldDemo_Reasoning_Train.py create mode 100644 sdk/python/jobs/grpo/src/deepspeed_stage3_zero_config.json create mode 100644 sdk/python/jobs/grpo/src/grpo_trainer_callbacks.py create mode 100644 sdk/python/jobs/grpo/src/grpo_trainer_config.yaml create mode 100644 sdk/python/jobs/grpo/src/grpo_trainer_rewards.py diff --git a/.github/CODEOWNERS b/.github/CODEOWNERS index cb9d0d0717..be8ebdec39 100644 --- a/.github/CODEOWNERS +++ b/.github/CODEOWNERS @@ -25,6 +25,7 @@ sdk/python/foundation-models/cohere/command_tools-langchain.ipynb @stewart-co @k /sdk/python/foundation-models/healthcare-ai/ @jmerkow @ivantarapov /sdk/python/assets/data/versioning.ipynb @ShakutaiGit /sdk/python/jobs/finetuning @amltres @sasum @marici +/sdk/python/jobs/grpo @sharvin2187 @rtanase @gpenumetsa-msft #### files referenced in docs (DO NOT EDIT, except for Docs team!!!) ############################################################################################# /cli/assets/component/train.yml @Azure/AI-Platform-Docs diff --git a/sdk/python/jobs/grpo/README.md b/sdk/python/jobs/grpo/README.md new file mode 100644 index 0000000000..aecad68046 --- /dev/null +++ b/sdk/python/jobs/grpo/README.md @@ -0,0 +1,13 @@ +### This directory hosts an example of running GRPO on AzureML + +#### Repo structure: +- aml_setup.py: AzureML specific code relating to creation of dataset, model and environment. The workspace config is located here, make changes to this file before running the notebook. +- launch_grpo_command_job-med-mcqa-commented.ipynb: Entrypoint for the directory. In most cases, developers will just run this notebook; after adjusting the AzureML configuration. +- datasets/medmcqa: It has 3 jsonl files for train, test and validation. Each record in the jsonl has 2 important fields, **problem** (column) which is the prompt encouraging the model to do reasoning and the golden **solution** +- environment: This is the definition of the [AzureML environment](https://learn.microsoft.com/en-us/azure/machine-learning/concept-environments?view=azureml-api-2) in which the training job will run. +- src/ + - BldDemo_Reasoning_Train.py: Code relating to creating an instance of the GRPOTrainer class from trl with correct configurations. + - grpo_trainer_callbacks.py: Code which converts the output models to MLflow model format. This conversation simplifies deployment as AzureML automatically generated inferencing environment and server for models in this format. + - grpo_trainer_rewards.py: Rewards for the training, these are python functions which take an LLM response and grade it for accuracy and format. + +Additionally, [this video](https://youtu.be/YOm_IQt3YWw?si=5nZzyy-PZyP9XFSU&t=1344) offers an overview based on the contents of this repository. \ No newline at end of file diff --git a/sdk/python/jobs/grpo/aml_setup.py b/sdk/python/jobs/grpo/aml_setup.py new file mode 100644 index 0000000000..2b6f2b13e8 --- /dev/null +++ b/sdk/python/jobs/grpo/aml_setup.py @@ -0,0 +1,183 @@ +# --------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# --------------------------------------------------------- +""" +Install necessary libraries and packages required for running AML training jobs. +""" +from azure.ai.ml import MLClient, command, Input, Output +from azure.ai.ml.constants import AssetTypes +from azure.ai.ml.entities import Model + +# Import necessary classes for environment management +from azure.ai.ml.entities import ( + ManagedOnlineEndpoint, + ManagedOnlineDeployment, + Model, + Data, + AmlCompute, + Environment, + BuildContext, +) +from azure.core.exceptions import ResourceNotFoundError +from rich import print +from azure.identity import InteractiveBrowserCredential, DefaultAzureCredential + +try: + credential = DefaultAzureCredential() +except Exception: + # Use interactive browser-based authentication + credential = InteractiveBrowserCredential() + +# Input your Azure subscription ID, resource group name, and workspace name. +# You can find these values in the Azure portal. +AML_SUBSCRIPTION = "" +AML_RESOURCE_GROUP = "" +AML_WORKSPACE_NAME = "" + +# Initialize the MLClient to connect to your Azure ML workspace +ml_client = MLClient( + credential=credential, + subscription_id=AML_SUBSCRIPTION, + resource_group_name=AML_RESOURCE_GROUP, + workspace_name=AML_WORKSPACE_NAME, +) + + +def setup_dataset(): + """ + Register the medical MCQA dataset in the Azure ML workspace. + The datasets will be referred from the ./datasets folder. + """ + med_mcqa_dataset = "dataset-med-mcqa" + med_mcqa_data_path = "./datasets/med_mcqa" + med_mcqa_data = ml_client.data.create_or_update( + Data( + path=med_mcqa_data_path, + type=AssetTypes.URI_FOLDER, + description="Training dataset with medical mcqa data", + name=med_mcqa_dataset, + ) + ) + print(f"✅ Dataset {med_mcqa_dataset} created in AML workspace.") + return med_mcqa_data + + +def setup_model(): + """ + Download and Register the model in the Azure ML workspace. + """ + # Model details + model_id = "Qwen/Qwen2.5-7B-Instruct" # Hugging Face model ID + download_path = "./models" # Local download path + model_name = "Qwen2_5-7B-Instruct_base" # Name for Azure ML registration + + # Download model from Hugging Face and register in Azure ML workspace + try: + model = ml_client.models.get(name=model_name, label="latest") + print(f"✅ Model {model_name} created in AML workspace.") + except ResourceNotFoundError: + print(f"❌ Model {model_name} not found, downloading and registering...") + from huggingface_hub import snapshot_download + + snapshot_download( + repo_id=model_id, + local_dir=download_path, + # token=os.environ["HF_TOKEN"] # Uncomment if authentication is needed + ) + model = ml_client.models.create_or_update( + Model(name=model_name, path=download_path, type=AssetTypes.CUSTOM_MODEL) + ) + print(f"✅ Model registered in AML workspace: {model.id}") + return model + + +def setup_compute(): + """ + Create a compute cluster in the Azure ML workspace. + """ + # Compute Cluster Setup: Select or Create GPU Compute for Training + + # Specify the desired Azure VM size (default: 8 x H100 GPUs). This job requires falsh attention and needs A100 or H100 GPUs. + compute_cluster_size = "STANDARD_ND96ISR_H100_V5" + + # Name of the compute cluster to use (change if you have a different cluster) + compute_cluster = "grpo-h100-2" + + try: + # Try to get the existing compute cluster + compute = ml_client.compute.get(compute_cluster) + print(f"✅ Compute cluster {compute_cluster} created in AML workspace.") + except Exception: + print( + f"❌ Compute cluster '{compute_cluster}' not found. Creating a new one ({compute_cluster_size})..." + ) + try: + print("🔄 Creating dedicated GPU compute cluster...") + compute = AmlCompute( + name=compute_cluster, + size=compute_cluster_size, + tier="Dedicated", + max_instances=2, # Increase for multi-node training + min_instances=2, + ) + ml_client.compute.begin_create_or_update(compute).wait() + print("✅ Compute cluster created successfully.") + except Exception as e: + print(f"🚨 Error: {e}") + raise ValueError( + f"WARNING! Compute size '{compute_cluster_size}' not available in this workspace." + ) + + # Final check: Ensure the compute cluster is provisioned and ready + compute = ml_client.compute.get(compute_cluster) + if compute.provisioning_state.lower() == "failed": + raise ValueError( + f"🚫 Provisioning failed: Compute '{compute_cluster}' is in a failed state. " + f"Please try creating a different compute cluster." + ) + return compute + + +def setup_environment(): + """ + Setup environment for the training job. + """ + # Define the environment name + env_name = "grpo-training-env" + + try: + # Try to get the existing environment from Azure ML workspace + environment = ml_client.environments.get(name=env_name, label="latest") + print(f"✅ Environment {env_name} created in AML workspace.") + except ResourceNotFoundError as e: + # If not found, create a new environment using the specified build context + print(f"❌ Environment {env_name} not found, creating a new one.") + environment = ml_client.environments.create_or_update( + Environment( + build=BuildContext(path="./environment"), + name=env_name, + description="Environment for grpo trainer.", + ) + ) + print(f"✅ Environment registered in AML workspace: {environment.id}") + return environment + + +def setup(): + """ + Main function to setup the Azure ML workspace with dataset, model, compute, and environment. + """ + # Register the dataset + med_mcqa_data = setup_dataset() + + # Setup the model + model = setup_model() + + # Setup the compute cluster + compute = setup_compute() + + # Setup the environment + environment = setup_environment() + + print("✅ Setup completed successfully !!!") + return ml_client, med_mcqa_data, model, compute, environment diff --git a/sdk/python/jobs/grpo/datasets/med_mcqa/test.jsonl b/sdk/python/jobs/grpo/datasets/med_mcqa/test.jsonl new file mode 100644 index 0000000000..901cb4dc08 --- /dev/null +++ b/sdk/python/jobs/grpo/datasets/med_mcqa/test.jsonl @@ -0,0 +1,50 @@ +{"id":"b64a9cd7-d076-4c55-8be1-f9c44fece6cc","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nQuestion:\nA 29 yrs old woman with a pregnancy of 17 week has a 10 years old boy with down syndrome. She does not want another down syndrome kid; best advice to her is\nOptions:\nA. No test is required now as her age is below 35 years\nB. Ultra sound at this point of time will definitely tell her that next baby will be down syndromic or not\nC. Amniotic fluid samples plus chromosomal analysis will definitely tell her that next baby will be down syndromic or not\nD. blood screening at this point of time will clear the exact picture\n","solution":"C"} +{"id":"17360c6c-2c98-4fe2-aa85-487dcf4678df","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nQuestion:\nConcentration of tropicamide:\nOptions:\nA. 0.01\nB. 0.02\nC. 0.03\nD. 0.04\n","solution":"A"} +{"id":"ce49098b-cc48-4168-859e-936e3e0c7459","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nQuestion:\nWhich of the following are not a branch of external carotid Aery in Kiesselbach's plexus.\nOptions:\nA. Sphenopalatine aery\nB. Anterior ethmoidal aery\nC. Greater palatine aery\nD. Septal branch of superior labial aery\n","solution":"B"} +{"id":"18d5c4a1-cb81-41a8-9bfc-b6f7dec431d2","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nQuestion:\nDiagnosis of the following ECG-\nOptions:\nA. Ventricular bigeminy\nB. Electrical alternans\nC. P pulmonale\nD. Left ventricular failure\n","solution":"B"} +{"id":"384a9281-2ee1-480b-a7d8-fd3ef49558e5","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nQuestion:\n27.\tThe lateral spread of dental caries is facilitated mostly by the\nOptions:\nA. Enamel spindles\nB. Dentinoenamel junction\nC. Enamel lamellae\nD. Striae of Retzius\n","solution":"B"} +{"id":"84d89a4f-db04-402d-88ab-ca39f6eee8bd","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nQuestion:\nRespiratory rhythm generation center is located at:\nOptions:\nA. Dorsal respiratory group\nB. Pre-Botzinger complex\nC. Ventral respiratory neurons\nD. Pneumotaxic center\n","solution":"B"} +{"id":"b1cc4ab0-a82c-437a-88c2-00953f3618ff","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nQuestion:\nGait apraxia is seen in thromboembolic episode involving:\nOptions:\nA. ACA\nB. MCA\nC. PCA\nD. Posterior choroidal aery\n","solution":"A"} +{"id":"f184a533-98b4-43ad-b1f5-70edc6704d9e","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nQuestion:\nHeavy forces on periodontal ligament causes:\nOptions:\nA. Hyalinization\nB. Osteoclastic activity around tooth\nC. Osteoblastic activity around tooth\nD. Crest bone resorption\n","solution":"A"} +{"id":"e38402c3-69bf-40a8-90c4-09277ca352eb","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nQuestion:\nIn a\t6-month-old child, thick curd like white\tpatch\nappears on the buccal mucosa. On rubbing it leaves an\nerythematous patch. Most likely diagnosis is:\nOptions:\nA. Tuberculosis\nB. Lichen planus\nC. Lupus erythematous\nD. Candidiasis\n","solution":"D"} +{"id":"9078aaca-bbfd-41cd-ad69-03057fca84ba","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nQuestion:\nCharacteristic of venous blood flow of lower limb in duplex Doppler is?\nOptions:\nA. Monophasic\nB. Biphasic\nC. Triphasic\nD. Non phasic\n","solution":"A"} +{"id":"ac6be140-880b-40c6-9855-01f30c8dd7b2","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nQuestion:\nThe source of calcium ions of a calcific bridge in a\ntooth in which calcium hydroxide pulpotomy has been\nperformed is:\nOptions:\nA. Blood vessel borne\nB. Derided cells\nC. Dentin\nD. From the calcium hydroxide\n","solution":"A"} +{"id":"7111d7b2-7e80-4e23-b5d0-9f42595c6a80","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nQuestion:\n2, 3-BPG binds to sites of haemoglobin and the affinity for oxygen\nOptions:\nA. 4, decreases\nB. 1, decreases\nC. 4, increases\nD. 1, increases\n","solution":"B"} +{"id":"406b5b4f-9ad4-42c0-9669-12d038df4ac8","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nQuestion:\nHighest concentration of oxygen is delivered through?\nOptions:\nA. Nasal cannula\nB. Venturi mask\nC. Bag and mask\nD. Mask with reservoir\n","solution":"C"} +{"id":"8d2e7fe2-3354-44c2-8cdf-55ff23caaebb","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nQuestion:\nSharpest cusp is seen in\nOptions:\nA. Upper Canine\nB. Lower Canine\nC. Upper Premolar\nD. Lower premolar\n","solution":"A"} +{"id":"7f0270fd-7d45-43ec-b77e-0038115bb845","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nQuestion:\nA mother is Hbs Ag positive and anti Hbe Ag positive. Risk of transmission of Hep. B in child is:\nOptions:\nA. 20%\nB. 50%\nC. 0%\nD. 90%\n","solution":"A"} +{"id":"8b9a0e04-4281-418a-aa94-7414a325732f","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nQuestion:\nIn a patient with competent lips together at rest, the lip\nline is opposite the tips of the upper incisors. The lip line is then described as\nOptions:\nA. Average\nB. High\nC. Incomplete\nD. Low\n","solution":"D"} +{"id":"a3da135a-a6a2-4e1c-8823-8ab0913f5668","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nQuestion:\nA patient presented to emergency with overdose some drug. There were increased salivation and increased bronchial secretions. On examination, blood pressure was 88\/60 mmHg. RBC esterase level is 50. What should be the treatment of this person?\nOptions:\nA. Neostigmine\nB. Atropine\nC. Flumazenil\nD. Physostigmine\n","solution":"B"} +{"id":"7751b7ed-6aa4-4b9a-a03c-bb991a2936db","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nQuestion:\nThe current bar clasp design used is:\nOptions:\nA. Mesio-occlusal rest\nB. Disto-occlusal rest\nC. Mesial or distal depending on the situation\nD. Choice of the dentist\n","solution":"A"} +{"id":"f0f91922-12fd-4213-871c-19e47532a6d6","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nQuestion:\nIn a study, two groups of newborns are checked for their weights based on whether their mothers received food supplements or not. The appropriate test which can be used for comparing the data is:\nOptions:\nA. Chi square test\nB. Paired T-test\nC. Student's T-test\nD. Fischer exact test\n","solution":"C"} +{"id":"3624dceb-9318-4aa7-add1-b4c2fbac3065","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nQuestion:\nWhich vitamin is required for glycogen Phosphorylase?\nOptions:\nA. PLP\nB. TPP\nC. Riboflavin\nD. Lipoic acid\n","solution":"A"} +{"id":"e845e625-845e-4d02-ab3f-e00f2284d62e","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nQuestion:\nWhich of the following is the only complete cailage ring in the respiratory tree?\nOptions:\nA. Cricoid cailage\nB. Thyroid cailage\nC. Cunieform cailage\nD. Epiglottis\n","solution":"A"} +{"id":"67add9f9-94b5-40b2-904d-fb4965fb8309","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nQuestion:\nIn Erythroblastosis fetalis not involved is \u2013\nOptions:\nA. Anti C\nB. Anti D\nC. Anti E\nD. Anti Lewis\n","solution":"D"} +{"id":"641f160f-eef0-4b8c-9cb8-3241d2d63173","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nQuestion:\nIn a child with active liver failure, the most important prognosis factor for death is \u2013\nOptions:\nA. Increasing transaminases\nB. Increasing bilirubin\nC. Increasing prothrombin time\nD. Gram (\u2013)ve sepsis\n","solution":"D"} +{"id":"3a13e9bb-48ab-46c1-9d50-e1612840d922","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nQuestion:\nA patient went into shock immediately after normal delivery, likely cause:\nOptions:\nA. Amniotic fluid embolism\nB. PPH\nC. Uterine inversion\nD. Eclampsia\n","solution":"C"} +{"id":"9bad2095-2dd9-4485-946a-4ef51d16e8a4","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nQuestion:\nA patient presented with flat topped violaceous papules, thinning of nails and onycholysis. What is the microscopic finding observed in this patient?\nOptions:\nA. Prominent necrotic cell\nB. Suprabasal split\nC. Basal cell degeneration\nD. Acantholysis\n","solution":"C"} +{"id":"ddb3f2a6-295a-4d4b-8478-5c15049b62a8","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nQuestion:\nWhich of the following committees has recommended a 3-year bachelor degree in medical education for rural health service?\nOptions:\nA. High level expe group of universal health program for india\nB. Central bureau of health investigation\nC. Srivastava committee\nD. Sundar committee\n","solution":"A"} +{"id":"9a3940ff-8c7a-492c-86d4-259c47cef675","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nQuestion:\nWhich one of the following is a muscle splitting incision?\nOptions:\nA. Kocher's incision\nB. Rutherford-Morrison incision\nC. Pfannenstiel incision\nD. Lanz incision\n","solution":"D"} +{"id":"5ce754b8-b358-4270-9bd1-8828700a19b1","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nQuestion:\nWhich of the following blade angle is appropriate for scaling and root planing\nOptions:\nA. A\nB. B\nC. C\nD. D\n","solution":"B"} +{"id":"849b1909-c988-4d0b-8eaf-a716707cbe97","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nQuestion:\nWhich pa of brachial plexus do not give branches\nOptions:\nA. Root\nB. Division\nC. Cord\nD. Trunk\n","solution":"B"} +{"id":"07d58883-b752-4898-ab70-a1df892ef7bd","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nQuestion:\nIf the Rb gene phosphorylation is defective, which of the following will happen?\nOptions:\nA. Cell cycle will stop at GI phase\nB. Cell cycle will stop at G2 phase\nC. The cell cycle will progress and the cell will divide\nD. There will be no effect on cell cycle as for Rb gene phosphorylation is not needed\n","solution":"A"} +{"id":"66b9ec80-bfe0-485a-89ae-42e666aab572","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nQuestion:\nCumulative index is:\nOptions:\nA. Russet's periodontal index\nB. Ramjford's periodontal index\nC. PMA (Massler and Schlour)\nD. Gingival index (Loe and Silness)\n","solution":"C"} +{"id":"0e46082c-1abc-4330-a12d-6948554559a2","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nQuestion:\nThe cells which will proliferate from top to bottom of villi are:\nOptions:\nA. Chief cells\nB. Goblet cells\nC. Paneth cells\nD. Parietal cells\n","solution":"C"} +{"id":"360f90ec-189e-464a-a60d-ed9d9bda46ef","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nQuestion:\nWhat is the usual weight of rabbit used in ophthalmological experiments?\nOptions:\nA. 0.5-1 kg\nB. 1.5-2.5 kg\nC. 5-7 kg\nD. 10-12 kg\n","solution":"B"} +{"id":"ee55a9bc-62bb-4952-957a-1902ff5b4376","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nQuestion:\nIn plasmodium vivax malaria, relapse is caused by:September 2012\nOptions:\nA. Sporozoite\nB. Schizont\nC. Hypnozoite\nD. Gamteocyte\n","solution":"C"} +{"id":"06cab4ad-fb49-4daa-92db-5ee3529af02f","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nQuestion:\nRetraction of mandible is achieved by:\nOptions:\nA. Lateral pterygoid\nB. Temporalis\nC. Medial pterygoid\nD. Masseter\n","solution":"B"} +{"id":"783df438-362f-4ab5-a8b3-11ea2bfb2af7","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nQuestion:\nHow is modified shock index represented as?\nOptions:\nA. HR\/MAP\nB. MAP\/HR\nC. HR\/SBP\nD. HR\/DBP\n","solution":"A"} +{"id":"87f6392c-b727-4e7d-be12-189db181fd2b","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nQuestion:\nBase pairs in DNA\nOptions:\nA. 1.5 billion\nB. 46 billions\nC. 3.2 billion\nD. 100 billion\n","solution":"C"} +{"id":"1bf69f9a-987c-48fc-9356-d62d2148c3a6","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nQuestion:\nTracheal secretions should be suctioned for:\nOptions:\nA. 10-15 seconds\nB. 60 seconds\nC. 30 seconds\nD. 3 minutes\n","solution":"A"} +{"id":"0e7917ea-310b-4477-9897-f4901f728448","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nQuestion:\nWhich of the following lipoproteins does not move towards charged end in electrophoresis?\nOptions:\nA. VLDL\nB. LDL\nC. HDL\nD. Chylomicrons\n","solution":"D"} +{"id":"133b6b41-ac32-4d94-b0c8-9004aa2214f4","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nQuestion:\nName the structure marked with arrow\nOptions:\nA. Mitochondria\nB. Golgi bodies\nC. Secretory vesicles\nD. Rough Endoplasmic reticulum\n","solution":"C"} +{"id":"a570d3c3-865a-41b6-8e21-dccbf7feec4c","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nQuestion:\nA patient from,nqh-eastern states was diagnosed to have infection with P. falciparum malaria. What is the most appropriate drug for this patient?\nOptions:\nA. Aemether plus lumefantrine\nB. Sulfadoxine plus pyrimethamine\nC. Chloroquine\nD. Mefloquine\n","solution":"A"} +{"id":"57eb90ac-1025-4763-b6c0-ff5581ef2126","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nQuestion:\nThe most common bone tumor that occurs in children is:\nOptions:\nA. Osteosarcoma\nB. Ewing's sarcoma\nC. Metastatic carcinoma\nD. Multiple myeloma\n","solution":"A"} +{"id":"890982b8-3906-44be-aff1-437a7c6c373d","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nQuestion:\nIn a couple for treatment of infeility from the last four years, female paner is normal. Male paner has 0.8 ml semen volume per ejaculate on two repeated samples and absent fructose, with no sperms on examination under microscope. What is the next line of management?\nOptions:\nA. Per-rectal examination to check ejaculatory duct obstruction\nB. Give antioxidants\nC. Testicular biopsy\nD. Transrectal ultrasound to detect duct obstruction\n","solution":"D"} +{"id":"069b7516-54c4-4e5d-acf7-a7c92fdd2a01","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nQuestion:\nThe zygomatic bone does not articulate with:\nOptions:\nA. Frontal bone\nB. Maxillary bone\nC. Nasal bone\nD. Temporal bone\n","solution":"C"} +{"id":"21af7233-ae6a-423c-ae71-9148212a37c3","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nQuestion:\nCalcium ions triggers muscle contraction by binding to:\nOptions:\nA. Actin\nB. Myosin\nC. Troponin\nD. Tropomyosin\n","solution":"C"} +{"id":"1e94a9ca-55e4-4e9a-bf7b-cb2dc4ba2ab5","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nQuestion:\nSD plasma destroys lipid enveloped virus. On SD plasma transfusion, which of the following infection is the likely possibility?\nOptions:\nA. HIV\nB. HAV\nC. HBV\nD. HCV\n","solution":"B"} +{"id":"d1d16eda-c34e-4492-bee4-1b8c4246daf3","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nQuestion:\nDue to which of the following enzyme deficiency, vitamin C cannot be synthesised in humans?\nOptions:\nA. L-Glucuronic acid oxidase\nB. L-Gulonic acid reductase\nC. L-Gulonolactone oxidase\nD. L-Gulonolactone reductase\n","solution":"C"} +{"id":"732401b0-673b-4842-baed-ddd00626c561","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nQuestion:\nIn a patient of heart disease antibiotic prophylaxis for dental extraction is:\nOptions:\nA. Amoxicillin.\nB. Imipenem.\nC. Gentamicin.\nD. Erythromycin.\n","solution":"A"} +{"id":"87d8663f-e0cd-4766-87b7-5312dfc4cd62","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nQuestion:\nWhich of the following type of failure may occur, if the coefficient of thermal expansion of ceramic is much lower than that of metal?\nOptions:\nA. Cohesive failure of ceramic\nB. Adhesive failure of metal ceramic bond\nC. Cohesive failure of metal\nD. Cohesive failure of metal ceramic bond\n","solution":"B"} +{"id":"4a36bb7a-a19f-4aba-82b3-6cd35fc3cbc0","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nQuestion:\nDuring periradicular surgery bone is removed with?\nOptions:\nA. Round burr\nB. Double inverted cone burr\nC. Fissure burr\nD. Taper Burr\n","solution":"A"} \ No newline at end of file diff --git a/sdk/python/jobs/grpo/datasets/med_mcqa/train.jsonl b/sdk/python/jobs/grpo/datasets/med_mcqa/train.jsonl new file mode 100644 index 0000000000..3b1b25d1ca --- /dev/null +++ b/sdk/python/jobs/grpo/datasets/med_mcqa/train.jsonl @@ -0,0 +1,50 @@ +{"id":"7b33754c-beaa-43e5-81f5-f4c3f8bf2245","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nWhich of the following drugs is antipseudomonal penicillin?\nOptions:\nA. Cephalexin\nB. Dicloxacillin\nC. Piperacillin\nD. Cloxacillin\n","solution":"C"} +{"id":"149dd06e-52d9-454e-9f8f-d0cf94af8501","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nLady has B\/L hearing loss since 4 years which worsened during pregnancy.Type of impedance audiometry graph will be:\nOptions:\nA. Ad\nB. As\nC. B\nD. C\n","solution":"B"} +{"id":"e36297ad-7aa2-4dd5-be15-c101e52c5843","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nThe person whose work on the radical cure of hernia immortalised his name was -\nOptions:\nA. William Halsted\nB. Eduardo Bassini\nC. Mc Vay\nD. Koontz\n","solution":"B"} +{"id":"de0080fb-b898-408c-8c56-3417388ab2b7","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nNGO eye hospital make which stage of NPCB\nOptions:\nA. Primary\nB. Secondary\nC. Teiary\nD. Not included\n","solution":"B"} +{"id":"fd81c067-c433-4856-ac21-f2e4a7dc47cc","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nHemophilia B is due to deficiency of-\nOptions:\nA. Factor VIII\nB. Factor VII\nC. Factor IX\nD. factor X\n","solution":"C"} +{"id":"d59818d9-ba4e-46a0-a4c4-e15dd989960a","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nIn a slow sand filter, the element responsible for yielding bacteria\u2013free water is the \u2013\nOptions:\nA. Valve\nB. Vital layer\nC. Supernatant water\nD. Under\u2013drainage system\n","solution":"B"} +{"id":"a61d11c3-2ad9-4c6c-a4a4-60c7e3422c53","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nProgesterone of choice in Emergency contraception is-\nOptions:\nA. Norgesterone\nB. Micronized progesterone\nC. Levonorgestrel\nD. DMPA\n","solution":"C"} +{"id":"ef54d3b3-76b5-4c0e-af08-c3895f40a30a","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nA Pap smear from a 30-year-old woman demonstrates protozoal parasites. These organisms are likely to be:\nOptions:\nA. Cryptosporidium parvum\nB. Entamoeba histolytica\nC. Giardia lamblia\nD. Trichomonas vaginalis\n","solution":"D"} +{"id":"2608653f-1771-4eb1-b91f-e91518191857","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nCSF Rhinorrhea occurs d\/t of:\nOptions:\nA. Roof of orbit\nB. Cribiform plate of ethmoidal bone\nC. Frontal sinus\nD. Sphenoid bone\n","solution":"B"} +{"id":"cee9fe7c-1934-4a8d-a6cc-65c99d02f924","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nThe prevalence of diabetes mellitus in a population was found to be 10 per cent. What is the probability that three people selected at random from the population have the disease?\nOptions:\nA. 0.003\nB. 0.001\nC. 0.03\nD. 0.01\n","solution":"B"} +{"id":"fc3110ba-dbf1-4a4b-beaa-9abad5cb3a03","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nGriesofulvin is not useful in one of the following\nOptions:\nA. Tinea capitis\nB. Tinea Cruris\nC. Tinea versicolor\nD. Tinea pedis\n","solution":"C"} +{"id":"60f2fd8e-eb69-45e5-8e13-4c1b265eb69d","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nTime sector scanning of neonates is preferred for the following reason ?\nOptions:\nA. Open fontanelles\nB. Inexpensive\nC. Children more co-operative\nD. Better resolution\n","solution":"A"} +{"id":"b4b2ad6e-51c4-430c-acf6-5939e013346c","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nFogay's catheter is used for -\nOptions:\nA. Drainage of urinary bladder\nB. Parenteral hyperalimentation\nC. Removal of embolus from blood vessels\nD. Ureteric catheterisation\n","solution":"C"} +{"id":"0d865bcf-b511-4d5f-ade7-09e66c9ebf8a","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nDenosumab, a monoclonal antibody against RANK ligand is used for the treatment of :\nOptions:\nA. Rheumatoid arthritis\nB. Osteoporosis\nC. Osteoarthritis\nD. Systemic lupus erythematosis\n","solution":"B"} +{"id":"38010b9c-10c9-40b3-8635-9bae6ad8d61c","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nPuff of smoke appearance on contrast CT angiography is seen -\nOptions:\nA. Moya Moya Disease\nB. Acrodermatitis Enterohepatica\nC. Neuromyelitis Optica\nD. Kasbach Meritt Syndrome\n","solution":"A"} +{"id":"74d6ef56-834f-447b-83f6-f14f0cea504c","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nWhat is the dose of Amoxicillin that has to be administered in Triple-drug regimen for H. pylori?\nOptions:\nA. 500mg BD\nB. 500mg TDS\nC. 1g BD\nD. 1g OD\n","solution":"C"} +{"id":"ac695054-a41f-45c4-bc02-091a91f3d04b","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nPterygium of the nail is seen in -\nOptions:\nA. Lichen planus\nB. Psoriasis\nC. Tinea unguium\nD. alopecia areata\n","solution":"A"} +{"id":"603fb440-902b-4690-be58-83975bd9b93d","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nFluoxetine is a -\nOptions:\nA. Noradrenaline uptake inhibitor\nB. Serotonin uptake enhancer\nC. Serotonin uptake inhibitor\nD. MAO\n","solution":"C"} +{"id":"2bd0d0c3-9956-47e2-961b-ef7d6a4f6ee8","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nMagendie foramen or the median apeure drains CSF from which of the following structures to the cisterna magna?\nOptions:\nA. Lateral ventricle\nB. 3rd ventricle\nC. 4th ventricle\nD. Interpeduncular fossa\n","solution":"C"} +{"id":"0b8de2cc-5d40-4424-ab6b-850cd85e33b1","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nIn follow up of BPH, most important indication of surgery is:\nOptions:\nA. Prostate size >75gm\nB. Single episode of UTI requiring 3 day antibiotics\nC. Cannot use medication due to hypertension\nD. Bilateral hydronephrosis\n","solution":"D"} +{"id":"2f40b5c2-a5ac-4565-994e-a39a418d4ff3","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nTreatment of choice for perforation in pars flaccida of the tympanic membrane with cholesteatoma is:\nOptions:\nA. Myringoplasty\nB. Modified radical mastoidectomy\nC. Antibiotics\nD. Radical mastoidectomy\n","solution":"B"} +{"id":"4149f5a8-60ab-4ca9-a4df-225de5d99059","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nCataract brunescen results due to deposition of-\nOptions:\nA. Urochrome\nB. Copper\nC. Iron\nD. Silver\n","solution":"A"} +{"id":"fb9ab0b5-84d3-4669-82cf-46dfc73b44ff","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nThe elimination of alcohol follows-\nOptions:\nA. Zero order kinetics\nB. 1st order kinetics\nC. 2nd order kinetics\nD. 3rd order kinetics\n","solution":"A"} +{"id":"c4661695-b367-4cb4-b61b-5f35a4be103a","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nNSAID induced ulcer are treated by:\nOptions:\nA. Antacids\nB. H2 blockers\nC. Misoprostol\nD. PPI (proton pump inhibitors)\n","solution":"D"} +{"id":"2d98b00f-93aa-4716-bc4f-b3a89f26a15f","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nDonovanosis\/granuloma venerum is caused by\nOptions:\nA. Calymatobacter granulomatis\nB. T. peenue\nC. Chlamydia Trachomatis\nD. H. ducreyi\n","solution":"A"} +{"id":"34b29852-eff9-4218-8069-3bcaf040944f","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nNerve of medial compament of thigh:-\nOptions:\nA. Femoral\nB. Obturator\nC. Tibial\nD. Common peroneal\n","solution":"B"} +{"id":"d1ab8d38-ea76-4159-ac5a-2c23bef4f72a","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nChildhood cholelithiasis is seen in-\nOptions:\nA. Hurler Syndrome\nB. Mucopolysaccharidosis\nC. Neimann Pick's disease\nD. Autoimmune hepatitis\n","solution":"C"} +{"id":"94b1cc48-a778-4823-ac9f-6a5d0589056e","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nAccording to international standards, still birth is defined as weight of fetus over?\nOptions:\nA. 500 gm\nB. 850 gm\nC. 1000 gm\nD. 2000 gm\n","solution":"C"} +{"id":"9cfc48cf-1c35-4678-8003-f50d3aba9cf5","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nWhich of the following is NOT a mechanism by which diabetes influences periodontium?\nOptions:\nA. Hyperglycemia\nB. Altered collagen metabolism\nC. Altered neutrophil chemotaxis\nD. Increased vascularity\n","solution":"D"} +{"id":"d4c9e713-867a-4a6e-89a6-c557cbd17d43","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nWhich breathing pattern is described in below diagram?\nOptions:\nA. Kussmaul's respiration\nB. Biot's respiration\nC. Cheyne-Stokes respiration\nD. Hyperventilation respiration\n","solution":"C"} +{"id":"08f6ab66-d980-466e-99be-c654a5393710","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nLactic acidosis is a side effect of:\nOptions:\nA. Phenformin\nB. Metformin\nC. Chlorpropramide\nD. Glibenclamide\n","solution":"A"} +{"id":"c7bfad31-138a-4851-b648-3deb69042272","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nMost vascular part of bone :(Repeat Kerala 2007)\nOptions:\nA. Epiphysis\nB. Metaphysis\nC. Diaphysis\nD. Periosteum\n","solution":"B"} +{"id":"40be7755-d423-4427-b456-bcd9ab3af37e","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nUlceration of Peyer's patches occur in _________ infection\nOptions:\nA. Amoebiasis\nB. Crohn's\nC. Salmonella\nD. Clostridium difficile\n","solution":"C"} +{"id":"3870af55-eb53-446b-a6c6-7a1944abc66a","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nIn normal pregnancy b-Hcg doubles in\nOptions:\nA. 24hours\nB. 48hours\nC. 72hours\nD. 90hours\n","solution":"B"} +{"id":"712b2010-d39b-43b3-9606-969409703b65","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nA 28-year-old man was found to have elevated cholesterol levels of 325 mg\/dL on a routine checkup. His father died of a hea attack at the age of 42, and also had markedly elevated cholesterol levels throughout his life. The man's physician placed him on lovastatin, and his cholesterol levels dropped to 170 mg\/dL. The nature of the elevated cholesterol in this patient is most likely due to a mutation in which one of the following proteins?\nOptions:\nA. Microsomal triglyceride transfer protein\nB. LCAT\nC. Lipoprotein lipase\nD. LDL receptor\n","solution":"D"} +{"id":"2de776e2-b80b-436f-8d38-1e94de429857","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nTreatment of Ca Cervix IIIB include -\nOptions:\nA. Wertheims hysterectomy\nB. Schauta ' s hysterectomy\nC. Chemotherapy\nD. Intracavity brachytherapy followed by external beam radiotherapy\n","solution":"D"} +{"id":"1108a2aa-4ff4-4274-b181-0633f58a8484","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nChediak-Higashi syndrome\nOptions:\nA. Disorders of specific immunity\nB. Disorders of complement\nC. Disorders of phagocytosis\nD. Secondary immunodeficiencies\n","solution":"C"} +{"id":"2fcd6367-0133-468f-b0ab-b7e2d1b74920","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nMetabolism of a drug primarily results in\nOptions:\nA. Activation of the active drug\nB. Conversion of prodruv to active metabolite\nC. Conversion of lipid soluble drugs to water soluble metabolites\nD. Conversion of water soluble drugs to lipid solible metabolites\n","solution":"C"} +{"id":"2b271d27-1d15-4d02-ab4b-c2e61ef24e68","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nWhich of the following is a Live vaccine -\nOptions:\nA. Salk polio vaccine\nB. HDCV\nC. Hepatitis B vaccine\nD. 17-D Vaccine\n","solution":"D"} +{"id":"e98c3402-2432-4516-bd86-1925e5484754","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nCardiotoxicity is the side effect of\nOptions:\nA. Bleomycin\nB. Topotecan\nC. Rubidomycin\nD. Procarbazine\n","solution":"C"} +{"id":"a0bc42bd-1860-4a6a-8fda-4772dc1a338f","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nType of bond seen between the phosphate group and 5' carbon of ribose sugar within a nucleotide is:\nOptions:\nA. Phosphodiester bond\nB. Ester bond\nC. Beta N- glycosidic bond\nD. Acid anhydride bond\n","solution":"B"} +{"id":"f29d8efc-7a3c-4583-9b7b-5a1592288cf4","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nAnti GBM antibodies are seen in?\nOptions:\nA. Wegener's granulomatosis\nB. Goodpasture syndrome\nC. PAN\nD. SLE\n","solution":"B"} +{"id":"a23c1a8f-55f8-479c-8a31-bb8ddd2418ec","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nPappenheimer bodies are\nOptions:\nA. Ribosomal inclusions\nB. Iron deposits\nC. Denatured Hemoglobin\nD. DNA remnants\n","solution":"B"} +{"id":"2d468a55-b092-44af-b756-f253d094e3a7","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nSclera is thinnest at -\nOptions:\nA. Limbus\nB. Equator\nC. Anterior to attachment of superior rectus\nD. Posterior to attachment of superior rectus\n","solution":"D"} +{"id":"cd06d6dc-1ce2-4fce-9396-a9fa147b0905","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nAn abnormal attitude is illustrated by:\nOptions:\nA. Breech presentation\nB. Face presentation\nC. Transverse position\nD. Occiput posterior\n","solution":"B"} +{"id":"5c4a9189-70e0-468b-9d04-32960fd888de","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nWhich does not cause hypoglycemia?\nOptions:\nA. Insulin\nB. Glimipiride\nC. Nateglinide\nD. Acarbose\n","solution":"D"} +{"id":"738002b3-b974-47d1-a9e5-71d85f25b4aa","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nWhich feature would most likely be found in a patient with lepromatous leprosy?\nOptions:\nA. No bacteremia\nB. Macrophages with a foamy appearance in the dermis\nC. A positive lepromin reaction\nD. A high cell-mediated response against Mycobacterium leprae\n","solution":"B"} +{"id":"dad7d21c-b525-4a27-a3e9-a2b7de55bcbe","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nParoxysmal Nocturnal Hemoglobinuria (PN ) is screened by -a) Acid ham testb) Sucrose lysis testc) Serum hapten levelsd) Low serum complement levels\nOptions:\nA. ab\nB. a\nC. ad\nD. bc\n","solution":"A"} +{"id":"3ac0de71-db62-4efd-9f66-e1d9d687a07c","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nConcentration of sodium in Ringer's lactate is:\nOptions:\nA. 154 mEq\/L\nB. 120 mEq\/L\nC. 130 mEq\/L\nD. 144 mEq\/L\n","solution":"C"} +{"id":"1dcc788e-1253-4d75-ae6c-b3e04ab06366","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nEruption cyst\nOptions:\nA. Transforms into dentigerous cyst\nB. Regresses after eruption of the tooth\nC. Is found in the place of the missing tooth\nD. Is a type of dentigerous cyst\n","solution":"B"} \ No newline at end of file diff --git a/sdk/python/jobs/grpo/datasets/med_mcqa/validation.jsonl b/sdk/python/jobs/grpo/datasets/med_mcqa/validation.jsonl new file mode 100644 index 0000000000..d453025bbc --- /dev/null +++ b/sdk/python/jobs/grpo/datasets/med_mcqa/validation.jsonl @@ -0,0 +1,50 @@ +{"id":"276358eb-5fb7-4623-a3fc-758bf40a113b","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nMurphy&;s sign is seen in?\nOptions:\nA. Acute appendicitis\nB. Acute cholecystitis\nC. Acute pancreatitis\nD. Ectopic pregnancy\n","solution":"B"} +{"id":"8ca6a639-0c70-48be-8c71-4b88f6a0872a","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nMost common site of aoic aneurysm rupture is\nOptions:\nA. Descending thoracic aoa\nB. Arch of aoa\nC. Aoic Root\nD. Infrarenal aoa\n","solution":"D"} +{"id":"e9aa8c5c-9a1c-408c-b375-eedba833ebe8","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nVeical transmission is by -\nOptions:\nA. Mosquitoes\nB. Direct contact\nC. Droplet\nD. Placenta\n","solution":"D"} +{"id":"3cbebb6e-adcd-48a4-b372-faf2bf772e58","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nNihilistic ideas are seen in -\nOptions:\nA. Depression\nB. Schizophrenia\nC. Mania\nD. OCN\n","solution":"A"} +{"id":"ece907e6-7b5a-468f-928d-82125d2da402","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nMechanism of action of teduglutide in sho bowel syndrome:-\nOptions:\nA. GLP-2 analogue\nB. HT la inhibitor\nC. GLP-1 analogs\nD. C- peptic analogs\n","solution":"A"} +{"id":"d2131ea2-c938-4e86-859c-d0fc8c34b3f3","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nEthics and planning approvals are taken in\nOptions:\nA. Public health practice\nB. Group practice\nC. Individual practice\nD. Solo practice\n","solution":"A"} +{"id":"9eeaaade-d820-4d2c-8117-ec4e3c93e23f","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nNot seen in dengue: March 2010\nOptions:\nA. Hemorrhagic tendencies\nB. High grade fever\nC. Circulatory problem\nD. Hepato-splenomegaly\n","solution":"D"} +{"id":"855514ec-18ce-4e66-872e-f623581ca15e","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nWhich of the following is used in tracking transfer of MS from mother to infant, to detect risk of early childhood caries?\nOptions:\nA. Albans test\nB. GC-Saliva check SM\nC. Dentocult lactobacilli test\nD. Fosdick calcium dissolution test\n","solution":"B"} +{"id":"7d8f517b-4c38-478b-bc53-812f3b2ad6c7","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nWhich of the following is not a usual feature of right middle cerebral aery territory infarct :\nOptions:\nA. Aphasia.\nB. Hemiparesis\nC. Facial weakness\nD. Dysahria\n","solution":"A"} +{"id":"6a80c3f2-7f72-4359-be8e-f591efc86e96","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nDouble bubble sign is seen in:-\nOptions:\nA. Duodenal Atresia\nB. Jejunal atresia\nC. Ileal atresia\nD. Hypertrophic pyloric stenosis\n","solution":"A"} +{"id":"74507e7a-af21-4c93-a2c2-f46d95db4af5","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nLabour is said to be prolonged when duration of latent stage of labour crosses:\nOptions:\nA. 12 hours\nB. 18 hours\nC. 20 hours\nD. 36 hours\n","solution":"C"} +{"id":"aed1ac8b-85c6-4402-9341-c3239433dc03","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nFerruginous bodies are commonly seen in:\nOptions:\nA. Silicosis\nB. Asbestosis\nC. Bagassosis\nD. Byssinosis\n","solution":"B"} +{"id":"d5bc4f01-f637-4480-9f03-606d99cbfd24","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nWhich of the following segments of the circulatory system has the highest velocity of blood flow?\nOptions:\nA. Aoa\nB. Aeries\nC. Capillaries\nD. Venules\n","solution":"A"} +{"id":"20e0b0cd-3157-494d-a7ce-570a904ee1e6","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nBranched-chain ketoacid decarboxylation is defective in -\nOptions:\nA. Maple syrup urine disease\nB. Hartnup disease\nC. Alkaptonuria\nD. GM-1 gangliosidosis\n","solution":"A"} +{"id":"65462519-0c79-4215-8caa-e33afe2eabc5","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nInability to adduct the thumb is due to the injury of: March 2009\nOptions:\nA. Median nerve\nB. Ulnar nerve\nC. Radial nerve\nD. Musculocutaneous nerve\n","solution":"B"} +{"id":"7b5a658e-37fe-4926-8d5d-a836770ecb5a","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nIn \"Three glass test\" shreds are present in 1st glass only. The likely diagnosis would be:\nOptions:\nA. Cystitis\nB. Renal pathology\nC. Anterior urethritis\nD. Prostatitis\n","solution":"C"} +{"id":"478691be-704a-4673-81bc-78b6805f0754","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nThe first and the most important measure in the\nmanagement of severely injured patient is to:\nOptions:\nA. To maintain airway\nB. Start I.V. fluids\nC. Arrest bleeding\nD. Splinting fractures\n","solution":"A"} +{"id":"4664e712-6676-49f2-84b8-aed2641137e1","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nHyoid bone is closely associated to: September 2012\nOptions:\nA. Bronchogenic cyst\nB. Cystic hygroma\nC. Thyroglossal cyst\nD. Ranula\n","solution":"C"} +{"id":"08eeb9ce-2589-4597-a13d-c8859a3b4f4f","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nCarbamazepine in elderly causes:\nOptions:\nA. Hypernatremia\nB. Hyponatremia\nC. Hyperkalemia\nD. Hypokalemia\n","solution":"B"} +{"id":"6e55ddf0-e310-4ecc-9169-2bb966c612db","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nA 55-year-old man suffers from an acute myocardial infarction after occlusion of the left anterior descending coronary artery. The patient undergoes coronary bypass surgery 3 days later. Which of the following is the most frequent cause of saphenous vein graft failure several years following coronary bypass surgery?\nOptions:\nA. Acute inflammation\nB. Atherosclerosis\nC. Graft-versus-host disease\nD. Metastatic calcification\n","solution":"B"} +{"id":"2c8d4986-fe2c-405f-badc-a9008d00fcd7","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nPrognosis of melanoma depends ona) Stageb) Depth of melanoma of biopsyc) Duration of growthd) Site\nOptions:\nA. acd\nB. bcd\nC. abd\nD. ab\n","solution":"C"} +{"id":"15648d73-525d-4943-9152-d8132b457e88","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nAgenesis of cerebellar vermis,large posterior fossa with megacisterna magna is a feature of\nOptions:\nA. Von Hippel-Lindau syndrome\nB. Sturge weber syndrome\nC. Chiari malformation\nD. Dandy Walker malformation\n","solution":"D"} +{"id":"6947c893-f735-460e-956a-c19d8e6b0326","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nNot a contraindication for combined spinal and epidural anaesthesia: (CSE) (D. REPEAT 2009)\nOptions:\nA. Platelet count <50,000\nB. Patient on clopidogrel\nC. Local infection\nD. Patient on antihypertensive medication\n","solution":"D"} +{"id":"3465a27e-5fac-476c-a02c-c7ad99aa958c","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nWhich of the following is the drug used for testing errors of refraction in a 7-year-old girl?\nOptions:\nA. Atropine ointment\nB. Tropicamide\nC. Phenylephrine\nD. Homatropine\n","solution":"A"} +{"id":"298fbb06-02e8-4fe0-abec-ddf06c02c92f","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nDeclaration of oslo deals with -\nOptions:\nA. Organ donation\nB. Right to death\nC. Therapeutic abortion\nD. Human experiments\n","solution":"C"} +{"id":"84625869-9461-494c-a070-49d4bdb61727","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nMore than 5% carboxy hemoglobin is indicative of\nOptions:\nA. Ante mortem burn\nB. Drowning\nC. HCN poisoning\nD. Suffocation\n","solution":"A"} +{"id":"359ac4ce-2d3e-414a-9ce4-5d19e8787e78","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nLeast blood gas paition coeficient anesthetic agent:\nOptions:\nA. Desflurane\nB. Nitrous oxide\nC. Halothane\nD. Ether\n","solution":"A"} +{"id":"ef3dd9e9-66a4-479f-aeed-f4a525f9c1f8","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nWof are anti diabetic agents that acts by increasing insulin sensitivity, and so should be euglycemics\nOptions:\nA. Phenformin\nB. Pioglitazone\nC. Sitagliptin\nD. Liraglutide\n","solution":"B"} +{"id":"ca365f23-4929-4349-92a0-bbc496b6bbcb","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nWahin-Finkeldey giant cells are seen in -\nOptions:\nA. Measles\nB. Rubella\nC. Influenza\nD. Rickettsial pox\n","solution":"A"} +{"id":"a60536c7-0b9f-42a8-b633-f52d1aefd69c","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nA group of expes discussing and interacting about a paicular topic in presence of audience is:\nOptions:\nA. Symposium\nB. Workshop\nC. Seminar\nD. Panel discussion\n","solution":"D"} +{"id":"dea02d79-a806-47c0-895a-826af2e715eb","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nHighest incidence of anemia in the tropics is due to?\nOptions:\nA. Hookworm\nB. Thread Worm\nC. Ascaris\nD. Guinea worm\n","solution":"A"} +{"id":"9a112f29-daf4-4d28-9fce-3a03d6849a0d","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nCD marker specific for myeloid series -\nOptions:\nA. CD34\nB. CD45\nC. CD99\nD. CD117\n","solution":"D"} +{"id":"30755d1b-2c82-4f7c-8b36-2597318dadf6","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nOtospongiosis causes\nOptions:\nA. U\/L conductive deafness only\nB. B\/L conductive deafness only\nC. U\/L sensorineural deafness only\nD. B\/L mixed conductive and sensorineural deafness\n","solution":"D"} +{"id":"1998711d-5c7a-4879-a9c4-920f3ef4f413","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nSclerotic bodies is seen:\nOptions:\nA. Sporotrichosis\nB. Blastomycosis\nC. Chromoblastomycosis\nD. Cocci diodes\n","solution":"C"} +{"id":"3bce4935-9b04-4474-8b6d-aa8efdbe5ba2","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nThe largest gene expressed in cardiac and smooth muscle is\nOptions:\nA. Dystrophin gene\nB. Semiphorin gene\nC. Tumor suppressor gene\nD. Huntington gene\n","solution":"A"} +{"id":"a93566ee-c7e4-45ce-87e3-6e658dcbcbed","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nGreen stick fractures are most common with:\nOptions:\nA. Older people\nB. Adult\nC. Children\nD. Soldiers\n","solution":"C"} +{"id":"5da6d855-38cf-48a4-9b63-1318e19e2511","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nWhich of the following is the most common neurocutaneous syndrome?\nOptions:\nA. Neurofibromatosis\nB. Tuberous sclerosis\nC. Sturge Weber syndrome\nD. Von Hippel Lindau disease\n","solution":"A"} +{"id":"88f0f9d8-df54-4527-bdda-4bb7c701392b","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nThe approximate protein content of breast milk is -\nOptions:\nA. 0.9-1.1 g\/dL\nB. 2.0-3.0 g\/dL\nC. 3.5-4.0 g\/dL\nD. 5.0-5.5 g\/dL\n","solution":"A"} +{"id":"649fd78a-3a0f-47f3-9bb8-97ea8f84a013","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nTissue adhesive glue contains\nOptions:\nA. Cyanoacrylate\nB. Ethanolamine\nC. Methacrylate\nD. Polychloroprene\n","solution":"A"} +{"id":"9a96a2e7-cf8d-4bd0-b0a2-7a029e49eea6","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nCation used in PCR is\nOptions:\nA. Calcium\nB. Lithium\nC. Magnesium\nD. Sodium\n","solution":"C"} +{"id":"ebfad76a-1411-4a67-90ab-7c50c7e562b5","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nTypical appearance of diabetic cataract is\nOptions:\nA. Blue dot cataract\nB. Post capsular cataract\nC. Snow flake opacities\nD. Sunflower cataract\n","solution":"C"} +{"id":"9f2ea0b5-ef0a-4047-9a4a-00ed05182951","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nThe culture media for fungus is\nOptions:\nA. Tellurite medium\nB. NNN medium\nC. Chocolate agar medium\nD. Sabourauds medium\n","solution":"D"} +{"id":"ae0da800-f3c5-4602-960d-72e9d94e6233","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nThe type of hemoglobin that has least affinity for 2,3-Diphosphoglycerate (2,3-DPG) or (2,3-BPG) is:\nOptions:\nA. HbA\nB. HbF\nC. HB\nD. HgA2\n","solution":"B"} +{"id":"374c3dbe-b90e-447f-9e32-9d8a70f4ffa2","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nA destitute woman is admitted to the hospital with altered sensorium and dehydration; urine analysis shows mild proteinuria and no sugar; what other test would be desirable\nOptions:\nA. Fouchet\nB. Rothera\nC. Hays\nD. Benedicts\n","solution":"B"} +{"id":"c0784d59-ab4a-4890-9949-3914ad0550f2","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nIn essential hypertention changes seen in the heart are -\nOptions:\nA. Cardiac cell hyperplasia\nB. Cardiac cell hypertrophy\nC. Increase in the mitochondrial number\nD. Increase in size of mitochondria\n","solution":"B"} +{"id":"8c2a2ab5-abe4-4007-aba9-b7b2d9b4b2a4","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nVesicovaginal fistula by obstructed labour manifests..................of delivery :\nOptions:\nA. Within 24 hours\nB. Within 72 hours\nC. Within 1st week\nD. After 1st week\n","solution":"D"} +{"id":"6b9003c6-a10a-4e0b-a1c0-a436acfcea23","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nMost common complication of celiac plexus block:\nOptions:\nA. Pneumothorax\nB. Postural hypotension\nC. Retroperitoneal hemorrhage\nD. Intra-aerial injection\n","solution":"B"} +{"id":"c17667e9-2ddd-428a-8b20-90d2478b3bc1","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nWidening of intercondylar notch of femur is seen in\nOptions:\nA. Osteoarthritis\nB. Rheumatoid arthritis\nC. Hemophiliac Artheopathy\nD. Charcot's joint\n","solution":"C"} +{"id":"f36868f6-507f-4028-a59d-f4a5ee47ae49","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nType of inheritance in Tuberous sclerosis ?\nOptions:\nA. Autosomal dominant\nB. Autosomal recessive\nC. X-linked dominant\nD. X-linked recessive\n","solution":"A"} +{"id":"f972c22b-3711-43d0-ba9b-50dee3c104a6","problem":"Instructions:\nThink step-by-step and answer the following multiple-choice question. The reasoning process and answer should be enclosed within <\/think> and <\/answer> tags, respectively, in the answer i.e., reasoning process here <\/think> detailed answer with logical, concise explanation <\/answer>.The final answer should be on a new line starting with the phrase 'Final Answer: '. It should be one of 'A', 'B', 'C', 'D'. No other outputs are allowed. Now, try to solve the following question through the above guidelines:\n\nCatalase positive coagulase negative beta hemolytic bacteria -\nOptions:\nA. Strep pyogens\nB. Staph aureus\nC. Coagulase negative staph\nD. Enterococci\n","solution":"C"} diff --git a/sdk/python/jobs/grpo/environment/Dockerfile b/sdk/python/jobs/grpo/environment/Dockerfile new file mode 100644 index 0000000000..5f995b9bc8 --- /dev/null +++ b/sdk/python/jobs/grpo/environment/Dockerfile @@ -0,0 +1,19 @@ +#PTCA image +FROM mcr.microsoft.com/aifx/acpt/stable-ubuntu2204-cu124-py310-torch251:biweekly.202505.1 + +USER root + +RUN apt-get update && apt-get -y upgrade +RUN pip install --upgrade pip + +COPY requirements.txt . + +RUN pip install -r requirements.txt --no-cache-dir + +RUN pip install azureml-acft-common-components==0.0.75 +RUN pip install azureml-sdk==1.60.0 +RUN pip install numpy==2.2.5 +RUN pip install azureml-evaluate-mlflow==0.0.75 +# clean conda and pip caches +RUN rm -rf ~/.cache/pip + \ No newline at end of file diff --git a/sdk/python/jobs/grpo/environment/requirements.txt b/sdk/python/jobs/grpo/environment/requirements.txt new file mode 100644 index 0000000000..7dc6251ef2 --- /dev/null +++ b/sdk/python/jobs/grpo/environment/requirements.txt @@ -0,0 +1,159 @@ +accelerate==1.6.0 +aiohappyeyeballs==2.6.1 +aiohttp==3.11.18 +aiosignal==1.3.2 +airportsdata==20250224 +annotated-types==0.7.0 +anyio==4.9.0 +astor==0.8.1 +attrs==25.3.0 +blake3==1.0.4 +cachetools==5.5.2 +certifi==2025.4.26 +charset-normalizer==3.4.2 +click==8.1.8 +cloudpickle==3.1.1 +compressed-tensors==0.9.3 +cupy-cuda12x==13.4.1 +datasets==3.6.0 +deepspeed==0.16.7 +deprecated==1.2.18 +depyf==0.18.0 +dill==0.3.8 +diskcache==5.6.3 +distro==1.9.0 +dnspython==2.7.0 +einops==0.8.1 +email-validator==2.2.0 +fastapi==0.115.12 +fastapi-cli==0.0.7 +fastrlock==0.8.3 +filelock==3.18.0 +flash-attn==2.7.4.post1 +frozenlist==1.6.0 +fsspec==2025.3.0 +gguf==0.16.3 +googleapis-common-protos==1.70.0 +grpcio==1.71.0 +h11==0.16.0 +hf-xet==1.1.0 +hjson==3.1.0 +httpcore==1.0.9 +httptools==0.6.4 +httpx==0.28.1 +huggingface-hub==0.31.1 +idna==3.10 +importlib-metadata==8.0.0 +interegular==0.3.3 +jinja2==3.1.6 +jiter==0.9.0 +jsonschema==4.23.0 +jsonschema-specifications==2025.4.1 +lark==1.2.2 +llguidance==0.7.19 +llvmlite==0.44.0 +lm-format-enforcer==0.10.11 +markdown-it-py==3.0.0 +markupsafe==3.0.2 +mdurl==0.1.2 +mistral-common==1.5.4 +mpmath==1.3.0 +msgpack==1.1.0 +msgspec==0.19.0 +multidict==6.4.3 +multiprocess==0.70.16 +nest-asyncio==1.6.0 +networkx==3.4.2 +ninja==1.11.1.4 +numba==0.61.2 +numpy==2.2.5 +nvidia-cublas-cu12==12.4.5.8 +nvidia-cuda-cupti-cu12==12.4.127 +nvidia-cuda-nvrtc-cu12==12.4.127 +nvidia-cuda-runtime-cu12==12.4.127 +nvidia-cudnn-cu12==9.1.0.70 +nvidia-cufft-cu12==11.2.1.3 +nvidia-cufile-cu12==1.11.1.6 +nvidia-curand-cu12==10.3.5.147 +nvidia-cusolver-cu12==11.6.1.9 +nvidia-cusparse-cu12==12.3.1.170 +nvidia-cusparselt-cu12==0.6.2 +nvidia-nccl-cu12==2.21.5 +nvidia-nvjitlink-cu12==12.4.127 +nvidia-nvtx-cu12==12.4.127 +openai==1.78.0 +opencv-python-headless==4.11.0.86 +opentelemetry-api==1.26.0 +opentelemetry-exporter-otlp==1.26.0 +opentelemetry-exporter-otlp-proto-common==1.26.0 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\n", + "
\n", + "

The reasoning model training process typically includes three key components:

\n", + "
    \n", + "
  • Sampler – Generates multiple candidate responses from the model
    • \n", + "
  • Reward Function – Evaluates and scores each response based on criteria like accuracy or structure
    • \n", + "
  • Trainer – Updates the model to reinforce high-quality outputs
    • \n", + "
\n", + "

\n", + " In this example we use the GRPO Trainer for training Qwen2.5-7B-Instruct model into a reasoning model. We use the GRPO implementation from TRL library.\n", + "

\n", + "
\n", + "

\n", + " GRPO (Group Relative Policy Optimization) is a reinforcement learning technique that:\n", + "

\n", + "
    \n", + "
  • Compares multiple answers within a group
    • \n", + "
  • Rewards the best-performing outputs
    • \n", + "
  • Penalizes poor ones
    • \n", + "
  • Applies careful updates to avoid sudden changes
    • \n", + "
\n", + "
\n", + "
\n", + " \"Training\n", + "
\n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "id": "25545006-ab16-4641-87d7-96128ad6da6a", + "metadata": { + "nteract": { + "transient": { + "deleting": false + } + } + }, + "source": [ + "## Why does training reasoning models become easy in Azure ML? \n", + "\n", + "\n", + "- **AzureML natively supports reasoning model training**, with seamless integration of vLLM and scalable training workflows.\n", + "\n", + "- **DeepSpeed scales effortlessly on AML**, enabling multi-node training by sharding model states across GPUs.\n", + "\n", + "- **LoRA support makes fine-tuning large models lightweight and cost-efficient**, even on smaller setups.\n", + "\n", + "- **Robust tracking, metrics, and debugging tools** make experimentation on AML smooth and production-ready." + ] + }, + { + "cell_type": "markdown", + "id": "28f0947a-3fcc-4809-a02f-8b5b7848e6d2", + "metadata": { + "nteract": { + "transient": { + "deleting": false + } + } + }, + "source": [ + "## GRPO Trainer Configuration \n", + "\n", + "There are 4 main configs and scripts to train a reasoning model using TRL. \n", + "\n", + "\n", + "**1. BldDemo_Reasoning_Train.py**\n", + "\n", + "This is the main script for running GRPO training. Here is a section, where one can control the **_base model (current policy)_**, **_reward function_**, **_dataset_** and **_LoRA_** configuration for PEFT (Parameter-Efficient Fine-Tuning). \n", + "\n", + "It's **important to note that the sampler, trainer and grader are abstracted** within the GRPO trainer implementation.\n", + "\n", + "\n", + "\"image.png\"\n", + "\n", + "**2. grpo_trainer_rewards.py**\n", + "\n", + "This file defines a set of reward functions **_used to evaluate model outputs_** during training for reasoning tasks. \n", + "\n", + "For example, a format_reward function **_encourages the mode_** to follow the correct output structure, while an accuracy_reward function **_promotes correct answers_**—both rewarding desired behavior and penalizing deviations. \n", + "\n", + "_format_reward_: \n", + "\n", + "\n", + "\"image.png\"\n", + "\n", + "\n", + "**3. grpo_trainer_config.yaml**\n", + "\n", + "This file defines the training configuration. Some of the key trainer config parameters are discussed below:\n", + "\n", + "a. **_vllm_mode_** - TRL leverages vLLM to accelerate sampling during reasoning model training.\n", + "\n", + "It is supported in two modes:\n", + "\n", + "- **Server Mode**\n", + " - vLLM runs on _dedicated_ nodes/GPUs\n", + " - Ideal for large-scale, high-throughput sampling\n", + "\n", + "- **Colocate Mode**\n", + " - vLLM and trainer _share_ the same GPU\n", + " - Useful for smaller setups or resource-constrained environments\n", + "\n", + "\"image.png\"\n", + "\n", + "b. **_reward_functions_**: The reward functions to **_use by the grader_**, we define the format and accuracy reward. \\\n", + "c. **_reward_weights_**: Weight of each reward function. We give **_more weight_** to the accuracy reward. \\\n", + "d. **_report_to_**: To integrate the **_logs and metrics_** into Azure ML.\n", + "\n", + "\n", + "\"image.png\"\n" + ] + }, + { + "cell_type": "markdown", + "id": "9b471827", + "metadata": { + "nteract": { + "transient": { + "deleting": false + } + } + }, + "source": [ + "**4. Deepspeed ZeRO config**\n", + "\n", + "Azure ML simplifies hardware scaling with built-in support for distributed training. Use DeepSpeed to maximize hardware efficiency across GPU clusters.\n", + "\n", + "In this example, ZeRO Stage3 config has been used where - The **model**, **Gradients** and **Optimizer States** are partitioned across the GPUs. \n", + "\n", + "This drastically reduces memory requirements per GPU and without ZeRO Stage 3, larger models would need significantly more GPUs.\n", + "\n", + "**Efficient scaling = Lower costs + ability to train much larger models**\n", + "\n", + "\n", + "\"image.png\"\n", + "\n", + "\n", + "a. _offload_optimizer_device = cpu_ : allows to offload optimizer states computations to be made on CPU. \\\n", + "b. _zero_stage_: Stage 3 optimization, **helps scale training horizontally** for bigger models. \\\n", + "c. _train_micro_batch_size_per_gpu_: **Batch size that a single GPU** processes in one forward/backward pass. \n", + "\n", + "A small train_micro_batch_size_per_gpu with offload_optimizer_device: cpu one can **fit bigger models** or **train on fewer GPUs** at the cost of slightly longer training times.\n", + "\n", + "\n", + "\"image.png\"\n" + ] + }, + { + "cell_type": "markdown", + "id": "6a47a690-4461-4306-9534-68d49a560374", + "metadata": { + "nteract": { + "transient": { + "deleting": false + } + } + }, + "source": [ + "# 🚀 Section 3: Launch the job! \n", + "The below section shows how to kick off the pytorch distributed command job and rut it across multiple nodes. \\\n", + "For this command job we pass the **GRPO trainer config, base model, dataset, DeepSpeed configuration** as arguments." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "de60b13c", + "metadata": { + "gather": { + "logged": 1747817752135 + } + }, + "outputs": [], + "source": [ + "from azure.ai.ml import command, Input, Output\n", + "from azure.ai.ml.entities import ManagedOnlineEndpoint, ManagedOnlineDeployment, Model\n", + "from azure.ai.ml.constants import AssetTypes\n", + "\n", + "# Below is a command job that takes grpo config, deepspeed config, the dataset and the model parameters as inputs.\n", + "# This kicks off a distributed job on a gpu cluster with 2 nodes (8XH100 on each).\n", + "command_str = \"\"\"python BldDemo_Reasoning_Train.py \\\n", + " --config grpo_trainer_config.yaml \\\n", + " --model_name_or_path ${{inputs.model_dir}} \\\n", + " --dataset_name ${{inputs.dataset}} \\\n", + " --output_dir ${{outputs.checkpoint_folder}} \\\n", + " --final_model_save_path ${{outputs.mlflow_model_folder}} \\\n", + " --deepspeed deepspeed_stage3_zero_config.json \\\n", + " --mlflow_task_type \"chat-completion\" \"\"\"\n", + "\n", + "# Base model name is passed as an argument to the script.\n", + "command_str += f'--base_model_name \"{model.name}\"'\n", + "\n", + "# Model directory and dataset as job inputs.\n", + "job_input = {\n", + " \"model_dir\": Input(\n", + " path=model.path,\n", + " type=AssetTypes.CUSTOM_MODEL,\n", + " ),\n", + " \"dataset\": Input(\n", + " type=AssetTypes.URI_FOLDER,\n", + " path=med_mcqa_data.path,\n", + " ),\n", + "}\n", + "\n", + "# The job outputs the finetuned model in mlflow format and the intermediate checkpoints.\n", + "job_output = {\n", + " \"mlflow_model_folder\": Output(\n", + " type=AssetTypes.CUSTOM_MODEL,\n", + " mode=\"rw_mount\",\n", + " ),\n", + " \"checkpoint_folder\": Output(\n", + " type=AssetTypes.URI_FOLDER,\n", + " mode=\"rw_mount\",\n", + " ),\n", + "}\n", + "\n", + "# Setting up the distributed training job.\n", + "job = command(\n", + " code=\"./src\",\n", + " inputs=job_input,\n", + " command=command_str,\n", + " environment=environment,\n", + " compute=compute.name,\n", + " instance_count=2,\n", + " outputs=job_output,\n", + " distribution={\n", + " \"type\": \"PyTorch\",\n", + " # set process count to the number of gpus per node\n", + " \"process_count_per_instance\": 8,\n", + " },\n", + " experiment_name=\"build-demo-reasoning-training-jobs\",\n", + " display_name=\"build-demo-reasoning-train-batchsize-16\",\n", + " properties={\"_azureml.LogTrainingMetricsToAzMon\": \"true\"},\n", + " # Environment variables to enable profiling\n", + " environment_variables={\n", + " \"KINETO_USE_DAEMON\": \"1\",\n", + " \"ENABLE_AZUREML_TRAINING_PROFILER\": \"true\",\n", + " \"AZUREML_PROFILER_WAIT_DURATION_SECOND\": \"2\",\n", + " \"AZUREML_PROFILER_RUN_DURATION_MILLISECOND\": \"500\",\n", + " \"AZUREML_COMMON_RUNTIME_USE_APPINSIGHTS_CAPABILITY\": \"true\",\n", + " },\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "5bad54d9-b35f-4f84-bf8c-0d9bcb393b1f", + "metadata": { + "nteract": { + "transient": { + "deleting": false + } + } + }, + "source": [ + "The below block will submit a machine learning job to Azure Machine Learning (AML) for execution.\n", + "\n", + "After submission, the returned **train_job object contains metadata and status information** about the job, such as its ID, current state, and output location. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e5a8ff68", + "metadata": { + "gather": { + "logged": 1747817763878 + } + }, + "outputs": [], + "source": [ + "# 🚀 Submit the job\n", + "train_job = ml_client.jobs.create_or_update(job)\n", + "train_job" + ] + }, + { + "cell_type": "markdown", + "id": "4072cec0", + "metadata": {}, + "source": [ + "## ⏳ **Wait** for the job to finish successfully, To move to next section..\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "f138ca1a-50fc-4847-8af2-dbcb9d152567", + "metadata": { + "nteract": { + "transient": { + "deleting": false + } + } + }, + "source": [ + "## Register and deploy the fine tuned model\n", + "\n", + "The output of the training is a set of files representing the weights of the trained model. To use it for inferencing, we will register the files as a model and then create an endpoint and a deployment for it. An endpoint provides security, url and traffic-splitting aspects of inferencing, whereas a deployment actually hosts and runs the registered model.\n", + "\n", + "You can find the assets registered in this section in the AzureML portal ([ml.azure.com](ml.azure.com)). Navigate to your resource group and workspace and click on the models or endpoints tab on the left panel. Deployments are sub-entities of endpoints and they can be found on the detailed view page of a particular endpoint." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "424bc234-5f4e-4cf0-91d1-9c1fb50226c0", + "metadata": {}, + "outputs": [], + "source": [ + "# Registering the model is necessary to deploy the model to an online endpoint.\n", + "\n", + "model_output_path = f\"azureml://jobs/{train_job.name}/outputs/mlflow_model_folder\"\n", + "run_model = Model(\n", + " path=model_output_path, # model output path from the job\n", + " name=\"grpo-finetuned-model\", # registered model name\n", + " description=f\"Model created from run {train_job.name}.\",\n", + " type=AssetTypes.MLFLOW_MODEL, # registering as mlflow model\n", + ")\n", + "\n", + "ft_model = ml_client.models.create_or_update(run_model)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ea9d47a1-620a-4591-a539-06420e7846b6", + "metadata": {}, + "outputs": [], + "source": [ + "online_endpoint_name = \"grpo-ft-model-endpoint\"\n", + "# create an online endpoint\n", + "endpoint = ManagedOnlineEndpoint(\n", + " name=online_endpoint_name, # name for the endpoint\n", + " description=\"Online endpoint for the GRPO fine-tuned model\",\n", + " auth_mode=\"key\",\n", + ")\n", + "endpoint = ml_client.begin_create_or_update(endpoint)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9aca9675-72eb-42fb-ae62-d75e0613d03f", + "metadata": {}, + "outputs": [], + "source": [ + "# A deployment will be created in the online endpoint.\n", + "deployment = ManagedOnlineDeployment(\n", + " name=\"grpo-ft-model-deployment\", # name for the deployment\n", + " endpoint_name=online_endpoint_name, # endpoint name where model will be deployed\n", + " model=ft_model, # finetuned and registered model\n", + " instance_type=\"Standard_ND96amsr_A100_v4\",\n", + " instance_count=1,\n", + ")\n", + "ml_client.begin_create_or_update(deployment)" + ] + }, + { + "cell_type": "markdown", + "id": "7640cf63", + "metadata": {}, + "source": [ + "## Results and metrics\n", + "\n", + "This job has a truncated dataset and fewer iterations than needed to see a significant principle for managing job runtime. But with over 100 iterations and the full dataset (takes about 6 hours on 16 H100s), you may see an improvement in the accuracy metric.\n", + "\n", + "As training progresses, the length of completions may increase with more iterations. Ideally, the mean completion length should stabilize over time, indicating that responses are not being capped and are of reasonable size. These metrics are of interest: \n", + "\n", + "- eval_rewards/accuracy/mean: This represents the mean of accuracy reward over the eval dataset as the training progresses. Note that we calculate this over the eval dataset but dont use the information from it to change our training.\n", + "- eval_completions/mean_length: This represents the mean of the completions over the eval dataset. We should see this metric increasing as the model beigns to reason, which usually takes more tokens.\n", + "- eval_reward: This represents how the overall reward (80% accuracy + 20% format) moved as the training progressed. We should see this increasing over longer runs.\n", + "- reward: This is the training version of the net reward. This should increase as well as the training progresses. This information is used to inform the training process.\n", + "\n", + "You can view this in AzureML portal under the metrics Tab of the job. Expand the panel to the left which says \"Select metrics\" and search for the above listed metrics." + ] + } + ], + "metadata": { + "kernel_info": { + "name": "python38-azureml" + }, + "kernelspec": { + "display_name": "aip-tutorials", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.17" + }, + "microsoft": { + "ms_spell_check": { + "ms_spell_check_language": "en" + } + }, + "nteract": { + "version": "nteract-front-end@1.0.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/sdk/python/jobs/grpo/src/BldDemo_Reasoning_Train.py b/sdk/python/jobs/grpo/src/BldDemo_Reasoning_Train.py new file mode 100644 index 0000000000..05868d9b5f --- /dev/null +++ b/sdk/python/jobs/grpo/src/BldDemo_Reasoning_Train.py @@ -0,0 +1,215 @@ +# --------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# --------------------------------------------------------- +""" +GRPO trainer +""" +import logging +import os +import sys +from dataclasses import dataclass, field + +import transformers +from transformers import AutoTokenizer, PreTrainedTokenizer, set_seed +from trl import ( + GRPOConfig, + GRPOTrainer, + ModelConfig, + ScriptArguments, + TrlParser, + get_peft_config, +) + +import datasets +from datasets import DatasetDict, load_dataset +from grpo_trainer_callbacks import SaveMLflowModelCallback +from grpo_trainer_rewards import get_rewards_funcs + +# VLLM_PP_LAYER_PARTITION = layers per pipeline stage +# VLLM_PP_NUM_PARTITIONS = number of pipeline stages (GPUs/processes) +# Both are essential for configuring pipeline parallelism in vLLM for efficient distributed training or inference. +os.environ["VLLM_PP_LAYER_PARTITION"] = "28" +os.environ["VLLM_PP_NUM_PARTITIONS"] = "8" + +logger = logging.getLogger(__name__) + + +# System prompt used at the time of sampling +system_prompt = "You are a helpful AI Assistant, designed to provided well-reasoned and detailed responses. You FIRST think about the reasoning process as an internal monologue and then provide the user with the answer. The reasoning process MUST BE enclosed within and tags." + +# Chat template used for the tokenizer +DEFAULT_CHAT_TEMPLATE = "{% for message in messages %}\n{% if message['role'] == 'user' %}\n{{ '<|user|>\n' + message['content'] + eos_token }}\n{% elif message['role'] == 'system' %}\n{{ '<|system|>\n' + message['content'] + eos_token }}\n{% elif message['role'] == 'assistant' %}\n{{ '<|assistant|>\n' + message['content'] + eos_token }}\n{% endif %}\n{% if loop.last and add_generation_prompt %}\n{{ '<|assistant|>' }}\n{% endif %}\n{% endfor %}" + + +@dataclass +class GRPOScriptArguments(ScriptArguments): + """ + Extra script arguments for the GRPO training script. + """ + + final_model_save_path: str = field( + default="final_model", metadata={"help": "Path to save the final model."} + ) + reward_funcs: list[str] = field( + default_factory=lambda: ["format"], + metadata={ + "help": "List of reward functions. Possible values: 'format', 'accuracy'." + }, + ) + mlflow_task_type: str = field(default="chat-completion") + base_model_name: str = field( + default="base_model", metadata={"help": "Base model name for MLflow."} + ) + + +def get_tokenizer(model_args: ModelConfig) -> PreTrainedTokenizer: + """Returns the tokenizer for the model. + + Args: + model_args (ModelConfig): Model configuration object. + Returns: + PreTrainedTokenizer: The tokenizer for the model. + """ + tokenizer = AutoTokenizer.from_pretrained( + model_args.model_name_or_path, + revision=model_args.model_revision, + trust_remote_code=model_args.trust_remote_code, + ) + tokenizer.chat_template = DEFAULT_CHAT_TEMPLATE + return tokenizer + + +def make_conversation(example, system_prompt=None): + """Transform the given record to be compatible for GRPO training. + + Args: + example (dict): The input record. + system_prompt (str): The system prompt to be used. + Returns: + dict: The transformed record. + """ + prompt = [] + + if system_prompt is not None: + prompt.append({"role": "system", "content": system_prompt}) + + prompt.append({"role": "user", "content": example["problem"]}) + return {"prompt": prompt} + + +def prepare_dataset(dataset_folder, system_prompt=None): + """Load the splits from the given dataset folder and transform the dataset to be compatible for GRPO training. + + Args: + dataset_folder (str): The folder containing the dataset splits. + Scans for train.jsonl, validation.jsonl, and test.jsonl. + system_prompt (str): The system prompt to be used. + Returns: + DatasetDict: The transformed dataset. + """ + split_names = ["train", "validation", "test"] + dataset_dict = {} + for split in split_names: + temp_path = os.path.join(dataset_folder, f"{split}.jsonl") + if os.path.exists(temp_path): + dataset_dict[split] = load_dataset( + "json", + data_files=temp_path, + split="train", + ) + + dataset = DatasetDict(dataset_dict) + dataset = dataset.map( + lambda example: make_conversation(example, system_prompt=system_prompt) + ) + for split in dataset: + if "messages" in dataset[split].column_names: + dataset[split] = dataset[split].remove_columns("messages") + return dataset + + +def main(script_args, training_args, model_args): + """Main function to run the GRPO training script. + + Args: + script_args (GRPOScriptArguments): Arguments to configure the datasets, reward functions. + training_args (GRPOConfig): Trainer specific settings such as vllm server config, + learning rate and reward weights. + model_args (ModelConfig): Arguments to load the model. + Returns: + None + """ + # Set seed for reproducibility + set_seed(training_args.seed) + + # Setup logging + logging.basicConfig( + format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", + datefmt="%Y-%m-%d %H:%M:%S", + handlers=[logging.StreamHandler(sys.stdout)], + ) + log_level = training_args.get_process_log_level() + logger.setLevel(log_level) + + datasets.utils.logging.set_verbosity(log_level) + + transformers.utils.logging.set_verbosity(log_level) + transformers.utils.logging.enable_explicit_format() + + logger.warning( + f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}" + + f" distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}" + ) + + logger.info(f"Model parameters {model_args}") + logger.info(f"Script parameters {script_args}") + logger.info(f"Training parameters {training_args}") + + dataset_path = script_args.dataset_name + current_policy = model_args.model_name_or_path + + # Load tokenizer + tokenizer = get_tokenizer(model_args) + + # Load the dataset + dataset = prepare_dataset(dataset_path, system_prompt=system_prompt) + logger.info(dataset) + + # Load the reward functions + reward_function = get_rewards_funcs(script_args.reward_funcs) + + # Add save callback + save_mlflow_callback = SaveMLflowModelCallback( + mlflow_model_save_path=script_args.final_model_save_path, + mlflow_task_type=script_args.mlflow_task_type, + base_model_name=script_args.base_model_name, + processing_class=tokenizer, + ) + + # Create the GRPOTrainer (It does SAMPLING, GRADING and TRAINING) + trainer = GRPOTrainer( + # The model to be trained, same copy of the model is used as reference policy. + model=current_policy, + # Rewards functions to be used by graders defined in "grpo_trainer_rewards.py". + reward_funcs=reward_function, + args=training_args, + # Each prompt from the dataset is used to generate multiple samples. + train_dataset=dataset[script_args.dataset_train_split], + # Configuration for lora. + peft_config=get_peft_config(model_args), + processing_class=tokenizer, + eval_dataset=( + dataset[script_args.dataset_test_split] + if training_args.eval_strategy != "no" + else None + ), + callbacks=[save_mlflow_callback], + ) + # Trigger the training loop + trainer.train() + + +if __name__ == "__main__": + parser = TrlParser((GRPOScriptArguments, GRPOConfig, ModelConfig)) + script_args, training_args, model_args = parser.parse_args_and_config() + main(script_args, training_args, model_args) diff --git a/sdk/python/jobs/grpo/src/deepspeed_stage3_zero_config.json b/sdk/python/jobs/grpo/src/deepspeed_stage3_zero_config.json new file mode 100644 index 0000000000..fb988054ec --- /dev/null +++ b/sdk/python/jobs/grpo/src/deepspeed_stage3_zero_config.json @@ -0,0 +1,44 @@ +{ + "deepspeed_multinode_launcher": "standard", + "offload_optimizer_device": "cpu", + "offload_param_device": "cpu", + "zero3_init_flag": true, + "zero3_save_16bit_model": true, + "zero_stage": 3, + "overlap_comm": true, + "sub_group_size": 1000000, + "stage3_max_live_parameters": 1000, + "stage3_max_reuse_distance": 1000000, + "stage3_gather_16bit_weights_on_model_save": true, + "train_batch_size": 16, + "train_micro_batch_size_per_gpu": 1, + "gradient_accumulation_steps" : 1, + "reduce_bucket_size": 5000000, + "stage3_prefetch_bucket_size": 5000000, + "stage3_param_persistence_threshold": 5000000, + "memory_efficient_linear": true, + "contiguous_gradients": true, + "zero_optimization": { + "stage": 3, + "cpu_offload": true, + "contiguous_gradients": true, + "sub_group_size": 1000000, + "stage3_prefetch_bucket_size": 5000000, + "stage3_param_persistence_threshold": 5000000, + "stage3_max_live_parameters": 1000, + "stage3_max_reuse_distance": 1000000, + "stage3_gather_16bit_weights_on_model_save": true + }, + "optimizer": { + "type": "Adam", + "params": { + "lr": 3e-06, + "betas": [ + 0.9, + 0.999 + ], + "eps": 1e-8, + "weight_decay": 0.0 + } + } +} \ No newline at end of file diff --git a/sdk/python/jobs/grpo/src/grpo_trainer_callbacks.py b/sdk/python/jobs/grpo/src/grpo_trainer_callbacks.py new file mode 100644 index 0000000000..0ec322e976 --- /dev/null +++ b/sdk/python/jobs/grpo/src/grpo_trainer_callbacks.py @@ -0,0 +1,111 @@ +# --------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# --------------------------------------------------------- +""" +callback utilities +""" +import logging +import shutil +from pathlib import Path +from typing import Union + +import azureml.evaluate.mlflow as mlflow +from mlflow.models import Model +from transformers import ( + TrainerCallback, + TrainerControl, + TrainerState, + TrainingArguments, +) + +logger = logging.getLogger(__name__) + + +def copy_tokenizer_files_to_model_folder(mlflow_model_folder: str): + """Copy tokenizer files to model folder. + The mlflow model format expects tokenizer and model files in model folder i.e. "data/model". + + Args: + mlflow_model_folder (str): Path to the mlflow model folder. + + Returns: + None + """ + src_dir = Path(mlflow_model_folder, "data", "tokenizer") + dst_dir = Path(mlflow_model_folder, "data", "model") + if src_dir.is_dir() and dst_dir.is_dir(): + logger.info("Copying tokenizer files to model folder") + shutil.copytree( + str(Path(mlflow_model_folder, "data", "tokenizer")), + str(Path(mlflow_model_folder, "data", "model")), + dirs_exist_ok=True, + ) + logger.info("Copy completed for tokenizer files to model folder") + else: + logger.warning("Couldn't copy the tokenizer files to model folder.") + + +class SaveMLflowModelCallback(TrainerCallback): + """ + A [`TrainerCallback`] that saves the model in mlflow model format. + """ + + def __init__( + self, + mlflow_model_save_path: Union[str, Path], + mlflow_task_type: str, + base_model_name: str, + **kwargs, + ) -> None: + + self.mlflow_model_save_path = mlflow_model_save_path + self.mlflow_task_type = mlflow_task_type + self.metadata = { + "azureml.base_image": "mcr.microsoft.com/azureml/curated/foundation-model-inference:71", + "base_model_asset_id": "", + "base_model_name": base_model_name, + "base_model_task": mlflow_task_type, + "finetuning_task": mlflow_task_type, + "is_acft_model": True, + "is_finetuned_model": True, + } + + def on_train_end( + self, + args: TrainingArguments, + state: TrainerState, + control: TrainerControl, + **kwargs, + ): + """ + Callback function to save the model in mlflow model format at the end of the training. + + Args: + args (TrainingArguments): The training arguments. + state (TrainerState): The trainer state. + control (TrainerControl): The trainer control. + **kwargs: Additional keyword arguments. The kwargs contain the model and tokenizer to be saved. + - model: The model to be saved. + - processing_class: The tokenizer to be used for saving the model. + Returns: + None + """ + full_path = self.mlflow_model_save_path + model, tokenizer = kwargs["model"], kwargs["processing_class"] + + # Saving the mlflow on world process 0 + if state.is_world_process_zero: + hf_conf = {"task_type": self.mlflow_task_type} + # This is to unify hfv2/OSS metadata dump + mlflow_model = Model(metadata=self.metadata) + mlflow.hftransformers.save_model( + model, + full_path, + tokenizer, + model.config, + mlflow_model=mlflow_model, + hf_conf=hf_conf, + ) + # Copy tokenizer files to model folder + copy_tokenizer_files_to_model_folder(full_path) + logger.info(f"MLflow model saved at {full_path}") diff --git a/sdk/python/jobs/grpo/src/grpo_trainer_config.yaml b/sdk/python/jobs/grpo/src/grpo_trainer_config.yaml new file mode 100644 index 0000000000..9092d5fded --- /dev/null +++ b/sdk/python/jobs/grpo/src/grpo_trainer_config.yaml @@ -0,0 +1,76 @@ +# There are two modes to run vllm for generating samples: server and colocate. +# We use the colocate mode here, which runs the sampler and trainer on the same GPU. +use_vllm: True +vllm_mode: "colocate" +# This ratio controls memory distribution between sampler and trainer. +vllm_gpu_memory_utilization: 0.25 +vllm_tensor_parallel_size: 4 + +# Evaluation settings +do_eval: true +eval_strategy: steps +eval_steps: 5 +eval_on_start: true +per_device_eval_batch_size: 1 + +# Flash Attention 2 distributes computations more granularly across GPU threads, reducing bottlenecks and improving throughput +attn_implementation: flash_attention_2 + +# Sampler settings +max_prompt_length: 1500 +max_completion_length: 5000 +# Number of samples to generate for each prompt +num_generations: 2 +log_completions: true +num_completions_to_print: 2 + +# Enable logging metrics to Azure ML +report_to: +- azure_ml + +# Logging settings +log_level: info +logging_first_step: true +logging_steps: 5 +logging_strategy: steps + +# Checkpoint settings +save_strategy: "steps" +save_steps: 100 +save_total_limit: 20 + +# Reward settings for sample rewarding. +reward_funcs: +- accuracy +- format +# reward = 0.8*accuracy + 0.2*format +# These weights helps us define the importance of each reward function. +reward_weights: +- 0.8 +- 0.2 + +# Training settings +max_steps: 100 +num_train_epochs: 3 +per_device_train_batch_size: 1 +learning_rate: 3.0e-06 +lr_scheduler_type: cosine +warmup_ratio: 0.1 + +# Specifies how many batches to process before performing a backward/update step. +# A higher value improves GPU utilization, but it comes with the risk of running out of memory. +gradient_accumulation_steps: 1 +gradient_checkpointing: true +gradient_checkpointing_kwargs: + use_reentrant: false +run_name: rlm8 +seed: 42 +overwrite_output_dir: false +push_to_hub: false +hub_model_id: NA +hub_strategy: checkpoint +hub_private_repo: true +model_revision: main +torch_dtype: bfloat16 +bf16: true +dataset_config: default \ No newline at end of file diff --git a/sdk/python/jobs/grpo/src/grpo_trainer_rewards.py b/sdk/python/jobs/grpo/src/grpo_trainer_rewards.py new file mode 100644 index 0000000000..05e44221ca --- /dev/null +++ b/sdk/python/jobs/grpo/src/grpo_trainer_rewards.py @@ -0,0 +1,167 @@ +# --------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# --------------------------------------------------------- +""" +reward functions +""" +import logging +import re +from typing import List + +logger = logging.getLogger(__name__) + + +def format_reward(completions, **kwargs): + """ + This function determines whether the predicted answer is in the correct format. + It checks if the reasoning process is enclosed within and tags, + while the final answer is enclosed within and tags. + + Args: + completions (list): List of model predictions. + Returns: + list: List of rewards (1.0 for correct format, 0.0 for incorrect format). + """ + pattern = r"^\n.*?\n\n\n.*?\n$" + completion_contents = [completion[0]["content"] for completion in completions] + matches = [ + re.match(pattern, content, re.DOTALL | re.MULTILINE) + for content in completion_contents + ] + return [1.0 if match else 0.0 for match in matches] + + +def _medmcqa_match_fn(pred, ref): + """ + This function determines whether a predicted answer matches the reference choice for a single MCQA record. + Even if the requested output format is not strictly followed, the function extracts the answer based on static patterns + and then matches the extracted answer (A, B, C, D) to the reference choice. + + The function works as follows: + It takes the model's full prediction text and splits it into individual lines. + It looks at the very last line: + If that line contains the word “Answer”, it strips out everything before the actual answer and cleans up whitespace/punctuation. + If the last line didn't include “Answer”, it then searches for any of these markers in the whole prediction: + “Final Answer” + “” + “<|begin_of_solution|>” For each marker, it finds the last occurrence, grabs up to 250 characters immediately after it (to avoid overly long context), and cleans that snippet. + If none of those markers appear at all, it falls back to simply cleaning up the last line. + Once it has a candidate answer string, it checks: + If it matches the pattern “[A-D].” (e.g. “B.”), it discards the dot and keeps just the letter. + If the resulting text isn't one of “A”, “B”, “C” or “D”, the function gives up and returns False. + Finally, if the cleaned-up answer letter is valid, it compares it to the reference label and returns True or False depending on whether they match. + + Args: + pred (str): The raw model prediction text. + ref (str): The reference answer choice (one of "A", "B", "C", "D"). + Returns: + bool: True if the extracted choice equals ref; False otherwise. + """ + + ext_pattern1 = r"Final Answer" + ext_pattern2 = r"" + ext_pattern3 = r"<|begin_of_solution|>" + + choices_patobj = re.compile(r"[A-D]\.") + logger.debug("**") + soln_start_match_fn = lambda pat: [ + match.span()[1] for match in re.finditer(pat, pred, re.DOTALL) + ] + lines = pred.split("\n") + + if len(lines) > 0: + ## Finding the answer snippet when the LLM response is in required format + check_next_cond = True + if lines[-1].find("Answer") >= 0: + hyp_unfilt = ( + lines[-1] + .replace("*", "") + .replace("Final Answer:", "") + .replace("Answer:", "") + .replace("", "") + .replace("", "") + .strip() + ) + check_next_cond = False + ## Finding the answer based on the "Final Answer" string in the LLM response + if check_next_cond: + check_next_cond = True + cond_match = soln_start_match_fn(ext_pattern1) + + if len(cond_match) > 0: + suffix_text = pred[cond_match[-1] :] + hyp_unfilt = suffix_text[: min(250, len(suffix_text))] + check_next_cond = False + ## Finding the answer based on the "" tags in the LLM response + if check_next_cond: + check_next_cond = True + cond_match = soln_start_match_fn(ext_pattern2) + if len(cond_match) > 0: + suffix_text = pred[cond_match[-1] :] + hyp_unfilt = suffix_text[: min(250, len(suffix_text))] + check_next_cond = False + ## Finding the answer based on the "<|begin_of_solution|>" tags in the LLM response + if check_next_cond: + check_next_cond = True + cond_match = soln_start_match_fn(ext_pattern3) + if len(cond_match) > 0: + suffix_text = pred[cond_match[-1] :] + hyp_unfilt = suffix_text[: min(250, len(suffix_text))] + check_next_cond = False + ## Last ditch attempt to parse the response into an answer + if check_next_cond: + hyp_unfilt = ( + lines[-1] + .replace("*", "") + .replace("Final Answer:", "") + .replace("Answer:", "") + .replace("", "") + .replace("", "") + .strip() + ) + + ## Extract the single letter answer from the response + if re.match(choices_patobj, hyp_unfilt) is not None: + hyp_unfilt = hyp_unfilt[0] + logger.debug(f"Hyp Unfilt: {hyp_unfilt}") + if hyp_unfilt not in ["A", "B", "C", "D"]: + logger.debug( + f"ERROR: Invalid answer (outside of allowed choices): {hyp_unfilt}" + ) + return False + + return hyp_unfilt == ref + + +def accuracy(completions, solution, **kwargs): + """Reward function that checks if the completion is the same as the ground truth. + + Args: + completions (list): List of model predictions. + solution (list): List of ground truth answers. + Returns: + list: List of rewards (1.0 for correct answer, 0.0 for incorrect answer). + """ + contents = [completion[0]["content"] for completion in completions] + rewards = [] + for content, sol in zip(contents, solution): + # _medmcqa_match_fn is used to calculate the accuracy reward for a single completion + rewards.append(float(_medmcqa_match_fn(content, sol))) + return rewards + + +reward_registry = { + "format": format_reward, + "accuracy": accuracy, +} + + +def get_rewards_funcs(reward_names: List[str]): + """Helper function to get the reward functions given the names + + Args: + reward_names (list): List of reward function names. + Returns: + list: List of reward functions. + """ + return [reward_registry[name] for name in reward_names if name in reward_registry] From 920e2c54e71a9e3f16c9713787db3b49e2001e6f Mon Sep 17 00:00:00 2001 From: javed-73 Date: Wed, 28 May 2025 16:11:16 +0530 Subject: [PATCH 02/37] update deployment env to ubuntu22.04 for automl notebook jobs (#3598) Co-authored-by: Mohd Javed Ansari --- .../automl-classification-task-bankmarketing.ipynb | 2 +- .../auto-ml-forecasting-function-gap-batch-inference.ipynb | 2 +- .../auto-ml-forecasting-github-dau.ipynb | 2 +- .../automl-forecasting-orange-juice-sales-mlflow.ipynb | 2 +- .../auto-ml-forecasting-bike-share.ipynb | 4 ++-- ...utoml-forecasting-task-energy-demand-advanced-mlflow.ipynb | 2 +- 6 files changed, 7 insertions(+), 7 deletions(-) diff --git a/sdk/python/jobs/automl-standalone-jobs/automl-classification-task-bankmarketing/automl-classification-task-bankmarketing.ipynb b/sdk/python/jobs/automl-standalone-jobs/automl-classification-task-bankmarketing/automl-classification-task-bankmarketing.ipynb index e5a358a728..34f1674057 100644 --- a/sdk/python/jobs/automl-standalone-jobs/automl-classification-task-bankmarketing/automl-classification-task-bankmarketing.ipynb +++ b/sdk/python/jobs/automl-standalone-jobs/automl-classification-task-bankmarketing/automl-classification-task-bankmarketing.ipynb @@ -665,7 +665,7 @@ "env = Environment(\n", " name=\"automl-tabular-env\",\n", " description=\"environment for automl inference\",\n", - " image=\"mcr.microsoft.com/azureml/openmpi4.1.0-ubuntu20.04:latest\",\n", + " image=\"mcr.microsoft.com/azureml/openmpi4.1.0-ubuntu22.04:latest\",\n", " conda_file=\"artifact_downloads/outputs/conda_env_v_1_0_0.yml\",\n", ")\n", "\n", diff --git a/sdk/python/jobs/automl-standalone-jobs/automl-forecasting-forecast-function/auto-ml-forecasting-function-gap-batch-inference.ipynb b/sdk/python/jobs/automl-standalone-jobs/automl-forecasting-forecast-function/auto-ml-forecasting-function-gap-batch-inference.ipynb index e40fea6025..d350a88a06 100644 --- a/sdk/python/jobs/automl-standalone-jobs/automl-forecasting-forecast-function/auto-ml-forecasting-function-gap-batch-inference.ipynb +++ b/sdk/python/jobs/automl-standalone-jobs/automl-forecasting-forecast-function/auto-ml-forecasting-function-gap-batch-inference.ipynb @@ -149,7 +149,7 @@ "env = Environment(\n", " name=\"automl-tabular-env\",\n", " description=\"environment for automl inference\",\n", - " image=\"mcr.microsoft.com/azureml/openmpi4.1.0-ubuntu20.04:latest\",\n", + " image=\"mcr.microsoft.com/azureml/openmpi4.1.0-ubuntu22.04:latest\",\n", " conda_file=\"artifact_downloads/outputs/conda_env_v_1_0_0.yml\",\n", ")\n", "\n", diff --git a/sdk/python/jobs/automl-standalone-jobs/automl-forecasting-github-dau/auto-ml-forecasting-github-dau.ipynb b/sdk/python/jobs/automl-standalone-jobs/automl-forecasting-github-dau/auto-ml-forecasting-github-dau.ipynb index 60c4ff444a..bfe92aa30f 100644 --- a/sdk/python/jobs/automl-standalone-jobs/automl-forecasting-github-dau/auto-ml-forecasting-github-dau.ipynb +++ b/sdk/python/jobs/automl-standalone-jobs/automl-forecasting-github-dau/auto-ml-forecasting-github-dau.ipynb @@ -714,7 +714,7 @@ "env = Environment(\n", " name=\"automl-tabular-env-tcn\",\n", " description=\"environment for automl TCN inference\",\n", - " image=\"mcr.microsoft.com/azureml/openmpi4.1.0-ubuntu20.04:latest\",\n", + " image=\"mcr.microsoft.com/azureml/openmpi4.1.0-ubuntu22.04:latest\",\n", " conda_file=\"artifact_downloads/outputs/conda_env_v_1_0_0.yml\",\n", ")\n", "\n", diff --git a/sdk/python/jobs/automl-standalone-jobs/automl-forecasting-orange-juice-sales/automl-forecasting-orange-juice-sales-mlflow.ipynb b/sdk/python/jobs/automl-standalone-jobs/automl-forecasting-orange-juice-sales/automl-forecasting-orange-juice-sales-mlflow.ipynb index e15b22a9d5..184b9a02c9 100644 --- a/sdk/python/jobs/automl-standalone-jobs/automl-forecasting-orange-juice-sales/automl-forecasting-orange-juice-sales-mlflow.ipynb +++ b/sdk/python/jobs/automl-standalone-jobs/automl-forecasting-orange-juice-sales/automl-forecasting-orange-juice-sales-mlflow.ipynb @@ -922,7 +922,7 @@ "env = Environment(\n", " name=\"automl-tabular-env\",\n", " description=\"environment for automl inference\",\n", - " image=\"mcr.microsoft.com/azureml/openmpi4.1.0-ubuntu20.04:latest\",\n", + " image=\"mcr.microsoft.com/azureml/openmpi4.1.0-ubuntu22.04:latest\",\n", " conda_file=\"artifact_downloads/outputs/conda_env_v_1_0_0.yml\",\n", ")\n", "\n", diff --git a/sdk/python/jobs/automl-standalone-jobs/automl-forecasting-task-bike-share/auto-ml-forecasting-bike-share.ipynb b/sdk/python/jobs/automl-standalone-jobs/automl-forecasting-task-bike-share/auto-ml-forecasting-bike-share.ipynb index 6817007a1b..f7a1f76439 100644 --- a/sdk/python/jobs/automl-standalone-jobs/automl-forecasting-task-bike-share/auto-ml-forecasting-bike-share.ipynb +++ b/sdk/python/jobs/automl-standalone-jobs/automl-forecasting-task-bike-share/auto-ml-forecasting-bike-share.ipynb @@ -422,7 +422,7 @@ "outputs": [], "source": [ "# Create the AutoML forecasting job with the related factory-function. Force the target column, to be integer type (To be added in phase 2)\n", - "forecasting_job = automl.forecasting(\n", + "forecasting_job = automl.forecasting(\n", " experiment_name=exp_name,\n", " training_data=my_training_data_input,\n", " target_column_name=target_column_name,\n", @@ -824,7 +824,7 @@ "env = Environment(\n", " name=\"automl-tabular-env\",\n", " description=\"environment for automl inference\",\n", - " image=\"mcr.microsoft.com/azureml/openmpi4.1.0-ubuntu20.04:latest\",\n", + " image=\"mcr.microsoft.com/azureml/openmpi4.1.0-ubuntu22.04:latest\",\n", " conda_file=\"artifact_downloads/outputs/conda_env_v_1_0_0.yml\",\n", ")\n", "\n", diff --git a/sdk/python/jobs/automl-standalone-jobs/automl-forecasting-task-energy-demand/automl-forecasting-task-energy-demand-advanced-mlflow.ipynb b/sdk/python/jobs/automl-standalone-jobs/automl-forecasting-task-energy-demand/automl-forecasting-task-energy-demand-advanced-mlflow.ipynb index d57b98cb78..978a4cd767 100644 --- a/sdk/python/jobs/automl-standalone-jobs/automl-forecasting-task-energy-demand/automl-forecasting-task-energy-demand-advanced-mlflow.ipynb +++ b/sdk/python/jobs/automl-standalone-jobs/automl-forecasting-task-energy-demand/automl-forecasting-task-energy-demand-advanced-mlflow.ipynb @@ -826,7 +826,7 @@ "env = Environment(\n", " name=\"automl-tabular-env\",\n", " description=\"environment for automl inference\",\n", - " image=\"mcr.microsoft.com/azureml/openmpi4.1.0-ubuntu20.04:latest\",\n", + " image=\"mcr.microsoft.com/azureml/openmpi4.1.0-ubuntu22.04:latest\",\n", " conda_file=\"artifact_downloads/outputs/conda_env_v_1_0_0.yml\",\n", ")\n", "\n", From 7724e99420281e9e1011e6d123b1d28c47d7da51 Mon Sep 17 00:00:00 2001 From: Sharvin J Date: Wed, 28 May 2025 17:26:59 +0530 Subject: [PATCH 03/37] Update CODEOWNERS (#3601) Add yeshwanth and harsha to code owners for GRPO --- .github/CODEOWNERS | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/CODEOWNERS b/.github/CODEOWNERS index be8ebdec39..22bd1549ba 100644 --- a/.github/CODEOWNERS +++ b/.github/CODEOWNERS @@ -25,7 +25,7 @@ sdk/python/foundation-models/cohere/command_tools-langchain.ipynb @stewart-co @k /sdk/python/foundation-models/healthcare-ai/ @jmerkow @ivantarapov /sdk/python/assets/data/versioning.ipynb @ShakutaiGit /sdk/python/jobs/finetuning @amltres @sasum @marici -/sdk/python/jobs/grpo @sharvin2187 @rtanase @gpenumetsa-msft +/sdk/python/jobs/grpo @sharvin2187 @rtanase @gpenumetsa-msft @yeshsurya @babu-namburi #### files referenced in docs (DO NOT EDIT, except for Docs team!!!) ############################################################################################# /cli/assets/component/train.yml @Azure/AI-Platform-Docs From 539744ebb783620a6136dd0ac42cda7692ac3fc0 Mon Sep 17 00:00:00 2001 From: Gayatri Penumetsa <181455625+gpenumetsa-msft@users.noreply.github.com> Date: Thu, 29 May 2025 13:15:54 +0530 Subject: [PATCH 04/37] Update launch_grpo_command_job-med-mcqa-commented.ipynb (#3599) Changed Agenda to 'Plan of Action' --- .../jobs/grpo/launch_grpo_command_job-med-mcqa-commented.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/sdk/python/jobs/grpo/launch_grpo_command_job-med-mcqa-commented.ipynb b/sdk/python/jobs/grpo/launch_grpo_command_job-med-mcqa-commented.ipynb index e36f569150..e95ac00f71 100644 --- a/sdk/python/jobs/grpo/launch_grpo_command_job-med-mcqa-commented.ipynb +++ b/sdk/python/jobs/grpo/launch_grpo_command_job-med-mcqa-commented.ipynb @@ -5,7 +5,7 @@ "id": "c8348fe9", "metadata": {}, "source": [ - "# 📝 Agenda\n", + "# 📝 Plan of Action\n", "\n", "This notebook guides you through the end-to-end process of fine-tuning the **Qwen2.5-7B-Instruct** model into a **reasoning model** using medical data on **Azure ML**. Qwen2.5-7B-Instruct is an instruction-tuned large language model developed by Alibaba Cloud, based on their Qwen2.5-7B foundation model. It is optimized for following human instructions across a wide range of tasks, such as question answering, code generation, and language understanding. In this walkthrough, one will learn how to enhance the model's reasoning capabilities using **Reinforced Fine-Tuning (RFT)** techniques, with a focus on **GRPO (**G**roup **R**elative **P**olicy **O**ptimization)**.\n", "\n", From 76827851906ac19d6312398e8331e61f3accfaa6 Mon Sep 17 00:00:00 2001 From: Sharvin J Date: Thu, 29 May 2025 13:50:07 +0530 Subject: [PATCH 05/37] Followup improvements to GRPO job example (#3600) * Followup improvements to GRPO job example * Add more docs to the readme * Update sdk/python/jobs/grpo/README.md Co-authored-by: Gayatri Penumetsa <181455625+gpenumetsa-msft@users.noreply.github.com> * Update sdk/python/jobs/grpo/README.md Co-authored-by: Gayatri Penumetsa <181455625+gpenumetsa-msft@users.noreply.github.com> * Update README.md --------- Co-authored-by: Sharvin Jondhale Co-authored-by: Gayatri Penumetsa <181455625+gpenumetsa-msft@users.noreply.github.com> --- sdk/python/jobs/grpo/README.md | 40 ++++++++++++++++++++++++++++++++-- 1 file changed, 38 insertions(+), 2 deletions(-) diff --git a/sdk/python/jobs/grpo/README.md b/sdk/python/jobs/grpo/README.md index aecad68046..b25cbc1d47 100644 --- a/sdk/python/jobs/grpo/README.md +++ b/sdk/python/jobs/grpo/README.md @@ -1,4 +1,40 @@ -### This directory hosts an example of running GRPO on AzureML +### This directory hosts an example to train an Instruct model into a Reasoning model on AML using Group Relative Policy Optimization (GRPO) - a RFT technique + +#### Introduction: +In this repo, one will find a notebook (launch_grpo_command_job-med-mcqa-commented.ipynb) that details an end-to-end process of fine-tuning the **Qwen2.5-7B-Instruct** model into a **reasoning model** using medical data on **Azure ML**. Qwen2.5-7B-Instruct is an instruction-tuned large language model developed by Alibaba Cloud, based on their Qwen2.5-7B foundation model. \ +It is optimized for following human instructions across a wide range of tasks, such as question answering, code generation, and language understanding. \ +In this walkthrough, one will learn how to enhance the model's reasoning capabilities using **Reinforced Fine-Tuning (RFT)** techniques, with a focus on **GRPO (**G**roup **R**elative **P**olicy **O**ptimization)**. + +image.png + +#### About the GRPO Trainer +
+
+

The reasoning model training process typically includes three key components:

+
    +
  • Sampler – Generates multiple candidate responses from the model
    • +
  • Reward Function – Evaluates and scores each response based on criteria like accuracy or structure
    • +
  • Trainer – Updates the model to reinforce high-quality outputs
    • +
+

+ In this example, the GRPO Trainer (an implementation from TRL library) is used for training Qwen2.5-7B-Instruct model into a reasoning model. +

+
+

+ GRPO (Group Relative Policy Optimization) is a reinforcement learning technique that: +

+
    +
  • Compares multiple answers within a group
    • +
  • Rewards the best-performing outputs
    • +
  • Penalizes poor ones
    • +
  • Applies careful updates to avoid sudden changes
    • +
+
+
+ Training Loop +
+
+ #### Repo structure: - aml_setup.py: AzureML specific code relating to creation of dataset, model and environment. The workspace config is located here, make changes to this file before running the notebook. @@ -10,4 +46,4 @@ - grpo_trainer_callbacks.py: Code which converts the output models to MLflow model format. This conversation simplifies deployment as AzureML automatically generated inferencing environment and server for models in this format. - grpo_trainer_rewards.py: Rewards for the training, these are python functions which take an LLM response and grade it for accuracy and format. -Additionally, [this video](https://youtu.be/YOm_IQt3YWw?si=5nZzyy-PZyP9XFSU&t=1344) offers an overview based on the contents of this repository. \ No newline at end of file +Additionally, [this video](https://youtu.be/YOm_IQt3YWw?si=5nZzyy-PZyP9XFSU&t=1344) offers an overview based on the contents of this repository. From f6e39a47b6e7999953e39877c2c6c3fed696800c Mon Sep 17 00:00:00 2001 From: javed-73 Date: Fri, 30 May 2025 16:00:43 +0530 Subject: [PATCH 06/37] Ignore warnings for Notebook test runs (#3603) Co-authored-by: Mohd Javed Ansari --- .github/test/scripts/check_notebook_output.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/.github/test/scripts/check_notebook_output.py b/.github/test/scripts/check_notebook_output.py index 5d46fffeaf..0e6654ccfc 100644 --- a/.github/test/scripts/check_notebook_output.py +++ b/.github/test/scripts/check_notebook_output.py @@ -82,6 +82,9 @@ "which generate code specific to the RPC implementation. service.py will be removed in Jan 2025" ), "from google.protobuf import service as _service", + "UserWarning: This class is intended as a base class and it's direct usage is deprecated", + "warnings.warn", + "UserWarning: pkg_resources is deprecated as an API.", ] with open(full_name, "r") as notebook_file: From 06204c0d6d668656678ca01c8436504948e6ffec Mon Sep 17 00:00:00 2001 From: javed-73 Date: Tue, 3 Jun 2025 14:27:04 +0530 Subject: [PATCH 07/37] fix deployment env image (#3606) Co-authored-by: Mohd Javed Ansari --- .../automl-forecasting-recipe-univariate-run.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/sdk/python/jobs/automl-standalone-jobs/automl-forecasting-recipes-univariate/automl-forecasting-recipe-univariate-run.ipynb b/sdk/python/jobs/automl-standalone-jobs/automl-forecasting-recipes-univariate/automl-forecasting-recipe-univariate-run.ipynb index fff90a86ee..338d6a3bdc 100644 --- a/sdk/python/jobs/automl-standalone-jobs/automl-forecasting-recipes-univariate/automl-forecasting-recipe-univariate-run.ipynb +++ b/sdk/python/jobs/automl-standalone-jobs/automl-forecasting-recipes-univariate/automl-forecasting-recipe-univariate-run.ipynb @@ -1118,7 +1118,7 @@ "env = Environment(\n", " name=\"automl-tabular-env\",\n", " description=\"environment for automl inference\",\n", - " image=\"mcr.microsoft.com/azureml/openmpi3.1.2-ubuntu18.04:20210727.v1\",\n", + " image=\"mcr.microsoft.com/azureml/openmpi4.1.0-ubuntu22.04:latest\",\n", " conda_file=\"artifact_downloads/outputs/conda_env_v_1_0_0.yml\",\n", ")\n", "\n", From 7f2136528b37d06d1143381abc68b38c6a8262b9 Mon Sep 17 00:00:00 2001 From: SeokJin Han <4353157+dem108@users.noreply.github.com> Date: Tue, 3 Jun 2025 18:33:53 -0700 Subject: [PATCH 08/37] fix broken link for SDK installation (#3605) --- .../online/triton/single-model/online-endpoints-triton.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/sdk/python/endpoints/online/triton/single-model/online-endpoints-triton.ipynb b/sdk/python/endpoints/online/triton/single-model/online-endpoints-triton.ipynb index 7c08c8dd40..ba978314e6 100644 --- a/sdk/python/endpoints/online/triton/single-model/online-endpoints-triton.ipynb +++ b/sdk/python/endpoints/online/triton/single-model/online-endpoints-triton.ipynb @@ -13,7 +13,7 @@ "\n", "* To use Azure Machine Learning, you must have an Azure subscription. If you don't have an Azure subscription, create a free account before you begin. Try the [free or paid version of Azure Machine Learning](https://azure.microsoft.com/free/).\n", "\n", - "* Install and configure the [Python SDK v2](sdk/setup.sh).\n", + "* Install and configure the [Python SDK v2](https://learn.microsoft.com/python/api/overview/azure/ai-ml-readme?view=azure-python#install-the-package).\n", "\n", "* You must have an Azure resource group, and you (or the service principal you use) must have Contributor access to it.\n", "\n", From 794431ac6a4a3c5dc83a1f96a31e155f7c3318d3 Mon Sep 17 00:00:00 2001 From: Kshitij Chawla <166698309+kshitij-microsoft@users.noreply.github.com> Date: Thu, 5 Jun 2025 14:44:50 +0530 Subject: [PATCH 09/37] Fail workflow of sdk install temporarily to test monitor (#3611) * fail sdk installation workflow temporarily --- .github/workflows/sdk-basic-installation-basic-installation.yml | 2 ++ 1 file changed, 2 insertions(+) diff --git a/.github/workflows/sdk-basic-installation-basic-installation.yml b/.github/workflows/sdk-basic-installation-basic-installation.yml index b91a482b31..42ed3414dd 100644 --- a/.github/workflows/sdk-basic-installation-basic-installation.yml +++ b/.github/workflows/sdk-basic-installation-basic-installation.yml @@ -92,3 +92,5 @@ jobs: with: name: basic-installation path: sdk/python/basic-installation + - name: Fail intentionally + run: exit 1 From 305b30381b31e43149c6a68ab5782a83523de49c Mon Sep 17 00:00:00 2001 From: Salman Arshad <63209621+salmanarshad97@users.noreply.github.com> Date: Thu, 5 Jun 2025 21:44:10 +0530 Subject: [PATCH 10/37] Update azureml inference server http. (#3590) * Replace azureml-defaults with azureml-inference-server-http==1.4.0 in environment configuration files * update azureml-inference-server-http * update inference-schema and joblib in conda.yaml * Replace azureml-defaults with azureml-inference-server-http==1.4.0 in environment configuration files * update azureml-inference-server-http * update inference-schema and joblib in conda.yaml --------- Co-authored-by: Salman Arshad --- .../online/managed/minimal/single-model-registered/env.yml | 2 +- .../online/managed/vnet/sample/environment/conda.yaml | 2 +- .../online/model-1/environment/conda-managedidentity.yaml | 2 +- cli/endpoints/online/model-1/environment/conda.yaml | 6 +++--- cli/endpoints/online/model-2/environment/conda.yaml | 6 +++--- 5 files changed, 9 insertions(+), 9 deletions(-) diff --git a/cli/endpoints/online/managed/minimal/single-model-registered/env.yml b/cli/endpoints/online/managed/minimal/single-model-registered/env.yml index 54e6470e1b..fd78136df1 100644 --- a/cli/endpoints/online/managed/minimal/single-model-registered/env.yml +++ b/cli/endpoints/online/managed/minimal/single-model-registered/env.yml @@ -8,6 +8,6 @@ dependencies: - scikit-learn=1.2.2 - scipy=1.10.1 - pip: - - azureml-defaults==1.49.0 + - azureml-inference-server-http==1.4.0 - joblib==1.2.0 - azureml-inference-server-http \ No newline at end of file diff --git a/cli/endpoints/online/managed/vnet/sample/environment/conda.yaml b/cli/endpoints/online/managed/vnet/sample/environment/conda.yaml index c5d9e5c53f..427107bc86 100644 --- a/cli/endpoints/online/managed/vnet/sample/environment/conda.yaml +++ b/cli/endpoints/online/managed/vnet/sample/environment/conda.yaml @@ -9,4 +9,4 @@ dependencies: - scipy=1.10.1 - pip: - joblib==1.2.0 - - azureml-defaults==1.49.0 + - azureml-inference-server-http==1.4.0 diff --git a/cli/endpoints/online/model-1/environment/conda-managedidentity.yaml b/cli/endpoints/online/model-1/environment/conda-managedidentity.yaml index 4e72c53a00..47d2a3e735 100644 --- a/cli/endpoints/online/model-1/environment/conda-managedidentity.yaml +++ b/cli/endpoints/online/model-1/environment/conda-managedidentity.yaml @@ -8,7 +8,7 @@ dependencies: - scikit-learn=0.24.2 - scipy=1.7.1 - pip: - - azureml-defaults==1.38.0 + - azureml-inference-server-http==1.4.0 - joblib==1.2.0 - azure-storage-blob==12.11 - azure-identity==1.7 diff --git a/cli/endpoints/online/model-1/environment/conda.yaml b/cli/endpoints/online/model-1/environment/conda.yaml index e28f80df02..c1abe979a4 100644 --- a/cli/endpoints/online/model-1/environment/conda.yaml +++ b/cli/endpoints/online/model-1/environment/conda.yaml @@ -8,6 +8,6 @@ dependencies: - scikit-learn=1.2.2 - scipy=1.10.1 - pip: - - azureml-defaults==1.56.0.post1 - - inference-schema[numpy-support]==1.7 - - joblib==1.2.0 + - azureml-inference-server-http==1.4.0 + - inference-schema[numpy-support] + - joblib diff --git a/cli/endpoints/online/model-2/environment/conda.yaml b/cli/endpoints/online/model-2/environment/conda.yaml index c2dea8edbb..f2f888f065 100644 --- a/cli/endpoints/online/model-2/environment/conda.yaml +++ b/cli/endpoints/online/model-2/environment/conda.yaml @@ -8,6 +8,6 @@ dependencies: - scikit-learn=1.2.2 - scipy=1.10.1 - pip: - - azureml-defaults==1.56.0.post1 - - inference-schema[numpy-support]==1.7 - - joblib==1.2.0 \ No newline at end of file + - azureml-inference-server-http==1.4.0 + - inference-schema[numpy-support] + - joblib \ No newline at end of file From 000b74cc8616f726bdf602f4dec03499963bc17e Mon Sep 17 00:00:00 2001 From: Jayesh Tanna Date: Wed, 11 Jun 2025 10:15:25 +0530 Subject: [PATCH 11/37] Configure mcp server for samples (#3614) * Adding mcp server content * updating content * add more content to it * adding more details * adding pr details * formating * updating readme file * updating readme * formating the content * formatting * applying formatng * applying formating * applying formatting * adding more ocntent --- setup/setup-repo/README.md | 65 ++++++++++++++++++++++++++++++++++++++ 1 file changed, 65 insertions(+) diff --git a/setup/setup-repo/README.md b/setup/setup-repo/README.md index 9af7bf2007..3109f48bbb 100644 --- a/setup/setup-repo/README.md +++ b/setup/setup-repo/README.md @@ -25,3 +25,68 @@ Required CLI tools include: - `azcopy` Ensure you `az login` and `azcopy login` and have permissions to set secrets via `gh`. + +# Setup MCP server + +Setting up the GitHub MCP server will expedite the implementation of the samples process from a minimum of 3-4 days to just minutes. This setup is completely optional and is shared here to help you generate your code (samples/workflows) in minutes. Please update below steps if something needs to add or modify. + +## Prerequisites +1. Install [Docker desktop](https://docs.docker.com/desktop/setup/install/windows-install/) +2. Install [VScode](https://code.visualstudio.com/download). You can choose your own IDE like cursor. + +## Steps +1. Start Docker desktop. +2. Open Azureml-examples repo in VScode. +3. Switch to the Agent mode. +4. Preferred model is Claude Sonnet 4 but you can choose any model of your wish. +5. Create Github personal access token. Make sure you dont push this anywhere in the code. + 1. Visit this link: https://github.com/settings/personal-access-tokens and click generate new token. + 2. Set token name, expiration etc. + 3. In Repository permissions, modify below permissions and Click on "Generate token" + * Contents -> Read and write + * Pull request -> Read and write + * Metadata -> Read only +6. [Onetime] Update settings (Press Ctrl + Shift + P, type Setting and Click on `Open User Setting (JSON)`) and add [github mcp servers](https://github.com/github/github-mcp-server) details as below. +``` + "chat.mcp.discovery.enabled": true, + "mcp": { + "inputs": [ + { + "type": "promptString", + "id": "github_token", + "description": "GitHub Personal Access Token", + "password": true + } + ], + "servers": { + "github": { + "command": "docker", + "args": [ + "run", + "-i", + "--rm", + "-e", + "GITHUB_PERSONAL_ACCESS_TOKEN", + "ghcr.io/github/github-mcp-server" + ], + "env": { + "GITHUB_PERSONAL_ACCESS_TOKEN": "${input:github_token}" + } + } + } + } +``` +7. Click on Start link in Settings window and it will ask you for the github token to enter. Verify that new container should start in Docker desktop after you enter your PAT. +8. Follow below prompts for generating all the boiler plate code. These prompts are for demonstration purpose only. You can use your own prompts also. + +## Sample Prompts +1. Create new branch named and dont push it to github. + - Example: Create new branch named mcp_test_3 and dont push it to github +2. Add new file in folder and update the readme file and also add readme file in folder. + - Example: Add new file sdkmcpsample.ipynb in sdk/python/assets/sdkmcpsample folder and update the readme file and also add readme file in sdkmcpsample folder. +3. Can you also add workflow for this ipynb file in .github/workflows folder exactly like how other workflows created? +4. Can you commit and push these changes to the current branch and raise pull request with main branch? + +Here is the sample pull request: https://github.com/Azure/azureml-examples/pull/3615 + +**Note that AI generated output can be incorrect. Before raising/merging pull request, consider to verify the output and make the require changes.** From 047ef2590ca404bf04b2d8412c546eb828fca6f8 Mon Sep 17 00:00:00 2001 From: Sharvin J Date: Wed, 11 Jun 2025 17:33:07 +0530 Subject: [PATCH 12/37] Add config for single node runs (#3604) * Add config for single node runs * Fix notebook metadata * Run black formatter --------- Co-authored-by: Sharvin Jondhale --- sdk/python/jobs/grpo/aml_setup.py | 7 +- ..._grpo_command_job-med-mcqa-commented.ipynb | 18 ++--- ...pspeed_stage3_zero_config_single_node.json | 44 +++++++++++ .../src/grpo_trainer_config_single_node.yaml | 76 +++++++++++++++++++ 4 files changed, 133 insertions(+), 12 deletions(-) create mode 100644 sdk/python/jobs/grpo/src/deepspeed_stage3_zero_config_single_node.json create mode 100644 sdk/python/jobs/grpo/src/grpo_trainer_config_single_node.yaml diff --git a/sdk/python/jobs/grpo/aml_setup.py b/sdk/python/jobs/grpo/aml_setup.py index 2b6f2b13e8..c76200158c 100644 --- a/sdk/python/jobs/grpo/aml_setup.py +++ b/sdk/python/jobs/grpo/aml_setup.py @@ -33,6 +33,7 @@ AML_SUBSCRIPTION = "" AML_RESOURCE_GROUP = "" AML_WORKSPACE_NAME = "" +N_NODES = 2 # Number of nodes to use for training, options: [1, 2] # Initialize the MLClient to connect to your Azure ML workspace ml_client = MLClient( @@ -97,7 +98,7 @@ def setup_compute(): """ # Compute Cluster Setup: Select or Create GPU Compute for Training - # Specify the desired Azure VM size (default: 8 x H100 GPUs). This job requires falsh attention and needs A100 or H100 GPUs. + # Specify the desired Azure VM size (default: 8 x H100 GPUs). This job requires flash attention and needs A100 or H100 GPUs. compute_cluster_size = "STANDARD_ND96ISR_H100_V5" # Name of the compute cluster to use (change if you have a different cluster) @@ -117,8 +118,8 @@ def setup_compute(): name=compute_cluster, size=compute_cluster_size, tier="Dedicated", - max_instances=2, # Increase for multi-node training - min_instances=2, + max_instances=N_NODES, + min_instances=N_NODES, ) ml_client.compute.begin_create_or_update(compute).wait() print("✅ Compute cluster created successfully.") diff --git a/sdk/python/jobs/grpo/launch_grpo_command_job-med-mcqa-commented.ipynb b/sdk/python/jobs/grpo/launch_grpo_command_job-med-mcqa-commented.ipynb index e95ac00f71..d7d3a52aeb 100644 --- a/sdk/python/jobs/grpo/launch_grpo_command_job-med-mcqa-commented.ipynb +++ b/sdk/python/jobs/grpo/launch_grpo_command_job-med-mcqa-commented.ipynb @@ -354,20 +354,20 @@ "from azure.ai.ml import command, Input, Output\n", "from azure.ai.ml.entities import ManagedOnlineEndpoint, ManagedOnlineDeployment, Model\n", "from azure.ai.ml.constants import AssetTypes\n", + "from aml_setup import N_NODES\n", "\n", "# Below is a command job that takes grpo config, deepspeed config, the dataset and the model parameters as inputs.\n", "# This kicks off a distributed job on a gpu cluster with 2 nodes (8XH100 on each).\n", - "command_str = \"\"\"python BldDemo_Reasoning_Train.py \\\n", - " --config grpo_trainer_config.yaml \\\n", + "command_str = f\"\"\"python BldDemo_Reasoning_Train.py \\\n", + " --config {\"grpo_trainer_config.yaml\" if N_NODES==2 else \"grpo_trainer_config_single_node.yaml\"} \\\n", " --model_name_or_path ${{inputs.model_dir}} \\\n", " --dataset_name ${{inputs.dataset}} \\\n", " --output_dir ${{outputs.checkpoint_folder}} \\\n", " --final_model_save_path ${{outputs.mlflow_model_folder}} \\\n", - " --deepspeed deepspeed_stage3_zero_config.json \\\n", - " --mlflow_task_type \"chat-completion\" \"\"\"\n", - "\n", - "# Base model name is passed as an argument to the script.\n", - "command_str += f'--base_model_name \"{model.name}\"'\n", + " --deepspeed {\"deepspeed_stage3_zero_config.json\" if N_NODES==2 else \"deepspeed_stage3_zero_config_single_node.json\"} \\\n", + " --mlflow_task_type \"chat-completion\" \\\n", + " --base_model_name \"{model.name}\"\n", + "\"\"\"\n", "\n", "# Model directory and dataset as job inputs.\n", "job_input = {\n", @@ -400,7 +400,7 @@ " command=command_str,\n", " environment=environment,\n", " compute=compute.name,\n", - " instance_count=2,\n", + " instance_count=N_NODES,\n", " outputs=job_output,\n", " distribution={\n", " \"type\": \"PyTorch\",\n", @@ -408,7 +408,7 @@ " \"process_count_per_instance\": 8,\n", " },\n", " experiment_name=\"build-demo-reasoning-training-jobs\",\n", - " display_name=\"build-demo-reasoning-train-batchsize-16\",\n", + " display_name=f\"build-demo-reasoning-train-batchsize-{N_NODES*8}\",\n", " properties={\"_azureml.LogTrainingMetricsToAzMon\": \"true\"},\n", " # Environment variables to enable profiling\n", " environment_variables={\n", diff --git a/sdk/python/jobs/grpo/src/deepspeed_stage3_zero_config_single_node.json b/sdk/python/jobs/grpo/src/deepspeed_stage3_zero_config_single_node.json new file mode 100644 index 0000000000..1c571620e4 --- /dev/null +++ b/sdk/python/jobs/grpo/src/deepspeed_stage3_zero_config_single_node.json @@ -0,0 +1,44 @@ +{ + "deepspeed_multinode_launcher": "standard", + "offload_optimizer_device": "cpu", + "offload_param_device": "cpu", + "zero3_init_flag": true, + "zero3_save_16bit_model": true, + "zero_stage": 3, + "overlap_comm": true, + "sub_group_size": 1000000, + "stage3_max_live_parameters": 1000, + "stage3_max_reuse_distance": 1000000, + "stage3_gather_16bit_weights_on_model_save": true, + "train_batch_size": 8, + "train_micro_batch_size_per_gpu": 1, + "gradient_accumulation_steps" : 1, + "reduce_bucket_size": 5000000, + "stage3_prefetch_bucket_size": 5000000, + "stage3_param_persistence_threshold": 5000000, + "memory_efficient_linear": true, + "contiguous_gradients": true, + "zero_optimization": { + "stage": 3, + "cpu_offload": true, + "contiguous_gradients": true, + "sub_group_size": 1000000, + "stage3_prefetch_bucket_size": 5000000, + "stage3_param_persistence_threshold": 5000000, + "stage3_max_live_parameters": 1000, + "stage3_max_reuse_distance": 1000000, + "stage3_gather_16bit_weights_on_model_save": true + }, + "optimizer": { + "type": "Adam", + "params": { + "lr": 3e-06, + "betas": [ + 0.9, + 0.999 + ], + "eps": 1e-8, + "weight_decay": 0.0 + } + } +} \ No newline at end of file diff --git a/sdk/python/jobs/grpo/src/grpo_trainer_config_single_node.yaml b/sdk/python/jobs/grpo/src/grpo_trainer_config_single_node.yaml new file mode 100644 index 0000000000..5ef893d5e8 --- /dev/null +++ b/sdk/python/jobs/grpo/src/grpo_trainer_config_single_node.yaml @@ -0,0 +1,76 @@ +# There are two modes to run vllm for generating samples: server and colocate. +# We use the colocate mode here, which runs the sampler and trainer on the same GPU. +use_vllm: True +vllm_mode: "colocate" +# This ratio controls memory distribution between sampler and trainer. +vllm_gpu_memory_utilization: 0.25 +vllm_tensor_parallel_size: 4 + +# Evaluation settings +do_eval: true +eval_strategy: steps +eval_steps: 5 +eval_on_start: true +per_device_eval_batch_size: 1 + +# Flash Attention 2 distributes computations more granularly across GPU threads, reducing bottlenecks and improving throughput +attn_implementation: flash_attention_2 + +# Sampler settings +max_prompt_length: 1500 +max_completion_length: 5000 +# Number of samples to generate for each prompt +num_generations: 2 +log_completions: true +num_completions_to_print: 2 + +# Enable logging metrics to Azure ML +report_to: +- azure_ml + +# Logging settings +log_level: info +logging_first_step: true +logging_steps: 5 +logging_strategy: steps + +# Checkpoint settings +save_strategy: "steps" +save_steps: 100 +save_total_limit: 20 + +# Reward settings for sample rewarding. +reward_funcs: +- accuracy +- format +# reward = 0.8*accuracy + 0.2*format +# These weights helps us define the importance of each reward function. +reward_weights: +- 0.8 +- 0.2 + +# Training settings +max_steps: 100 +num_train_epochs: 3 +per_device_train_batch_size: 1 +learning_rate: 3.0e-06 +lr_scheduler_type: cosine +warmup_ratio: 0.1 + +# Specifies how many batches to process before performing a backward/update step. +# A higher value improves GPU utilization, but it comes with the risk of running out of memory. +gradient_accumulation_steps: 1 +gradient_checkpointing: true +gradient_checkpointing_kwargs: + use_reentrant: false +run_name: rlm8 +seed: 42 +overwrite_output_dir: false +push_to_hub: false +hub_model_id: NA +hub_strategy: checkpoint +hub_private_repo: true +model_revision: main +torch_dtype: bfloat16 +bf16: true +dataset_config: default \ No newline at end of file From 642661c9563fe90393c3aba4820e6669ba9a4ed1 Mon Sep 17 00:00:00 2001 From: Yeshwanth N Date: Fri, 13 Jun 2025 14:53:40 +0530 Subject: [PATCH 13/37] [Fix]: Add accuracy also and reword informative assertions (#3617) * [Fix]: Add accuracy also and reword informative assertions --- sdk/python/jobs/grpo/aml_setup.py | 6 +++--- sdk/python/jobs/grpo/src/BldDemo_Reasoning_Train.py | 2 +- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/sdk/python/jobs/grpo/aml_setup.py b/sdk/python/jobs/grpo/aml_setup.py index c76200158c..53472424f2 100644 --- a/sdk/python/jobs/grpo/aml_setup.py +++ b/sdk/python/jobs/grpo/aml_setup.py @@ -75,7 +75,7 @@ def setup_model(): # Download model from Hugging Face and register in Azure ML workspace try: model = ml_client.models.get(name=model_name, label="latest") - print(f"✅ Model {model_name} created in AML workspace.") + print(f"✅ Selected model {model_name} from AML workspace.") except ResourceNotFoundError: print(f"❌ Model {model_name} not found, downloading and registering...") from huggingface_hub import snapshot_download @@ -107,7 +107,7 @@ def setup_compute(): try: # Try to get the existing compute cluster compute = ml_client.compute.get(compute_cluster) - print(f"✅ Compute cluster {compute_cluster} created in AML workspace.") + print(f"✅ Selected compute cluster {compute_cluster} from AML workspace.") except Exception: print( f"❌ Compute cluster '{compute_cluster}' not found. Creating a new one ({compute_cluster_size})..." @@ -149,7 +149,7 @@ def setup_environment(): try: # Try to get the existing environment from Azure ML workspace environment = ml_client.environments.get(name=env_name, label="latest") - print(f"✅ Environment {env_name} created in AML workspace.") + print(f"✅ Selected environment {env_name} from AML workspace.") except ResourceNotFoundError as e: # If not found, create a new environment using the specified build context print(f"❌ Environment {env_name} not found, creating a new one.") diff --git a/sdk/python/jobs/grpo/src/BldDemo_Reasoning_Train.py b/sdk/python/jobs/grpo/src/BldDemo_Reasoning_Train.py index 05868d9b5f..ad0dc1a7ca 100644 --- a/sdk/python/jobs/grpo/src/BldDemo_Reasoning_Train.py +++ b/sdk/python/jobs/grpo/src/BldDemo_Reasoning_Train.py @@ -51,7 +51,7 @@ class GRPOScriptArguments(ScriptArguments): default="final_model", metadata={"help": "Path to save the final model."} ) reward_funcs: list[str] = field( - default_factory=lambda: ["format"], + default_factory=lambda: ["format", "accuracy"], metadata={ "help": "List of reward functions. Possible values: 'format', 'accuracy'." }, From 7694c8cb91fbf9d211aed909c6b25eb489077246 Mon Sep 17 00:00:00 2001 From: Sheri Gilley Date: Tue, 17 Jun 2025 13:22:44 -0500 Subject: [PATCH 14/37] Create copilot-instructions.md (#3620) --- .github/copilot-instructions.md | 11 +++++++++++ 1 file changed, 11 insertions(+) create mode 100644 .github/copilot-instructions.md diff --git a/.github/copilot-instructions.md b/.github/copilot-instructions.md new file mode 100644 index 0000000000..4fa8291d29 --- /dev/null +++ b/.github/copilot-instructions.md @@ -0,0 +1,11 @@ +## Files owned by the AI Platform Docs team + +If a file is listed in the CODEOWNERS file with @azure/ai-platform-docs as the owner, it is owned by the AI Platform Docs team. For these files: + +- Do not change the filename or move the file. +- Do not remove any comments which contain or (for any text in between the tags) +- Do not remove any cell in a notebook if it contains metadata with "name:" in it. +- If you need to make any of the above changes, you must first contact the AI Platform Docs team. Their approval is required before making such changes. +- You can reach the team on the Teams channel [ai-platform-docs](https://teams.microsoft.com/l/team/19%3AHhf4F_YfPn3kYGdmWvePNwlbF5-RR8wciQEUwwrcggw1%40thread.tacv2/conversations?groupId=fdaf4412-8993-4ea6-a7d4-aeaded7fc854&tenantId=72f988bf-86f1-41af-91ab-2d7cd011db47). + +Only files owned by the AI Platform Docs team are subject to these rules. From 94732264e5776c15b4d24bb1fd419454dcb8e802 Mon Sep 17 00:00:00 2001 From: qusongms <142928570+qusongms@users.noreply.github.com> Date: Tue, 17 Jun 2025 13:50:28 -0700 Subject: [PATCH 15/37] Qunsong/remove image notebooks (#3621) * remove responsibleai-vision notebooks and related workflows --- ...ard-automl-image-classification-fridge.yml | 96 -- ...l-object-detection-fridge-private-data.yml | 96 -- ...idashboard-image-classification-fridge.yml | 96 -- ...idashboard-image-flower-classification.yml | 90 -- ...image-multilabel-classification-fridge.yml | 96 -- ...bleaidashboard-object-detection-MSCOCO.yml | 96 -- sdk/python/README.md | 6 - sdk/python/responsible-ai/README.md | 6 - sdk/python/responsible-ai/vision/README.md | 54 - ...d-automl-image-classification-fridge.ipynb | 1295 ---------------- ...object-detection-fridge-private-data.ipynb | 1303 ---------------- ...ashboard-image-classification-fridge.ipynb | 996 ------------- ...ashboard-image-flower-classification.ipynb | 1325 ----------------- ...age-multilabel-classification-fridge.ipynb | 999 ------------- ...eaidashboard-object-detection-MSCOCO.ipynb | 773 ---------- 15 files changed, 7327 deletions(-) delete mode 100644 .github/workflows/sdk-responsible-ai-vision-responsibleaidashboard-automl-image-classification-fridge.yml delete mode 100644 .github/workflows/sdk-responsible-ai-vision-responsibleaidashboard-automl-object-detection-fridge-private-data.yml delete mode 100644 .github/workflows/sdk-responsible-ai-vision-responsibleaidashboard-image-classification-fridge.yml delete mode 100644 .github/workflows/sdk-responsible-ai-vision-responsibleaidashboard-image-flower-classification.yml delete mode 100644 .github/workflows/sdk-responsible-ai-vision-responsibleaidashboard-image-multilabel-classification-fridge.yml delete mode 100644 .github/workflows/sdk-responsible-ai-vision-responsibleaidashboard-object-detection-MSCOCO.yml delete mode 100644 sdk/python/responsible-ai/vision/README.md delete mode 100644 sdk/python/responsible-ai/vision/responsibleaidashboard-automl-image-classification-fridge.ipynb delete mode 100644 sdk/python/responsible-ai/vision/responsibleaidashboard-automl-object-detection-fridge-private-data.ipynb delete mode 100644 sdk/python/responsible-ai/vision/responsibleaidashboard-image-classification-fridge.ipynb delete mode 100644 sdk/python/responsible-ai/vision/responsibleaidashboard-image-flower-classification.ipynb delete mode 100644 sdk/python/responsible-ai/vision/responsibleaidashboard-image-multilabel-classification-fridge.ipynb delete mode 100644 sdk/python/responsible-ai/vision/responsibleaidashboard-object-detection-MSCOCO.ipynb diff --git a/.github/workflows/sdk-responsible-ai-vision-responsibleaidashboard-automl-image-classification-fridge.yml b/.github/workflows/sdk-responsible-ai-vision-responsibleaidashboard-automl-image-classification-fridge.yml deleted file mode 100644 index 816d4a496c..0000000000 --- a/.github/workflows/sdk-responsible-ai-vision-responsibleaidashboard-automl-image-classification-fridge.yml +++ /dev/null @@ -1,96 +0,0 @@ -# This code is autogenerated. -# Code is generated by running custom script: python3 readme.py -# Any manual changes to this file may cause incorrect behavior. -# Any manual changes will be overwritten if the code is regenerated. - -name: sdk-responsible-ai-vision-responsibleaidashboard-automl-image-classification-fridge -# This file is created by sdk/python/readme.py. -# Please do not edit directly. -on: - workflow_dispatch: - schedule: - - cron: "11 2/12 * * *" - pull_request: - branches: - - main - paths: - - sdk/python/responsible-ai/vision/** - - .github/workflows/sdk-responsible-ai-vision-responsibleaidashboard-automl-image-classification-fridge.yml - - sdk/python/dev-requirements.txt - - infra/bootstrapping/** - - sdk/python/setup.sh - -permissions: - id-token: write -concurrency: - group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }} - cancel-in-progress: true -jobs: - build: - runs-on: ubuntu-latest - steps: - - name: check out repo - uses: actions/checkout@v2 - - name: setup python - uses: actions/setup-python@v2 - with: - python-version: "3.10" - - name: pip install notebook reqs - run: pip install -r sdk/python/dev-requirements.txt - - name: pip install mlflow reqs - run: pip install -r sdk/python/mlflow-requirements.txt - - name: azure login - uses: azure/login@v1 - with: - client-id: ${{ secrets.OIDC_AZURE_CLIENT_ID }} - tenant-id: ${{ secrets.OIDC_AZURE_TENANT_ID }} - subscription-id: ${{ secrets.OIDC_AZURE_SUBSCRIPTION_ID }} - - name: bootstrap resources - run: | - echo '${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }}'; - bash bootstrap.sh - working-directory: infra/bootstrapping - continue-on-error: false - - name: setup SDK - run: | - source "${{ github.workspace }}/infra/bootstrapping/sdk_helpers.sh"; - source "${{ github.workspace }}/infra/bootstrapping/init_environment.sh"; - bash setup.sh - working-directory: sdk/python - continue-on-error: true - - name: validate readme - run: | - python check-readme.py "${{ github.workspace }}/sdk/python/responsible-ai/vision" - working-directory: infra/bootstrapping - continue-on-error: false - - name: setup-cli - run: | - source "${{ github.workspace }}/infra/bootstrapping/sdk_helpers.sh"; - source "${{ github.workspace }}/infra/bootstrapping/init_environment.sh"; - bash setup.sh - working-directory: cli - continue-on-error: true - - name: Eagerly cache access tokens for required scopes - run: | - # Workaround for azure-cli's lack of support for ID token refresh - # Taken from: https://github.com/Azure/login/issues/372#issuecomment-2056289617 - - # Management - az account get-access-token --scope https://management.azure.com/.default --output none - # ML - az account get-access-token --scope https://ml.azure.com/.default --output none - - name: run responsible-ai/vision/responsibleaidashboard-automl-image-classification-fridge.ipynb - run: | - source "${{ github.workspace }}/infra/bootstrapping/sdk_helpers.sh"; - source "${{ github.workspace }}/infra/bootstrapping/init_environment.sh"; - bash "${{ github.workspace }}/infra/bootstrapping/sdk_helpers.sh" generate_workspace_config "../../.azureml/config.json"; - bash "${{ github.workspace }}/infra/bootstrapping/sdk_helpers.sh" replace_template_values "responsibleaidashboard-automl-image-classification-fridge.ipynb"; - [ -f "../../.azureml/config" ] && cat "../../.azureml/config"; - papermill -k python responsibleaidashboard-automl-image-classification-fridge.ipynb responsibleaidashboard-automl-image-classification-fridge.output.ipynb - working-directory: sdk/python/responsible-ai/vision - - name: upload notebook's working folder as an artifact - if: ${{ always() }} - uses: ./.github/actions/upload-artifact - with: - name: responsibleaidashboard-automl-image-classification-fridge - path: sdk/python/responsible-ai/vision diff --git a/.github/workflows/sdk-responsible-ai-vision-responsibleaidashboard-automl-object-detection-fridge-private-data.yml b/.github/workflows/sdk-responsible-ai-vision-responsibleaidashboard-automl-object-detection-fridge-private-data.yml deleted file mode 100644 index 59c916113d..0000000000 --- a/.github/workflows/sdk-responsible-ai-vision-responsibleaidashboard-automl-object-detection-fridge-private-data.yml +++ /dev/null @@ -1,96 +0,0 @@ -# This code is autogenerated. -# Code is generated by running custom script: python3 readme.py -# Any manual changes to this file may cause incorrect behavior. -# Any manual changes will be overwritten if the code is regenerated. - -name: sdk-responsible-ai-vision-responsibleaidashboard-automl-object-detection-fridge-private-data -# This file is created by sdk/python/readme.py. -# Please do not edit directly. -on: - workflow_dispatch: - schedule: - - cron: "2 7/12 * * *" - pull_request: - branches: - - main - paths: - - sdk/python/responsible-ai/vision/** - - .github/workflows/sdk-responsible-ai-vision-responsibleaidashboard-automl-object-detection-fridge-private-data.yml - - sdk/python/dev-requirements.txt - - infra/bootstrapping/** - - sdk/python/setup.sh - -permissions: - id-token: write -concurrency: - group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }} - cancel-in-progress: true -jobs: - build: - runs-on: ubuntu-latest - steps: - - name: check out repo - uses: actions/checkout@v2 - - name: setup python - uses: actions/setup-python@v2 - with: - python-version: "3.10" - - name: pip install notebook reqs - run: pip install -r sdk/python/dev-requirements.txt - - name: pip install mlflow reqs - run: pip install -r sdk/python/mlflow-requirements.txt - - name: azure login - uses: azure/login@v1 - with: - client-id: ${{ secrets.OIDC_AZURE_CLIENT_ID }} - tenant-id: ${{ secrets.OIDC_AZURE_TENANT_ID }} - subscription-id: ${{ secrets.OIDC_AZURE_SUBSCRIPTION_ID }} - - name: bootstrap resources - run: | - echo '${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }}'; - bash bootstrap.sh - working-directory: infra/bootstrapping - continue-on-error: false - - name: setup SDK - run: | - source "${{ github.workspace }}/infra/bootstrapping/sdk_helpers.sh"; - source "${{ github.workspace }}/infra/bootstrapping/init_environment.sh"; - bash setup.sh - working-directory: sdk/python - continue-on-error: true - - name: validate readme - run: | - python check-readme.py "${{ github.workspace }}/sdk/python/responsible-ai/vision" - working-directory: infra/bootstrapping - continue-on-error: false - - name: setup-cli - run: | - source "${{ github.workspace }}/infra/bootstrapping/sdk_helpers.sh"; - source "${{ github.workspace }}/infra/bootstrapping/init_environment.sh"; - bash setup.sh - working-directory: cli - continue-on-error: true - - name: Eagerly cache access tokens for required scopes - run: | - # Workaround for azure-cli's lack of support for ID token refresh - # Taken from: https://github.com/Azure/login/issues/372#issuecomment-2056289617 - - # Management - az account get-access-token --scope https://management.azure.com/.default --output none - # ML - az account get-access-token --scope https://ml.azure.com/.default --output none - - name: run responsible-ai/vision/responsibleaidashboard-automl-object-detection-fridge-private-data.ipynb - run: | - source "${{ github.workspace }}/infra/bootstrapping/sdk_helpers.sh"; - source "${{ github.workspace }}/infra/bootstrapping/init_environment.sh"; - bash "${{ github.workspace }}/infra/bootstrapping/sdk_helpers.sh" generate_workspace_config "../../.azureml/config.json"; - bash "${{ github.workspace }}/infra/bootstrapping/sdk_helpers.sh" replace_template_values "responsibleaidashboard-automl-object-detection-fridge-private-data.ipynb"; - [ -f "../../.azureml/config" ] && cat "../../.azureml/config"; - papermill -k python responsibleaidashboard-automl-object-detection-fridge-private-data.ipynb responsibleaidashboard-automl-object-detection-fridge-private-data.output.ipynb - working-directory: sdk/python/responsible-ai/vision - - name: upload notebook's working folder as an artifact - if: ${{ always() }} - uses: ./.github/actions/upload-artifact - with: - name: responsibleaidashboard-automl-object-detection-fridge-private-data - path: sdk/python/responsible-ai/vision diff --git a/.github/workflows/sdk-responsible-ai-vision-responsibleaidashboard-image-classification-fridge.yml b/.github/workflows/sdk-responsible-ai-vision-responsibleaidashboard-image-classification-fridge.yml deleted file mode 100644 index 1cef156db1..0000000000 --- a/.github/workflows/sdk-responsible-ai-vision-responsibleaidashboard-image-classification-fridge.yml +++ /dev/null @@ -1,96 +0,0 @@ -# This code is autogenerated. -# Code is generated by running custom script: python3 readme.py -# Any manual changes to this file may cause incorrect behavior. -# Any manual changes will be overwritten if the code is regenerated. - -name: sdk-responsible-ai-vision-responsibleaidashboard-image-classification-fridge -# This file is created by sdk/python/readme.py. -# Please do not edit directly. -on: - workflow_dispatch: - schedule: - - cron: "23 9/12 * * *" - pull_request: - branches: - - main - paths: - - sdk/python/responsible-ai/vision/** - - .github/workflows/sdk-responsible-ai-vision-responsibleaidashboard-image-classification-fridge.yml - - sdk/python/dev-requirements.txt - - infra/bootstrapping/** - - sdk/python/setup.sh - -permissions: - id-token: write -concurrency: - group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }} - cancel-in-progress: true -jobs: - build: - runs-on: ubuntu-latest - steps: - - name: check out repo - uses: actions/checkout@v2 - - name: setup python - uses: actions/setup-python@v2 - with: - python-version: "3.10" - - name: pip install notebook reqs - run: pip install -r sdk/python/dev-requirements.txt - - name: pip install mlflow reqs - run: pip install -r sdk/python/mlflow-requirements.txt - - name: azure login - uses: azure/login@v1 - with: - client-id: ${{ secrets.OIDC_AZURE_CLIENT_ID }} - tenant-id: ${{ secrets.OIDC_AZURE_TENANT_ID }} - subscription-id: ${{ secrets.OIDC_AZURE_SUBSCRIPTION_ID }} - - name: bootstrap resources - run: | - echo '${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }}'; - bash bootstrap.sh - working-directory: infra/bootstrapping - continue-on-error: false - - name: setup SDK - run: | - source "${{ github.workspace }}/infra/bootstrapping/sdk_helpers.sh"; - source "${{ github.workspace }}/infra/bootstrapping/init_environment.sh"; - bash setup.sh - working-directory: sdk/python - continue-on-error: true - - name: validate readme - run: | - python check-readme.py "${{ github.workspace }}/sdk/python/responsible-ai/vision" - working-directory: infra/bootstrapping - continue-on-error: false - - name: setup-cli - run: | - source "${{ github.workspace }}/infra/bootstrapping/sdk_helpers.sh"; - source "${{ github.workspace }}/infra/bootstrapping/init_environment.sh"; - bash setup.sh - working-directory: cli - continue-on-error: true - - name: Eagerly cache access tokens for required scopes - run: | - # Workaround for azure-cli's lack of support for ID token refresh - # Taken from: https://github.com/Azure/login/issues/372#issuecomment-2056289617 - - # Management - az account get-access-token --scope https://management.azure.com/.default --output none - # ML - az account get-access-token --scope https://ml.azure.com/.default --output none - - name: run responsible-ai/vision/responsibleaidashboard-image-classification-fridge.ipynb - run: | - source "${{ github.workspace }}/infra/bootstrapping/sdk_helpers.sh"; - source "${{ github.workspace }}/infra/bootstrapping/init_environment.sh"; - bash "${{ github.workspace }}/infra/bootstrapping/sdk_helpers.sh" generate_workspace_config "../../.azureml/config.json"; - bash "${{ github.workspace }}/infra/bootstrapping/sdk_helpers.sh" replace_template_values "responsibleaidashboard-image-classification-fridge.ipynb"; - [ -f "../../.azureml/config" ] && cat "../../.azureml/config"; - papermill -k python responsibleaidashboard-image-classification-fridge.ipynb responsibleaidashboard-image-classification-fridge.output.ipynb - working-directory: sdk/python/responsible-ai/vision - - name: upload notebook's working folder as an artifact - if: ${{ always() }} - uses: ./.github/actions/upload-artifact - with: - name: responsibleaidashboard-image-classification-fridge - path: sdk/python/responsible-ai/vision diff --git a/.github/workflows/sdk-responsible-ai-vision-responsibleaidashboard-image-flower-classification.yml b/.github/workflows/sdk-responsible-ai-vision-responsibleaidashboard-image-flower-classification.yml deleted file mode 100644 index 6819d04642..0000000000 --- a/.github/workflows/sdk-responsible-ai-vision-responsibleaidashboard-image-flower-classification.yml +++ /dev/null @@ -1,90 +0,0 @@ -# This code is autogenerated. -# Code is generated by running custom script: python3 readme.py -# Any manual changes to this file may cause incorrect behavior. -# Any manual changes will be overwritten if the code is regenerated. - -name: sdk-responsible-ai-vision-responsibleaidashboard-image-flower-classification -# This file is created by sdk/python/readme.py. -# Please do not edit directly. -on: - workflow_dispatch: - schedule: - - cron: "44 1/12 * * *" - pull_request: - branches: - - main - paths: - - sdk/python/responsible-ai/vision/** - - .github/workflows/sdk-responsible-ai-vision-responsibleaidashboard-image-flower-classification.yml - - sdk/python/dev-requirements.txt - - infra/bootstrapping/** - - sdk/python/setup.sh -permissions: - id-token: write -concurrency: - group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }} - cancel-in-progress: true -jobs: - build: - runs-on: ubuntu-latest - steps: - - name: check out repo - uses: actions/checkout@v2 - - name: setup python - uses: actions/setup-python@v2 - with: - python-version: "3.10" - - name: pip install notebook reqs - run: pip install -r sdk/python/dev-requirements.txt - - name: pip install mlflow reqs - run: pip install -r sdk/python/mlflow-requirements.txt - - name: azure login - uses: azure/login@v1 - with: - client-id: ${{ secrets.OIDC_AZURE_CLIENT_ID }} - tenant-id: ${{ secrets.OIDC_AZURE_TENANT_ID }} - subscription-id: ${{ secrets.OIDC_AZURE_SUBSCRIPTION_ID }} - - name: bootstrap resources - run: | - echo '${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }}'; - bash bootstrap.sh - working-directory: infra/bootstrapping - continue-on-error: false - - name: setup SDK - run: | - source "${{ github.workspace }}/infra/bootstrapping/sdk_helpers.sh"; - source "${{ github.workspace }}/infra/bootstrapping/init_environment.sh"; - bash setup.sh - working-directory: sdk/python - continue-on-error: true - - name: setup-cli - run: | - source "${{ github.workspace }}/infra/bootstrapping/sdk_helpers.sh"; - source "${{ github.workspace }}/infra/bootstrapping/init_environment.sh"; - bash setup.sh - working-directory: cli - continue-on-error: true - - name: Eagerly cache access tokens for required scopes - run: | - # Workaround for azure-cli's lack of support for ID token refresh - # Taken from: https://github.com/Azure/login/issues/372#issuecomment-2056289617 - - # Management - az account get-access-token --scope https://management.azure.com/.default --output none - # ML - az account get-access-token --scope https://ml.azure.com/.default --output none - - name: run responsible-ai/vision/responsibleaidashboard-image-flower-classification.ipynb - run: | - source "${{ github.workspace }}/infra/bootstrapping/sdk_helpers.sh"; - source "${{ github.workspace }}/infra/bootstrapping/init_environment.sh"; - bash "${{ github.workspace }}/infra/bootstrapping/sdk_helpers.sh" generate_workspace_config "../../.azureml/config.json"; - bash "${{ github.workspace }}/infra/bootstrapping/sdk_helpers.sh" replace_template_values "responsibleaidashboard-image-flower-classification.ipynb"; - [ -f "../../.azureml/config" ] && cat "../../.azureml/config"; - papermill -k python responsibleaidashboard-image-flower-classification.ipynb responsibleaidashboard-image-flower-classification.output.ipynb - working-directory: sdk/python/responsible-ai/vision - - name: upload notebook's working folder as an artifact - if: ${{ always() }} - uses: ./.github/actions/upload-artifact - with: - name: responsibleaidashboard-image-flower-classification - path: sdk/python/responsible-ai/vision diff --git a/.github/workflows/sdk-responsible-ai-vision-responsibleaidashboard-image-multilabel-classification-fridge.yml b/.github/workflows/sdk-responsible-ai-vision-responsibleaidashboard-image-multilabel-classification-fridge.yml deleted file mode 100644 index 1e6056bb2b..0000000000 --- a/.github/workflows/sdk-responsible-ai-vision-responsibleaidashboard-image-multilabel-classification-fridge.yml +++ /dev/null @@ -1,96 +0,0 @@ -# This code is autogenerated. -# Code is generated by running custom script: python3 readme.py -# Any manual changes to this file may cause incorrect behavior. -# Any manual changes will be overwritten if the code is regenerated. - -name: sdk-responsible-ai-vision-responsibleaidashboard-image-multilabel-classification-fridge -# This file is created by sdk/python/readme.py. -# Please do not edit directly. -on: - workflow_dispatch: - schedule: - - cron: "52 1/12 * * *" - pull_request: - branches: - - main - paths: - - sdk/python/responsible-ai/vision/** - - .github/workflows/sdk-responsible-ai-vision-responsibleaidashboard-image-multilabel-classification-fridge.yml - - sdk/python/dev-requirements.txt - - infra/bootstrapping/** - - sdk/python/setup.sh - -permissions: - id-token: write -concurrency: - group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }} - cancel-in-progress: true -jobs: - build: - runs-on: ubuntu-latest - steps: - - name: check out repo - uses: actions/checkout@v2 - - name: setup python - uses: actions/setup-python@v2 - with: - python-version: "3.10" - - name: pip install notebook reqs - run: pip install -r sdk/python/dev-requirements.txt - - name: pip install mlflow reqs - run: pip install -r sdk/python/mlflow-requirements.txt - - name: azure login - uses: azure/login@v1 - with: - client-id: ${{ secrets.OIDC_AZURE_CLIENT_ID }} - tenant-id: ${{ secrets.OIDC_AZURE_TENANT_ID }} - subscription-id: ${{ secrets.OIDC_AZURE_SUBSCRIPTION_ID }} - - name: bootstrap resources - run: | - echo '${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }}'; - bash bootstrap.sh - working-directory: infra/bootstrapping - continue-on-error: false - - name: setup SDK - run: | - source "${{ github.workspace }}/infra/bootstrapping/sdk_helpers.sh"; - source "${{ github.workspace }}/infra/bootstrapping/init_environment.sh"; - bash setup.sh - working-directory: sdk/python - continue-on-error: true - - name: validate readme - run: | - python check-readme.py "${{ github.workspace }}/sdk/python/responsible-ai/vision" - working-directory: infra/bootstrapping - continue-on-error: false - - name: setup-cli - run: | - source "${{ github.workspace }}/infra/bootstrapping/sdk_helpers.sh"; - source "${{ github.workspace }}/infra/bootstrapping/init_environment.sh"; - bash setup.sh - working-directory: cli - continue-on-error: true - - name: Eagerly cache access tokens for required scopes - run: | - # Workaround for azure-cli's lack of support for ID token refresh - # Taken from: https://github.com/Azure/login/issues/372#issuecomment-2056289617 - - # Management - az account get-access-token --scope https://management.azure.com/.default --output none - # ML - az account get-access-token --scope https://ml.azure.com/.default --output none - - name: run responsible-ai/vision/responsibleaidashboard-image-multilabel-classification-fridge.ipynb - run: | - source "${{ github.workspace }}/infra/bootstrapping/sdk_helpers.sh"; - source "${{ github.workspace }}/infra/bootstrapping/init_environment.sh"; - bash "${{ github.workspace }}/infra/bootstrapping/sdk_helpers.sh" generate_workspace_config "../../.azureml/config.json"; - bash "${{ github.workspace }}/infra/bootstrapping/sdk_helpers.sh" replace_template_values "responsibleaidashboard-image-multilabel-classification-fridge.ipynb"; - [ -f "../../.azureml/config" ] && cat "../../.azureml/config"; - papermill -k python responsibleaidashboard-image-multilabel-classification-fridge.ipynb responsibleaidashboard-image-multilabel-classification-fridge.output.ipynb - working-directory: sdk/python/responsible-ai/vision - - name: upload notebook's working folder as an artifact - if: ${{ always() }} - uses: ./.github/actions/upload-artifact - with: - name: responsibleaidashboard-image-multilabel-classification-fridge - path: sdk/python/responsible-ai/vision diff --git a/.github/workflows/sdk-responsible-ai-vision-responsibleaidashboard-object-detection-MSCOCO.yml b/.github/workflows/sdk-responsible-ai-vision-responsibleaidashboard-object-detection-MSCOCO.yml deleted file mode 100644 index a41fc6cbeb..0000000000 --- a/.github/workflows/sdk-responsible-ai-vision-responsibleaidashboard-object-detection-MSCOCO.yml +++ /dev/null @@ -1,96 +0,0 @@ -# This code is autogenerated. -# Code is generated by running custom script: python3 readme.py -# Any manual changes to this file may cause incorrect behavior. -# Any manual changes will be overwritten if the code is regenerated. - -name: sdk-responsible-ai-vision-responsibleaidashboard-object-detection-MSCOCO -# This file is created by sdk/python/readme.py. -# Please do not edit directly. -on: - workflow_dispatch: - schedule: - - cron: "19 8/12 * * *" - pull_request: - branches: - - main - paths: - - sdk/python/responsible-ai/vision/** - - .github/workflows/sdk-responsible-ai-vision-responsibleaidashboard-object-detection-MSCOCO.yml - - sdk/python/dev-requirements.txt - - infra/bootstrapping/** - - sdk/python/setup.sh - -permissions: - id-token: write -concurrency: - group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }} - cancel-in-progress: true -jobs: - build: - runs-on: ubuntu-latest - steps: - - name: check out repo - uses: actions/checkout@v2 - - name: setup python - uses: actions/setup-python@v2 - with: - python-version: "3.10" - - name: pip install notebook reqs - run: pip install -r sdk/python/dev-requirements.txt - - name: pip install mlflow reqs - run: pip install -r sdk/python/mlflow-requirements.txt - - name: azure login - uses: azure/login@v1 - with: - client-id: ${{ secrets.OIDC_AZURE_CLIENT_ID }} - tenant-id: ${{ secrets.OIDC_AZURE_TENANT_ID }} - subscription-id: ${{ secrets.OIDC_AZURE_SUBSCRIPTION_ID }} - - name: bootstrap resources - run: | - echo '${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }}'; - bash bootstrap.sh - working-directory: infra/bootstrapping - continue-on-error: false - - name: setup SDK - run: | - source "${{ github.workspace }}/infra/bootstrapping/sdk_helpers.sh"; - source "${{ github.workspace }}/infra/bootstrapping/init_environment.sh"; - bash setup.sh - working-directory: sdk/python - continue-on-error: true - - name: validate readme - run: | - python check-readme.py "${{ github.workspace }}/sdk/python/responsible-ai/vision" - working-directory: infra/bootstrapping - continue-on-error: false - - name: setup-cli - run: | - source "${{ github.workspace }}/infra/bootstrapping/sdk_helpers.sh"; - source "${{ github.workspace }}/infra/bootstrapping/init_environment.sh"; - bash setup.sh - working-directory: cli - continue-on-error: true - - name: Eagerly cache access tokens for required scopes - run: | - # Workaround for azure-cli's lack of support for ID token refresh - # Taken from: https://github.com/Azure/login/issues/372#issuecomment-2056289617 - - # Management - az account get-access-token --scope https://management.azure.com/.default --output none - # ML - az account get-access-token --scope https://ml.azure.com/.default --output none - - name: run responsible-ai/vision/responsibleaidashboard-object-detection-MSCOCO.ipynb - run: | - source "${{ github.workspace }}/infra/bootstrapping/sdk_helpers.sh"; - source "${{ github.workspace }}/infra/bootstrapping/init_environment.sh"; - bash "${{ github.workspace }}/infra/bootstrapping/sdk_helpers.sh" generate_workspace_config "../../.azureml/config.json"; - bash "${{ github.workspace }}/infra/bootstrapping/sdk_helpers.sh" replace_template_values "responsibleaidashboard-object-detection-MSCOCO.ipynb"; - [ -f "../../.azureml/config" ] && cat "../../.azureml/config"; - papermill -k python responsibleaidashboard-object-detection-MSCOCO.ipynb responsibleaidashboard-object-detection-MSCOCO.output.ipynb - working-directory: sdk/python/responsible-ai/vision - - name: upload notebook's working folder as an artifact - if: ${{ always() }} - uses: ./.github/actions/upload-artifact - with: - name: responsibleaidashboard-object-detection-MSCOCO - path: sdk/python/responsible-ai/vision diff --git a/sdk/python/README.md b/sdk/python/README.md index 6f15a91faa..3d5fe6e8dc 100644 --- a/sdk/python/README.md +++ b/sdk/python/README.md @@ -301,12 +301,6 @@ Test Status is for branch - **_main_** |responsible-ai|text|[responsibleaidashboard-text-classification-blbooksgenre](responsible-ai/text/responsibleaidashboard-text-classification-blbooksgenre.ipynb)|*no description*|[![responsibleaidashboard-text-classification-blbooksgenre](https://github.com/Azure/azureml-examples/actions/workflows/sdk-responsible-ai-text-responsibleaidashboard-text-classification-blbooksgenre.yml/badge.svg?branch=main)](https://github.com/Azure/azureml-examples/actions/workflows/sdk-responsible-ai-text-responsibleaidashboard-text-classification-blbooksgenre.yml)| |responsible-ai|text|[responsibleaidashboard-text-classification-financial-news](responsible-ai/text/responsibleaidashboard-text-classification-financial-news/responsibleaidashboard-text-classification-financial-news.ipynb)|*no description*|[![responsibleaidashboard-text-classification-financial-news](https://github.com/Azure/azureml-examples/actions/workflows/sdk-responsible-ai-text-responsibleaidashboard-text-classification-financial-news-responsibleaidashboard-text-classification-financial-news.yml/badge.svg?branch=main)](https://github.com/Azure/azureml-examples/actions/workflows/sdk-responsible-ai-text-responsibleaidashboard-text-classification-financial-news-responsibleaidashboard-text-classification-financial-news.yml)| |responsible-ai|text|[responsibleaidashboard-text-question-answering-squad](responsible-ai/text/responsibleaidashboard-text-question-answering-squad.ipynb)|*no description*|[![responsibleaidashboard-text-question-answering-squad](https://github.com/Azure/azureml-examples/actions/workflows/sdk-responsible-ai-text-responsibleaidashboard-text-question-answering-squad.yml/badge.svg?branch=main)](https://github.com/Azure/azureml-examples/actions/workflows/sdk-responsible-ai-text-responsibleaidashboard-text-question-answering-squad.yml)| -|responsible-ai|vision|[responsibleaidashboard-automl-image-classification-fridge](responsible-ai/vision/responsibleaidashboard-automl-image-classification-fridge.ipynb)|*no description*|[![responsibleaidashboard-automl-image-classification-fridge](https://github.com/Azure/azureml-examples/actions/workflows/sdk-responsible-ai-vision-responsibleaidashboard-automl-image-classification-fridge.yml/badge.svg?branch=main)](https://github.com/Azure/azureml-examples/actions/workflows/sdk-responsible-ai-vision-responsibleaidashboard-automl-image-classification-fridge.yml)| -|responsible-ai|vision|[responsibleaidashboard-automl-object-detection-fridge-private-data](responsible-ai/vision/responsibleaidashboard-automl-object-detection-fridge-private-data.ipynb)|*no description*|[![responsibleaidashboard-automl-object-detection-fridge-private-data](https://github.com/Azure/azureml-examples/actions/workflows/sdk-responsible-ai-vision-responsibleaidashboard-automl-object-detection-fridge-private-data.yml/badge.svg?branch=main)](https://github.com/Azure/azureml-examples/actions/workflows/sdk-responsible-ai-vision-responsibleaidashboard-automl-object-detection-fridge-private-data.yml)| -|responsible-ai|vision|[responsibleaidashboard-image-classification-fridge](responsible-ai/vision/responsibleaidashboard-image-classification-fridge.ipynb)|*no description*|[![responsibleaidashboard-image-classification-fridge](https://github.com/Azure/azureml-examples/actions/workflows/sdk-responsible-ai-vision-responsibleaidashboard-image-classification-fridge.yml/badge.svg?branch=main)](https://github.com/Azure/azureml-examples/actions/workflows/sdk-responsible-ai-vision-responsibleaidashboard-image-classification-fridge.yml)| -|responsible-ai|vision|[responsibleaidashboard-image-flower-classification](responsible-ai/vision/responsibleaidashboard-image-flower-classification.ipynb)|*no description*|[![responsibleaidashboard-image-flower-classification](https://github.com/Azure/azureml-examples/actions/workflows/sdk-responsible-ai-vision-responsibleaidashboard-image-flower-classification.yml/badge.svg?branch=main)](https://github.com/Azure/azureml-examples/actions/workflows/sdk-responsible-ai-vision-responsibleaidashboard-image-flower-classification.yml)| -|responsible-ai|vision|[responsibleaidashboard-image-multilabel-classification-fridge](responsible-ai/vision/responsibleaidashboard-image-multilabel-classification-fridge.ipynb)|*no description*|[![responsibleaidashboard-image-multilabel-classification-fridge](https://github.com/Azure/azureml-examples/actions/workflows/sdk-responsible-ai-vision-responsibleaidashboard-image-multilabel-classification-fridge.yml/badge.svg?branch=main)](https://github.com/Azure/azureml-examples/actions/workflows/sdk-responsible-ai-vision-responsibleaidashboard-image-multilabel-classification-fridge.yml)| -|responsible-ai|vision|[responsibleaidashboard-object-detection-MSCOCO](responsible-ai/vision/responsibleaidashboard-object-detection-MSCOCO.ipynb)|*no description*|[![responsibleaidashboard-object-detection-MSCOCO](https://github.com/Azure/azureml-examples/actions/workflows/sdk-responsible-ai-vision-responsibleaidashboard-object-detection-MSCOCO.yml/badge.svg?branch=main)](https://github.com/Azure/azureml-examples/actions/workflows/sdk-responsible-ai-vision-responsibleaidashboard-object-detection-MSCOCO.yml)| |schedules|job-schedule.ipynb|[job-schedule](schedules/job-schedule.ipynb)|Create a component asset|[![job-schedule](https://github.com/Azure/azureml-examples/actions/workflows/sdk-schedules-job-schedule.yml/badge.svg?branch=main)](https://github.com/Azure/azureml-examples/actions/workflows/sdk-schedules-job-schedule.yml)| |using-mlflow|deploy|[mlflow_sdk_online_endpoints](using-mlflow/deploy/mlflow_sdk_online_endpoints.ipynb)|*no description* - _This sample is excluded from automated tests_|[![mlflow_sdk_online_endpoints](https://github.com/Azure/azureml-examples/actions/workflows/sdk-using-mlflow-deploy-mlflow_sdk_online_endpoints.yml/badge.svg?branch=main)](https://github.com/Azure/azureml-examples/actions/workflows/sdk-using-mlflow-deploy-mlflow_sdk_online_endpoints.yml)| |using-mlflow|deploy|[mlflow_sdk_online_endpoints_progresive](using-mlflow/deploy/mlflow_sdk_online_endpoints_progresive.ipynb)|*no description* - _This sample is excluded from automated tests_|[![mlflow_sdk_online_endpoints_progresive](https://github.com/Azure/azureml-examples/actions/workflows/sdk-using-mlflow-deploy-mlflow_sdk_online_endpoints_progresive.yml/badge.svg?branch=main)](https://github.com/Azure/azureml-examples/actions/workflows/sdk-using-mlflow-deploy-mlflow_sdk_online_endpoints_progresive.yml)| diff --git a/sdk/python/responsible-ai/README.md b/sdk/python/responsible-ai/README.md index 82fe2de40d..bcb91523d9 100644 --- a/sdk/python/responsible-ai/README.md +++ b/sdk/python/responsible-ai/README.md @@ -26,17 +26,11 @@ If not compatible, you can wrap your model's prediction function into a wrapper | Binary Text Classification | [blbooksgenre dataset](https://huggingface.co/datasets/blbooksgenre) | Text | Explanation, Error Analysis | [responsibleaidashboard-text-classification-blbooksgenre.ipynb](./responsibleaidashboard-text-classification-blbooksgenre.ipynb) | [Text Dashboard Generation](https://learn.microsoft.com/en-us/azure/machine-learning/how-to-responsible-ai-text-dashboard?view=azureml-api-2) | | Text Classification | [News Article Classification](./text/responsibleaidashboard-text-classification-financial-news/Text_classification_dataset.xlsx) | Text | Explanation, Error Analysis | [Financial_News_Text_classifier.ipynb](./text/responsibleaidashboard-text-classification-financial-news/responsibleaidashboard-text-classification-financial-news.ipynb) | [Documentation](./text/responsibleaidashboard-text-classification-financial-news/readme.md) | | Text Question Answering | [Stanford Question Answering Dataset (SQuAD)](https://huggingface.co/datasets/squad) | Text | Explanation, Error Analysis | [responsibleaidashboard-text-question-answering-squad.ipynb](./text/responsibleaidashboard-text-question-answering-squad.ipynb) | [Text Dashboard Generation](https://learn.microsoft.com/en-us/azure/machine-learning/how-to-responsible-ai-text-dashboard?view=azureml-api-2) | -| AutoML Image Classification | [Fridge Images](https://github.com/microsoft/computervision-recipes/tree/master/scenarios/classification) | Image | Explanation, Error Analysis | [responsibleaidashboard-automl-image-classification-fridge.ipynb](./vision/responsibleaidashboard-automl-image-classification-fridge.ipynb) | [Vision Dashboard Generation](https://learn.microsoft.com/en-us/azure/machine-learning/how-to-responsible-ai-image-dashboard?view=azureml-api-2) | -| Object Detection | [MIT Computer Vision datasets](https://github.com/microsoft/computervision-recipes) | Image | Explanation, Error Analysis | [responsibleaidashboard-automl-object-detection-fridge-private-data.ipynb](./vision/responsibleaidashboard-automl-object-detection-fridge-private-data.ipynb) | [Vision Dashboard Generation](https://learn.microsoft.com/en-us/azure/machine-learning/how-to-responsible-ai-image-dashboard?view=azureml-api-2) | -| Image Classification | [Fridge Images](https://github.com/microsoft/computervision-recipes/tree/master/scenarios/classification) | Image | Explanation, Error Analysis | [responsibleaidashboard-image-classification-fridge.ipynb](./vision/responsibleaidashboard-image-classification-fridge.ipynb) | [Vision Dashboard Generation](https://learn.microsoft.com/en-us/azure/machine-learning/how-to-responsible-ai-image-dashboard?view=azureml-api-2) | -| Multilabel Image Classification | [Fridge Images](https://github.com/microsoft/computervision-recipes/tree/master/scenarios/classification) | Image | Explanation, Error Analysis | [responsibleaidashboard-image-multilabel-classification-fridge.ipynb](./vision/responsibleaidashboard-image-multilabel-classification-fridge.ipynb) | [Vision Dashboard Generation](https://learn.microsoft.com/en-us/azure/machine-learning/how-to-responsible-ai-image-dashboard?view=azureml-api-2) | -| Image Object Detection | [Object Detection Fridge Images](https://github.com/microsoft/computervision-recipes/tree/master/scenarios/detection) | Image | Explanation, Error Analysis | [responsibleaidashboard-object-detection-MSCOCO.ipynb](./vision/responsibleaidashboard-object-detection-MSCOCO.ipynb) | [Vision Dashboard Generation](https://learn.microsoft.com/en-us/azure/machine-learning/how-to-responsible-ai-image-dashboard?view=azureml-api-2) | To learn more about the different types of Dashboard visit the below tutorials: 1) [Tabular Dashboard Generation](https://learn.microsoft.com/en-us/azure/machine-learning/how-to-responsible-ai-dashboard?view=azureml-api-2) 2) [Text Dashboard Generation](https://learn.microsoft.com/en-us/azure/machine-learning/how-to-responsible-ai-text-dashboard?view=azureml-api-2) -3) [Vision Dashboard Generation](https://learn.microsoft.com/en-us/azure/machine-learning/how-to-responsible-ai-image-dashboard?view=azureml-api-2) ## Supportability 🧰 diff --git a/sdk/python/responsible-ai/vision/README.md b/sdk/python/responsible-ai/vision/README.md deleted file mode 100644 index d464e3d9c6..0000000000 --- a/sdk/python/responsible-ai/vision/README.md +++ /dev/null @@ -1,54 +0,0 @@ ---- -page_type: sample -languages: -- python -products: -- azure-machine-learning -description: This sample shows how to create responsible ai dashboard for image datasets. ---- - -## Azure Machine Learning Responsible AI Dashboard and Scorecard - -### overview - -Read more about how to generate the Responsible AI (RAI) dashboard [here](https://learn.microsoft.com/en-us/azure/machine-learning/how-to-responsible-ai-dashboard-sdk-cli?tabs=yaml) and Responsible AI scorecard [here](https://learn.microsoft.com/en-us/azure/machine-learning/how-to-responsible-ai-scorecard). - -The Responsible AI components are supported for MLflow models with `scikit-learn` flavor that are trained on `pandas.DataFrame`. -The components accept both models and SciKit-Learn pipelines as input as long as the model or pipeline implements `predict` and `predict_proba` functions that conforms to the `scikit-learn` convention. -If not compatible, you can wrap your model's prediction function into a wrapper class that transforms the output into the format that is supported (`predict` and `predict_proba` of `scikit-learn`), and pass that wrapper class to modules in this repo. - -### objective -The main objective of this tutorial is to help users understand the process of creating responsible ai dashboard with explanations & error analysis for image dataset. - -### programming languages - - Python - -### directory 📖 - -| Scenario | Dataset | Data type | RAI component included | Link to sample | Documentation | -| --- | --- | --- | --- | --- | --- | - AutoML Image Classification | [Fridge Images](https://github.com/microsoft/computervision-recipes/tree/master/scenarios/classification) | Image | Explanation, Error Analysis | [responsibleaidashboard-automl-image-classification-fridge.ipynb](./vision/responsibleaidashboard-automl-image-classification-fridge.ipynb) | [Vision Dashboard Generation](https://learn.microsoft.com/en-us/azure/machine-learning/how-to-responsible-ai-image-dashboard?view=azureml-api-2) | -| Object Detection | [MIT Computer Vision datasets](https://github.com/microsoft/computervision-recipes) | Image | Explanation, Error Analysis | [responsibleaidashboard-automl-object-detection-fridge-private-data.ipynb](./vision/responsibleaidashboard-automl-object-detection-fridge-private-data.ipynb) | [Vision Dashboard Generation](https://learn.microsoft.com/en-us/azure/machine-learning/how-to-responsible-ai-image-dashboard?view=azureml-api-2) | -| Image Classification | [Fridge Images](https://github.com/microsoft/computervision-recipes/tree/master/scenarios/classification) | Image | Explanation, Error Analysis | [responsibleaidashboard-image-classification-fridge.ipynb](./vision/responsibleaidashboard-image-classification-fridge.ipynb) | [Vision Dashboard Generation](https://learn.microsoft.com/en-us/azure/machine-learning/how-to-responsible-ai-image-dashboard?view=azureml-api-2) | -| Multilabel Image Classification | [Fridge Images](https://github.com/microsoft/computervision-recipes/tree/master/scenarios/classification) | Image | Explanation, Error Analysis | [responsibleaidashboard-image-multilabel-classification-fridge.ipynb](./vision/responsibleaidashboard-image-multilabel-classification-fridge.ipynb) | [Vision Dashboard Generation](https://learn.microsoft.com/en-us/azure/machine-learning/how-to-responsible-ai-image-dashboard?view=azureml-api-2) | -| Image Object Detection | [Object Detection Fridge Images](https://github.com/microsoft/computervision-recipes/tree/master/scenarios/detection) | Image | Explanation, Error Analysis | [responsibleaidashboard-object-detection-MSCOCO.ipynb](./vision/responsibleaidashboard-object-detection-MSCOCO.ipynb) | [Vision Dashboard Generation](https://learn.microsoft.com/en-us/azure/machine-learning/how-to-responsible-ai-image-dashboard?view=azureml-api-2) | - - -To learn more about the different types of Dashboard visit the tutorial - -[Vision Dashboard Generation](https://learn.microsoft.com/en-us/azure/machine-learning/how-to-responsible-ai-image-dashboard?view=azureml-api-2) - -### estimated runtime - -### supportability 🧰 -Currently, we support datasets having numerical and categorical features. The following table provides the scenarios supported for each of the four responsible AI components: -> **Note**: Model overview (performance metrics and fairness disparity metrics) and Data explorer are generated for every Responsible AI dashboard by default and do not require a component to be configured. - -| RAI component | Binary classification | Multi-class classification | Multilabel classification | Regression | Timeseries forecasting | Categorical features | Text features | Image Features | Recommender Systems | Reinforcement Learning | -| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | -- | -| Explainability | Yes | Yes | No | Yes | No | Yes | Yes | Yes | No | No | -| Error Analysis | Yes | Yes | No | Yes | No | Yes | Yes | Yes | No | No | -| Causal Analysis | Yes | No | No | Yes | No | Yes (max 5 features due to computational cost) | No | No | No | No | -| Counterfactual | Yes | Yes | No | Yes | No | Yes | No | No | No | No | - -Read more about how to use the Responsible AI dashboards [here](https://learn.microsoft.com/en-us/azure/machine-learning/how-to-responsible-ai-dashboard). - diff --git a/sdk/python/responsible-ai/vision/responsibleaidashboard-automl-image-classification-fridge.ipynb b/sdk/python/responsible-ai/vision/responsibleaidashboard-automl-image-classification-fridge.ipynb deleted file mode 100644 index f5ed978800..0000000000 --- a/sdk/python/responsible-ai/vision/responsibleaidashboard-automl-image-classification-fridge.ipynb +++ /dev/null @@ -1,1295 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "98605bcd", - "metadata": {}, - "source": [ - "# AutoML Image Classification scenario with RAI Dashboard\n", - "\n", - "This example notebook demonstrates how to use an automl trained computer vision model on the dataset to evaluate the model in AzureML.\n", - "\n", - "First, we need to specify the version of the RAI components which are available in the workspace. This was specified when the components were uploaded." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "53b4eeac", - "metadata": {}, - "outputs": [], - "source": [ - "version_string = \"0.0.20\"" - ] - }, - { - "cell_type": "markdown", - "id": "06008690", - "metadata": {}, - "source": [ - "We can optionally provide the name of the compute cluster we want to use in AzureML. Later in this notebook, we will create it if it does not already exist as an example. AzureML can also run on serverless computes if a compute is not explicitly set. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f1ad79f9", - "metadata": {}, - "outputs": [], - "source": [ - "train_compute_name = \"gpu-cluster-nc6-v3\"\n", - "\n", - "rai_compute_name = \"cpucluster\"" - ] - }, - { - "cell_type": "markdown", - "id": "9fc65dc7", - "metadata": {}, - "source": [ - "Finally, we need to specify a version for the data and components we will create while running this notebook. This should be unique for the workspace, but the specific value doesn't matter:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "78053935", - "metadata": {}, - "outputs": [], - "source": [ - "rai_example_version_string = \"63\"" - ] - }, - { - "cell_type": "markdown", - "id": "f386a61d", - "metadata": {}, - "source": [ - "# 1. Connect to Azure Machine Learning Workspace\n", - "\n", - "The [workspace](https://docs.microsoft.com/en-us/azure/machine-learning/concept-workspace) is the top-level resource for Azure Machine Learning, providing a centralized place to work with all the artifacts you create when you use Azure Machine Learning. In this section we will connect to the workspace in which the job will be run." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1847bd7a", - "metadata": {}, - "outputs": [], - "source": [ - "from azure.ai.ml.entities import Data, Model\n", - "from azure.ai.ml.constants import AssetTypes\n", - "\n", - "from azure.ai.ml import MLClient\n", - "from azure.identity import DefaultAzureCredential\n", - "\n", - "from azure.ai.ml.automl import ClassificationPrimaryMetrics\n", - "from azure.ai.ml import automl, Input, dsl" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7d053cc6", - "metadata": {}, - "outputs": [], - "source": [ - "# Enter details of your AML workspace\n", - "subscription_id = \"\"\n", - "resource_group = \"\"\n", - "workspace = \"\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9d4b1c33", - "metadata": {}, - "outputs": [], - "source": [ - "# Handle to the workspace\n", - "from azure.ai.ml import MLClient\n", - "from azure.identity import DefaultAzureCredential\n", - "\n", - "try:\n", - " credential = DefaultAzureCredential()\n", - " ml_client = MLClient(\n", - " credential=credential,\n", - " subscription_id=subscription_id,\n", - " resource_group_name=resource_group,\n", - " workspace_name=workspace,\n", - " )\n", - "except Exception:\n", - " # If in compute instance we can get the config automatically\n", - " from azureml.core import Workspace\n", - "\n", - " workspace = Workspace.from_config()\n", - " workspace.write_config()\n", - " ml_client = MLClient.from_config(\n", - " credential=DefaultAzureCredential(exclude_shared_token_cache_credential=True),\n", - " logging_enable=True,\n", - " )\n", - "\n", - "print(ml_client)" - ] - }, - { - "cell_type": "markdown", - "id": "e83d5b18", - "metadata": {}, - "source": [ - "#### Compute target setup\n", - "\n", - "There are two ways to submit a job - through a compute or a serverless job.\n", - "\n", - "##### Serverless Job:\n", - "\n", - "In a serverless job, there is no need to create a compute explicitly.\n", - "Simply pass the desired instance type value to the `instance_type` parameter while creating a pipeline job.\n", - "This allows for quick and convenient job submission without the need for managing a compute cluster.\n", - "\n", - "##### Compute Job:\n", - "\n", - "The following code below demonstrates how to create a gpu compute cluster.\n", - "After creating the compute cluster, pass the name of the compute cluster to the `compute_name` parameter while submitting the pipeline job. This ensures that the job runs on the specified compute cluster, allowing for more control and customization.\n", - "\n", - "You will need to provide a [Compute Target](https://docs.microsoft.com/en-us/azure/machine-learning/concept-azure-machine-learning-architecture#computes) that will be used for your AutoML model training. AutoML models for image tasks require [GPU SKUs](https://docs.microsoft.com/en-us/azure/virtual-machines/sizes-gpu) such as the ones from the NCv3, ND, NDv2 and NCasT4 series. We recommend using the NCsv3-series (with v100 GPUs) for faster training. Using a compute target with a multi-GPU VM SKU will leverage the multiple GPUs to speed up training. Additionally, setting up a compute target with multiple nodes will allow for faster model training by leveraging parallelism, when tuning hyperparameters for your model.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "68b8e1e6", - "metadata": {}, - "outputs": [], - "source": [ - "from azure.ai.ml.entities import AmlCompute\n", - "\n", - "all_compute_names = [x.name for x in ml_client.compute.list()]\n", - "\n", - "if train_compute_name in all_compute_names:\n", - " print(f\"Found existing compute: {train_compute_name}\")\n", - "else:\n", - " train_compute_config = AmlCompute(\n", - " name=train_compute_name,\n", - " type=\"amlcompute\",\n", - " size=\"Standard_NC6s_v3\",\n", - " min_instances=0,\n", - " max_instances=4,\n", - " idle_time_before_scale_down=120,\n", - " )\n", - " ml_client.compute.begin_create_or_update(train_compute_config).result()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ee6fa857", - "metadata": {}, - "outputs": [], - "source": [ - "from azure.ai.ml.entities import AmlCompute\n", - "\n", - "all_compute_names = [x.name for x in ml_client.compute.list()]\n", - "\n", - "if rai_compute_name in all_compute_names:\n", - " print(f\"Found existing compute: {rai_compute_name}\")\n", - "else:\n", - " rai_compute_config = AmlCompute(\n", - " name=rai_compute_name,\n", - " size=\"STANDARD_DS3_V2\",\n", - " min_instances=0,\n", - " max_instances=4,\n", - " idle_time_before_scale_down=3600,\n", - " )\n", - " ml_client.compute.begin_create_or_update(rai_compute_config)" - ] - }, - { - "cell_type": "markdown", - "id": "73be2b63", - "metadata": {}, - "source": [ - "# 2. Accessing the Data\n", - "\n", - "We supply the data as a pair of parquet files and accompanying `MLTable` file. We can download them, preprocess them, and take a brief look. \n", - "\n", - "The [fridge dataset](https://github.com/microsoft/computervision-recipes/tree/master/scenarios/classification) classifies images into four types of items commonly found in the Microsoft New England R&D office refrigerator - carton, water bottle, can and milk bottle. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5f875f18", - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import pandas as pd\n", - "\n", - "try:\n", - " from urllib import urlretrieve\n", - "except ImportError:\n", - " from urllib.request import urlretrieve" - ] - }, - { - "cell_type": "markdown", - "id": "8f9ce4ae", - "metadata": {}, - "source": [ - "## 2.1 Download Data\n", - "\n", - "Load the 'fridge items' dataset from a JSON file and MLTable definition.\n", - "\n", - "In this notebook, we use a toy dataset called Fridge Objects, which consists of 134 images of 4 classes of beverage container {can, carton, milk bottle, water bottle} photos taken on different backgrounds.\n", - "\n", - "All images in this notebook are hosted in [this repository](https://github.com/microsoft/computervision-recipes) and are made available under the [MIT license](https://github.com/microsoft/computervision-recipes/blob/master/LICENSE)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0eac648c", - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import urllib\n", - "from zipfile import ZipFile\n", - "\n", - "# Change to a different location if you prefer\n", - "dataset_parent_dir = \"./data\"\n", - "\n", - "# create data folder if it doesnt exist.\n", - "os.makedirs(dataset_parent_dir, exist_ok=True)\n", - "\n", - "# download data\n", - "download_url = (\n", - " \"https://publictestdatasets.blob.core.windows.net/computervision/fridgeObjects.zip\"\n", - ")\n", - "\n", - "# Extract current dataset name from dataset url\n", - "dataset_name = os.path.split(download_url)[-1].split(\".\")[0]\n", - "# Get dataset path for later use\n", - "dataset_dir = os.path.join(dataset_parent_dir, dataset_name)\n", - "\n", - "# Get the data zip file path\n", - "data_file = os.path.join(dataset_parent_dir, f\"{dataset_name}.zip\")\n", - "\n", - "# Download the dataset\n", - "urllib.request.urlretrieve(download_url, filename=data_file)\n", - "\n", - "# extract files\n", - "with ZipFile(data_file, \"r\") as zip:\n", - " print(\"extracting files...\")\n", - " zip.extractall(path=dataset_parent_dir)\n", - " print(\"done\")\n", - "# delete zip file\n", - "os.remove(data_file)" - ] - }, - { - "cell_type": "markdown", - "id": "65d0ee08", - "metadata": {}, - "source": [ - "## 2.2. Upload the images to Datastore through an AML Data asset (URI Folder) for training an AutomatedML Model\n", - "\n", - "In order to use the data for training in Azure ML, we upload it to our default Azure Blob Storage of our Azure ML Workspace.\n", - "\n", - "Reference to URI FOLDER data asset example for further details: https://github.com/Azure/azureml-examples/blob/samuel100/data-samples/sdk/assets/data/data.ipynb" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ba8507e5", - "metadata": {}, - "outputs": [], - "source": [ - "from azure.ai.ml.entities import Data\n", - "from azure.ai.ml.constants import AssetTypes\n", - "\n", - "input_test_data = \"fridge-items-images\"\n", - "\n", - "try:\n", - " uri_folder_data_asset = ml_client.data.get(\n", - " name=input_test_data, version=rai_example_version_string\n", - " )\n", - "except Exception:\n", - " my_data = Data(\n", - " path=dataset_dir,\n", - " type=AssetTypes.URI_FOLDER,\n", - " description=\"Fridge-items images\",\n", - " name=input_test_data,\n", - " version=rai_example_version_string,\n", - " )\n", - " uri_folder_data_asset = ml_client.data.create_or_update(my_data)\n", - "print(uri_folder_data_asset)\n", - "print(\"\")\n", - "print(\"Path to folder in Blob Storage:\")\n", - "print(uri_folder_data_asset.path)" - ] - }, - { - "cell_type": "markdown", - "id": "4ade415d", - "metadata": {}, - "source": [ - "## 2.3. Convert the downloaded data to JSONL\n", - "\n", - "In this example, the fridge object dataset is stored in a directory. There are four different folders inside:\n", - "\n", - "- /water_bottle\n", - "- /milk_bottle\n", - "- /carton\n", - "- /can\n", - "\n", - "This is the most common data format for multiclass image classification. Each folder title corresponds to the image label for the images contained inside. In order to use this data to create an AzureML MLTable, we first need to convert it to the required JSONL format. Please refer to the [documentation on how to prepare datasets](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-prepare-datasets-for-automl-images).\n", - "\n", - "\n", - "The following script is creating two .jsonl files (one for training and one for validation) in the corresponding MLTable folder. The train / validation ratio corresponds to 20% of the data going into the validation file." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b3eef3b6", - "metadata": {}, - "outputs": [], - "source": [ - "import json\n", - "import os\n", - "\n", - "\n", - "# We'll copy each JSONL file within its related MLTable folder\n", - "training_mltable_path = os.path.join(dataset_parent_dir, \"training-mltable-folder\")\n", - "validation_mltable_path = os.path.join(dataset_parent_dir, \"validation-mltable-folder\")\n", - "\n", - "# First, let's create the folders if they don't exist\n", - "os.makedirs(training_mltable_path, exist_ok=True)\n", - "os.makedirs(validation_mltable_path, exist_ok=True)\n", - "\n", - "train_validation_ratio = 5\n", - "\n", - "# Path to the training and validation files\n", - "train_annotations_file = os.path.join(training_mltable_path, \"train_annotations.jsonl\")\n", - "validation_annotations_file = os.path.join(\n", - " validation_mltable_path, \"validation_annotations.jsonl\"\n", - ")\n", - "\n", - "# Baseline of json line dictionary\n", - "json_line_sample = {\n", - " \"image_url\": uri_folder_data_asset.path,\n", - " \"label\": \"\",\n", - "}\n", - "\n", - "index = 0\n", - "# Scan each sub directary and generate a jsonl line per image, distributed on train and valid JSONL files\n", - "with open(train_annotations_file, \"w\") as train_f:\n", - " with open(validation_annotations_file, \"w\") as validation_f:\n", - " for class_name in os.listdir(dataset_dir):\n", - " sub_dir = os.path.join(dataset_dir, class_name)\n", - " if not os.path.isdir(sub_dir):\n", - " continue\n", - "\n", - " # Scan each sub directary\n", - " print(f\"Parsing {sub_dir}\")\n", - " for image in os.listdir(sub_dir):\n", - " json_line = dict(json_line_sample)\n", - " json_line[\"image_url\"] += f\"{class_name}/{image}\"\n", - " json_line[\"label\"] = class_name\n", - "\n", - " if index % train_validation_ratio == 0:\n", - " # validation annotation\n", - " validation_f.write(json.dumps(json_line) + \"\\n\")\n", - " else:\n", - " # train annotation\n", - " train_f.write(json.dumps(json_line) + \"\\n\")\n", - " index += 1" - ] - }, - { - "cell_type": "markdown", - "id": "17d53df4", - "metadata": {}, - "source": [ - "## 2.4 Create MLTable data input for training an AutomatedML Model\n", - "\n", - "Create MLTable data input using the jsonl files created above.\n", - "\n", - "For documentation on creating your own MLTable assets for jobs beyond this notebook, please refer to below resources\n", - "- [MLTable YAML Schema](https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-mltable) - covers how to write MLTable YAML, which is required for each MLTable asset.\n", - "- [Create MLTable data asset](https://learn.microsoft.com/en-us/azure/machine-learning/how-to-create-data-assets?tabs=Python-SDK#create-a-mltable-data-asset) - covers how to create mltable data asset. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f39d2ea8", - "metadata": {}, - "outputs": [], - "source": [ - "def create_ml_table_file(filename):\n", - " return (\n", - " \"$schema: https://azureml/sdk-2-0/MLTable.json\\n\"\n", - " \"type: mltable\\n\"\n", - " \"paths:\\n\"\n", - " \" - file: ./{0}\\n\"\n", - " \"transformations:\\n\"\n", - " \" - read_json_lines:\\n\"\n", - " \" encoding: utf8\\n\"\n", - " \" invalid_lines: error\\n\"\n", - " \" include_path_column: false\\n\"\n", - " \" - convert_column_types:\\n\"\n", - " \" - columns: image_url\\n\"\n", - " \" column_type: stream_info\"\n", - " ).format(filename)\n", - "\n", - "\n", - "def save_ml_table_file(output_path, mltable_file_contents):\n", - " with open(os.path.join(output_path, \"MLTable\"), \"w\") as f:\n", - " f.write(mltable_file_contents)\n", - "\n", - "\n", - "# Create and save train mltable\n", - "train_mltable_file_contents = create_ml_table_file(\n", - " os.path.basename(train_annotations_file)\n", - ")\n", - "save_ml_table_file(training_mltable_path, train_mltable_file_contents)\n", - "\n", - "# Create and save validation mltable\n", - "validation_mltable_file_contents = create_ml_table_file(\n", - " os.path.basename(validation_annotations_file)\n", - ")\n", - "save_ml_table_file(validation_mltable_path, validation_mltable_file_contents)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9d251a68", - "metadata": {}, - "outputs": [], - "source": [ - "# Training MLTable defined locally, with local data to be uploaded\n", - "my_training_data_input = Input(type=AssetTypes.MLTABLE, path=training_mltable_path)\n", - "\n", - "# Validation MLTable defined locally, with local data to be uploaded\n", - "my_validation_data_input = Input(type=AssetTypes.MLTABLE, path=validation_mltable_path)\n", - "\n", - "# WITH REMOTE PATH: If available already in the cloud/workspace-blob-store\n", - "# my_training_data_input = Input(type=AssetTypes.MLTABLE, path=\"azureml://datastores/workspaceblobstore/paths/vision-classification/train\")\n", - "# my_validation_data_input = Input(type=AssetTypes.MLTABLE, path=\"azureml://datastores/workspaceblobstore/paths/vision-classification/valid\")" - ] - }, - { - "cell_type": "markdown", - "id": "1115ac59", - "metadata": {}, - "source": [ - "The label column contains the classes:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5b42df3d", - "metadata": {}, - "outputs": [], - "source": [ - "target_column_name = \"label\"" - ] - }, - { - "cell_type": "markdown", - "id": "055288ec", - "metadata": {}, - "source": [ - "# 3. Configure and run the AutoML for Images Classification-MultiClass training job\n", - "\n", - "Here, we are using Automatic hyperparameter sweeping for your models (AutoMode). For details on individual runs or manual hyper parameter sweep, refer to [automl-image-classification-multiclass-task-fridge-items.ipynb notebook](https://github.com/Azure/azureml-examples/blob/main/sdk/python/jobs/automl-standalone-jobs/automl-image-classification-multiclass-task-fridge-items/automl-image-classification-multiclass-task-fridge-items.ipynb).\n", - "\n", - "When using AutoML for Images, we can perform an automatic hyperparameter sweep to find the optimal model (we call this functionality AutoMode). The system will choose a model architecture and values for the learning_rate, number_of_epochs, training_batch_size, etc. based on the number of runs. There is no need to specify the hyperparameter search space, sampling method or early termination policy. A number of runs between 10 and 20 will likely work well on many datasets.\n", - "\n", - "AutoMode is triggered by setting `max_trials` to a value greater than 1 in limits and by omitting the hyperparameter space, sampling method and termination policy.\n", - "\n", - "The following functions configure AutoML image jobs for automatic sweeps:\n", - "### image_classification() function parameters:\n", - "The `image_classification()` factory function allows user to configure the training job.\n", - "\n", - "- `compute` - The compute on which the AutoML job will run. In this example we are using a compute called 'gpu-cluster' present in the workspace. You can replace it any other compute in the workspace.\n", - "- `experiment_name` - The name of the experiment. An experiment is like a folder with multiple runs in Azure ML Workspace that should be related to the same logical machine learning experiment.\n", - "- `name` - The name of the Job/Run. This is an optional property. If not specified, a random name will be generated.\n", - "- `primary_metric` - The metric that AutoML will optimize for model selection.\n", - "- `target_column_name` - The name of the column to target for predictions. It must always be specified. This parameter is applicable to 'training_data' and 'validation_data'.\n", - "- `training_data` - The data to be used for training. It should contain both training feature columns and a target column. Optionally, this data can be split for segregating a validation or test dataset. \n", - "You can use a registered MLTable in the workspace using the format ':' OR you can use a local file or folder as a MLTable. For e.g Input(mltable='my_mltable:1') OR Input(mltable=MLTable(local_path=\"./data\"))\n", - "The parameter `training_data` must always be provided.\n", - "\n", - "### set_limits() function parameters:\n", - "This is an optional configuration method to configure limits parameters such as timeouts.\n", - "\n", - "- `max_trials` - Parameter for maximum number of configurations to sweep. Must be an integer between 1 and 1000. When exploring just the default hyperparameters for a given model algorithm, set this parameter to 1. Default value is 1.\n", - "- `max_concurrent_trials` - Maximum number of runs that can run concurrently. If not specified, all runs launch in parallel. If specified, must be an integer between 1 and 100. Default value is 1.\n", - " NOTE: The number of concurrent runs is gated on the resources available in the specified compute target. Ensure that the compute target has the available resources for the desired concurrency.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1733179c", - "metadata": {}, - "outputs": [], - "source": [ - "# set up experiment name\n", - "exp_name = \"dpv2-image-classification-experiment\"" - ] - }, - { - "cell_type": "markdown", - "id": "005a1098", - "metadata": {}, - "source": [ - "This pipeline uses serverless compute. To use the compute you created above, uncomment the compute parameter line." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9427a2e5", - "metadata": {}, - "outputs": [], - "source": [ - "# Create the AutoML job with the related factory-function.\n", - "\n", - "import random\n", - "import string\n", - "\n", - "allowed_chars = string.ascii_lowercase + string.digits\n", - "suffix = \"\".join(random.choice(allowed_chars) for x in range(5))\n", - "job_name = \"dpv2-image-classification-job-02\" + suffix\n", - "\n", - "image_classification_job = automl.image_classification(\n", - " # compute=train_compute_name,\n", - " name=job_name,\n", - " experiment_name=exp_name,\n", - " training_data=my_training_data_input,\n", - " validation_data=my_validation_data_input,\n", - " target_column_name=\"label\",\n", - " primary_metric=ClassificationPrimaryMetrics.ACCURACY,\n", - " tags={\"my_custom_tag\": \"My custom value\"},\n", - ")\n", - "\n", - "image_classification_job.set_limits(\n", - " max_trials=10,\n", - " max_concurrent_trials=2,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8f0735dd", - "metadata": {}, - "outputs": [], - "source": [ - "# Submit the AutoML job\n", - "returned_job = ml_client.jobs.create_or_update(\n", - " image_classification_job\n", - ") # submit the job to the backend\n", - "\n", - "print(f\"Created job: {returned_job}\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f2f41ca4", - "metadata": {}, - "outputs": [], - "source": [ - "ml_client.jobs.stream(returned_job.name)" - ] - }, - { - "cell_type": "markdown", - "id": "3220d145", - "metadata": {}, - "source": [ - "# 4. Retrieve the Best Trial (Best Model's trial/run) and Register the Best Model\n", - "Use the MLFLowClient to access the results (such as Models, Artifacts, Metrics) of a previously completed AutoML Trial." - ] - }, - { - "cell_type": "markdown", - "id": "3ff0484c", - "metadata": {}, - "source": [ - "### 4.1 Initialize MLFlow Client\n", - "\n", - "The models and artifacts that are produced by AutoML can be accessed via the MLFlow interface.\n", - "Initialize the MLFlow client here, and set the backend as Azure ML, via. the MLFlow Client.\n", - "\n", - "IMPORTANT, you need to have installed the latest MLFlow packages with:\n", - "\n", - " pip install azureml-mlflow\n", - "\n", - " pip install mlflow" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e3029324", - "metadata": {}, - "outputs": [], - "source": [ - "import mlflow\n", - "\n", - "# Obtain the tracking URL from MLClient\n", - "MLFLOW_TRACKING_URI = ml_client.workspaces.get(\n", - " name=ml_client.workspace_name\n", - ").mlflow_tracking_uri\n", - "\n", - "print(MLFLOW_TRACKING_URI)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "868dd2ac", - "metadata": {}, - "outputs": [], - "source": [ - "# Set the MLFLOW TRACKING URI\n", - "mlflow.set_tracking_uri(MLFLOW_TRACKING_URI)\n", - "print(f\"\\nCurrent tracking uri: {mlflow.get_tracking_uri()}\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "320db110", - "metadata": {}, - "outputs": [], - "source": [ - "from mlflow.tracking.client import MlflowClient\n", - "\n", - "# Initialize MLFlow client\n", - "mlflow_client = MlflowClient()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a7f791cb", - "metadata": {}, - "outputs": [], - "source": [ - "job_name = returned_job.name\n", - "\n", - "# # Example if providing an specific Job name/ID\n", - "# job_name = \"happy_yam_40fq53m7c2\" #\"ashy_net_gdd31zf2fq\"\n", - "\n", - "# Get the parent run\n", - "mlflow_parent_run = mlflow_client.get_run(job_name)\n", - "\n", - "print(\"Parent Run: \")\n", - "print(mlflow_parent_run)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7914c06e", - "metadata": {}, - "outputs": [], - "source": [ - "# Print parent run tags. 'automl_best_child_run_id' tag should be there.\n", - "print(mlflow_parent_run.data.tags.keys())" - ] - }, - { - "cell_type": "markdown", - "id": "e930dd0c", - "metadata": {}, - "source": [ - "### 4.2 Get the AutoML best child run" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6937627a", - "metadata": {}, - "outputs": [], - "source": [ - "# Get the best model's child run\n", - "\n", - "best_child_run_id = mlflow_parent_run.data.tags[\"automl_best_child_run_id\"]\n", - "print(f\"Found best child run id: {best_child_run_id}\")\n", - "\n", - "best_run = mlflow_client.get_run(best_child_run_id)\n", - "\n", - "print(\"Best child run: \")\n", - "print(best_run)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7afd65b8", - "metadata": {}, - "outputs": [], - "source": [ - "import json\n", - "\n", - "hyperparameter_tag_dict = json.loads(best_run.data.tags[\"hyperparameters\"])\n", - "print(hyperparameter_tag_dict)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8886474a", - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "\n", - "# Access the results (such as Models, Artifacts, Metrics) of a previously completed AutoML Run.\n", - "pd.DataFrame(best_run.data.metrics, index=[0]).T" - ] - }, - { - "cell_type": "markdown", - "id": "024e5458", - "metadata": {}, - "source": [ - "### 4.3 Download the best model locally\n", - "Access the results (such as Models, Artifacts, Metrics) of a previously completed AutoML Run." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "315bfb05", - "metadata": {}, - "outputs": [], - "source": [ - "# Create local folder\n", - "import os\n", - "\n", - "local_dir = \"./artifact_downloads\"\n", - "if not os.path.exists(local_dir):\n", - " os.mkdir(local_dir)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2da37b17", - "metadata": {}, - "outputs": [], - "source": [ - "# Download run's artifacts/outputs\n", - "local_path = mlflow_client.download_artifacts(\n", - " best_run.info.run_id, \"outputs\", local_dir\n", - ")\n", - "print(f\"Artifacts downloaded in: {local_path}\")\n", - "print(f\"Artifacts: {os.listdir(local_path)}\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "41956fba", - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "\n", - "mlflow_model_dir = os.path.join(local_dir, \"outputs\", \"mlflow-model\")\n", - "\n", - "# Show the contents of the MLFlow model folder\n", - "os.listdir(mlflow_model_dir)\n", - "\n", - "# You should see a list of files such as the following:\n", - "# ['artifacts', 'conda.yaml', 'MLmodel', 'python_env.yaml', 'python_model.pkl', 'requirements.txt']" - ] - }, - { - "cell_type": "markdown", - "id": "940b5239", - "metadata": {}, - "source": [ - "### 4.4 Register model" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "bf1c5a6b", - "metadata": {}, - "outputs": [], - "source": [ - "model_name = \"ic-mc-rai-fridge-items-model\" + suffix\n", - "model = Model(\n", - " path=f\"azureml://jobs/{best_run.info.run_id}/outputs/artifacts/outputs/mlflow-model/\",\n", - " name=model_name,\n", - " description=\"my sample image classification multiclass model\",\n", - " type=AssetTypes.MLFLOW_MODEL,\n", - ")\n", - "\n", - "# for downloaded file\n", - "# model = Model(\n", - "# path=mlflow_model_dir,\n", - "# name=model_name,\n", - "# description=\"my sample image classification multiclass model\",\n", - "# type=AssetTypes.MLFLOW_MODEL,\n", - "# )\n", - "\n", - "registered_model = ml_client.models.create_or_update(model)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5e9f147e", - "metadata": {}, - "outputs": [], - "source": [ - "registered_model.id" - ] - }, - { - "cell_type": "markdown", - "id": "6d165e2b", - "metadata": {}, - "source": [ - "We need a compute target on which to run our jobs. The following checks whether the compute specified above is present; if not, then the compute target is created." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d64dff2b", - "metadata": {}, - "outputs": [], - "source": [ - "print(registered_model.name, registered_model.version)" - ] - }, - { - "cell_type": "markdown", - "id": "0722395e", - "metadata": {}, - "source": [ - "# 5. Creating the RAI Vision Insights\n", - "\n", - "Now that we have our model, we can generate RAI Vision insights for it. We will need the `id` of the registered model, which will be as follows:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7d3e6e6e", - "metadata": {}, - "outputs": [], - "source": [ - "expected_model_id = f\"{registered_model.name}:{registered_model.version}\"\n", - "azureml_model_id = f\"azureml:{expected_model_id}\"" - ] - }, - { - "cell_type": "markdown", - "id": "310aa659", - "metadata": {}, - "source": [ - "Next, we load the RAI components, so that we can construct a pipeline:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d67b942e", - "metadata": {}, - "outputs": [], - "source": [ - "registry_name = \"azureml\"\n", - "credential = DefaultAzureCredential()\n", - "\n", - "ml_client_registry = MLClient(\n", - " credential=credential,\n", - " subscription_id=ml_client.subscription_id,\n", - " resource_group_name=ml_client.resource_group_name,\n", - " # workspace_name=ml_client.workspace_name,\n", - " registry_name=registry_name,\n", - ")\n", - "\n", - "rai_vision_insights_component = ml_client_registry.components.get(\n", - " name=\"rai_vision_insights\", label=\"latest\"\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "c98cd2d9", - "metadata": {}, - "source": [ - "## 5.1 Constructing the pipeline in sdk\n", - "We can now specify our pipeline. Complex objects (such as lists of column names) have to be converted to JSON strings before being passed to the components.\n", - "\n", - "\n", - "Note:\n", - "1. guided_gradcam doesn't work with transformer vision models\n", - "2. shap isn't supported for automl images models\n", - "\n", - "For more details on XAI parameters, refer to this [page](https://learn.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models?tabs=cli#generate-explanations-for-predictions)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5d4c5514", - "metadata": {}, - "outputs": [], - "source": [ - "# Prepare XAI parameters\n", - "xai_algorithm = (\n", - " \"guided_gradcam\" # xrai, integrated_gradients, guided_gradcam, guided_backprop\n", - ")\n", - "n_steps = 50 # applicable for xrai, integrated_gradients\n", - "xrai_fast = True # applicable for xrai\n", - "approximation_method = \"gausslegendre\" # applicable integrated_gradients\n", - "confidence_score_threshold_multilabel = 0.5 # applicable for multilabel classification\n", - "\n", - "# Note: latest automl wraps model_name property in model\n", - "if \"model\" in hyperparameter_tag_dict:\n", - " hyperparameter_tag_dict = hyperparameter_tag_dict[\"model\"]\n", - "\n", - "if \"model_name\" in hyperparameter_tag_dict:\n", - " model_name = hyperparameter_tag_dict[\"model_name\"]\n", - " if \"vit\" in model_name:\n", - " # guided_gradcam doesn't work with transformer vision models\n", - " # override defaults\n", - " xai_algorithm = \"xrai\" # xrai, integrated_gradients, guided_backprop\n", - " n_steps = 50 # applicable for xrai, integrated_gradients\n", - " xrai_fast = True # applicable for xrai\n", - " approximation_method = \"gausslegendre\" # applicable integrated_gradients\n", - " confidence_score_threshold_multilabel = (\n", - " 0.5 # applicable for multilabel classification\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a62105a7", - "metadata": {}, - "outputs": [], - "source": [ - "import json\n", - "from azure.ai.ml import Input\n", - "from azure.ai.ml.constants import AssetTypes\n", - "\n", - "\n", - "@dsl.pipeline(\n", - " compute=rai_compute_name,\n", - " description=\"Example RAI computation on Fridge data\",\n", - " experiment_name=f\"RAI_Fridge_Example_RAIInsights_Computation\",\n", - ")\n", - "def rai_fridge_image_classification_pipeline(target_column_name, test_data, classes):\n", - " # Initiate the RAIInsights\n", - " rai_image_job = rai_vision_insights_component(\n", - " model_input=Input(type=AssetTypes.MLFLOW_MODEL, path=azureml_model_id),\n", - " test_dataset=test_data,\n", - " task_type=\"image_classification\",\n", - " model_info=expected_model_id,\n", - " target_column_name=target_column_name,\n", - " classes=classes,\n", - " dataset_type=\"private\",\n", - " model_type=\"pyfunc\",\n", - " precompute_explanation=True,\n", - " enable_error_analysis=True,\n", - " xai_algorithm=xai_algorithm,\n", - " n_steps=n_steps,\n", - " xrai_fast=xrai_fast,\n", - " approximation_method=approximation_method,\n", - " confidence_score_threshold_multilabel=confidence_score_threshold_multilabel,\n", - " )\n", - " rai_image_job.set_limits(timeout=7200)\n", - "\n", - " rai_image_job.outputs.dashboard.mode = \"upload\"\n", - " rai_image_job.outputs.ux_json.mode = \"upload\"\n", - "\n", - " return {\n", - " \"dashboard\": rai_image_job.outputs.dashboard,\n", - " \"ux_json\": rai_image_job.outputs.ux_json,\n", - " }" - ] - }, - { - "cell_type": "markdown", - "id": "6b5b14a9", - "metadata": {}, - "source": [ - "Next, we define the pipeline object itself, and ensure that the outputs will be available for download:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e4d86ec2", - "metadata": {}, - "outputs": [], - "source": [ - "import uuid\n", - "from azure.ai.ml import Output\n", - "\n", - "insights_pipeline_job = rai_fridge_image_classification_pipeline(\n", - " target_column_name=target_column_name,\n", - " test_data=my_validation_data_input, # rai_fridge_validation_mltable,\n", - " classes=\"[]\",\n", - ")\n", - "\n", - "rand_path = str(uuid.uuid4())\n", - "insights_pipeline_job.outputs.dashboard = Output(\n", - " path=f\"azureml://datastores/workspaceblobstore/paths/{rand_path}/dashboard/\",\n", - " mode=\"upload\",\n", - " type=\"uri_folder\",\n", - ")\n", - "insights_pipeline_job.outputs.ux_json = Output(\n", - " path=f\"azureml://datastores/workspaceblobstore/paths/{rand_path}/ux_json/\",\n", - " mode=\"upload\",\n", - " type=\"uri_folder\",\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "25f34573", - "metadata": {}, - "source": [ - "And submit the pipeline to AzureML for execution:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9b23ab85", - "metadata": {}, - "outputs": [], - "source": [ - "import time\n", - "from azure.ai.ml.entities import PipelineJob\n", - "\n", - "\n", - "def submit_and_wait(ml_client, pipeline_job) -> PipelineJob:\n", - " created_job = ml_client.jobs.create_or_update(pipeline_job)\n", - " assert created_job is not None\n", - "\n", - " while created_job.status not in [\n", - " \"Completed\",\n", - " \"Failed\",\n", - " \"Canceled\",\n", - " \"NotResponding\",\n", - " ]:\n", - " time.sleep(30)\n", - " created_job = ml_client.jobs.get(created_job.name)\n", - " print(\"Latest status : {0}\".format(created_job.status))\n", - " assert created_job.status == \"Completed\"\n", - " return created_job" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2ca757f7", - "metadata": {}, - "outputs": [], - "source": [ - "insights_job = submit_and_wait(ml_client, insights_pipeline_job)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ce376147", - "metadata": {}, - "outputs": [], - "source": [ - "insights_job" - ] - }, - { - "cell_type": "markdown", - "id": "1381768a", - "metadata": {}, - "source": [ - "The dashboard should appear in the AzureML portal in the registered model view. The following cell computes the expected URI:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e86ab611", - "metadata": {}, - "outputs": [], - "source": [ - "sub_id = ml_client._operation_scope.subscription_id\n", - "rg_name = ml_client._operation_scope.resource_group_name\n", - "ws_name = ml_client.workspace_name\n", - "\n", - "expected_uri = f\"https://ml.azure.com/model/{expected_model_id}/model_analysis?wsid=/subscriptions/{sub_id}/resourcegroups/{rg_name}/workspaces/{ws_name}\"\n", - "\n", - "print(f\"Please visit {expected_uri} to see your analysis\")" - ] - }, - { - "cell_type": "markdown", - "id": "93a8dff9", - "metadata": {}, - "source": [ - "## 5.2 Constructing the pipeline in YAML\n", - "\n", - "It is also possible to specify the pipeline as a YAML file, and submit that using the command line. We will now create a YAML specification of the above pipeline and submit that:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7bcb4471", - "metadata": {}, - "outputs": [], - "source": [ - "my_validation_data_input" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "624bb0cd", - "metadata": {}, - "outputs": [], - "source": [ - "yaml_contents = f\"\"\"\n", - "$schema: https://azuremlschemas.azureedge.net/latest/pipelineJob.schema.json\n", - "experiment_name: AML_RAI_Vision_Sample_{rai_example_version_string}_From_YAML\n", - "type: pipeline\n", - "\n", - "compute: azureml:cpucluster\n", - "\n", - "inputs:\n", - " registered_model_name: {registered_model.name}\n", - " vision_model_info: {expected_model_id}\n", - " dataset_type: private\n", - " my_test_data:\n", - " type: mltable\n", - " path: {my_validation_data_input[\"path\"]}\n", - " mode: download\n", - "\n", - "settings:\n", - " default_datastore: azureml:workspaceblobstore\n", - " default_compute: azureml:cpucluster\n", - " continue_on_step_failure: false\n", - "\n", - "jobs:\n", - " analyse_model:\n", - " type: command\n", - " component: azureml://registries/azureml-preview/components/rai_vision_insights/versions/{version_string}\n", - " inputs:\n", - " task_type: image_classification\n", - " model_input:\n", - " type: mlflow_model\n", - " path: {azureml_model_id}\n", - " model_info: ${{{{parent.inputs.vision_model_info}}}}\n", - " test_dataset:\n", - " type: mltable\n", - " path: ${{{{parent.inputs.my_test_data}}}}\n", - " dataset_type: ${{{{parent.inputs.dataset_type}}}}\n", - " target_column_name: {target_column_name}\n", - " maximum_rows_for_test_dataset: 5000\n", - " classes: '[]'\n", - " precompute_explanation: True\n", - " model_type: pyfunc\n", - " xai_algorithm: {xai_algorithm}\n", - " n_steps: {n_steps}\n", - " xrai_fast: {xrai_fast}\n", - " approximation_method: {approximation_method}\n", - " confidence_score_threshold_multilabel: {confidence_score_threshold_multilabel}\n", - "\"\"\"\n", - "\n", - "yaml_pipeline_filename = \"rai_automl_vision_example.yaml\"\n", - "\n", - "with open(yaml_pipeline_filename, \"w\") as f:\n", - " f.write(yaml_contents)" - ] - }, - { - "cell_type": "markdown", - "id": "1fd5f2dd", - "metadata": {}, - "source": [ - "The created file can then be submitted using the Azure CLI:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3bf9bb1c", - "metadata": {}, - "outputs": [], - "source": [ - "cmd_line = [\n", - " \"az\",\n", - " \"ml\",\n", - " \"job\",\n", - " \"create\",\n", - " \"--resource-group\",\n", - " rg_name,\n", - " \"--workspace\",\n", - " ws_name,\n", - " \"--file\",\n", - " yaml_pipeline_filename,\n", - "]\n", - "\n", - "import subprocess\n", - "\n", - "try:\n", - " cmd = subprocess.run(cmd_line, check=True, shell=True, capture_output=True)\n", - "except subprocess.CalledProcessError as cpe:\n", - " print(f\"Error invoking: {cpe.args}\")\n", - " print(cpe.stdout)\n", - " print(cpe.stderr)\n", - " raise\n", - "else:\n", - " print(\"Azure CLI submission completed\")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3.10 - SDK V2", - "language": "python", - "name": "python310-sdkv2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.15" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/sdk/python/responsible-ai/vision/responsibleaidashboard-automl-object-detection-fridge-private-data.ipynb b/sdk/python/responsible-ai/vision/responsibleaidashboard-automl-object-detection-fridge-private-data.ipynb deleted file mode 100644 index ff7144bf44..0000000000 --- a/sdk/python/responsible-ai/vision/responsibleaidashboard-automl-object-detection-fridge-private-data.ipynb +++ /dev/null @@ -1,1303 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "nteract": { - "transient": { - "deleting": false - } - } - }, - "source": [ - "# Object Detection scenario with RAI Dashboard\n", - "\n", - "##### This notebook demonstrates how to\n", - "\n", - "##### 1. Upload a private mltable dataset to AzureML\n", - "##### 2. Fine-tune an **AutoML Object Detection model (fasterrcnn_resnet50_fpn)** on the OD Fridge Dataset using an **individual run of an AzureML training job** and then\n", - "##### 3. Evaluate the AutoML model on the dataset using the RAI Vision Dashboard created using another AzureML pipeline." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "nteract": { - "transient": { - "deleting": false - } - } - }, - "source": [ - "##### Depending on your current Python environment, you may need to install the following packages\n", - "\n", - "```\n", - "pip install azure-identity\n", - "pip install azure-ai-ml\n", - "pip install mlflow\n", - "pip install azureml-mlflow\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false, - "gather": { - "logged": 1686235424383 - }, - "jupyter": { - "outputs_hidden": false, - "source_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - } - }, - "outputs": [], - "source": [ - "# !pip install -U azure-identity\n", - "# !pip install -U azure-ai-ml\n", - "# !pip install -U mlflow\n", - "# !pip install -U azureml-mlflow\n", - "# !pip install -U mltable" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "nteract": { - "transient": { - "deleting": false - } - } - }, - "source": [ - "###### First, we need to specify the version of the RAI components which are available in the workspace. This was specified when the components were uploaded." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false, - "gather": { - "logged": 1686846410392 - }, - "jupyter": { - "outputs_hidden": false, - "source_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - } - }, - "outputs": [], - "source": [ - "version_string = \"0.0.20\"" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "nteract": { - "transient": { - "deleting": false - } - } - }, - "source": [ - "###### We also need to give the name of the compute cluster we want to use in AzureML. Later in this notebook, we will create it if it does not already exist:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false, - "gather": { - "logged": 1686846412047 - }, - "jupyter": { - "outputs_hidden": false, - "source_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - } - }, - "outputs": [], - "source": [ - "# Compute cluster to run the AutoML training job\n", - "train_compute_name = \"gpu-cluster-nc6-v3\"\n", - "\n", - "# Compute cluster to visualize and interact with the RAI Dashboard\n", - "rai_compute_name = \"cpucluster\"" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "nteract": { - "transient": { - "deleting": false - } - } - }, - "source": [ - "# 1. Connect to Azure Machine Learning Workspace\n", - "###### The [workspace](https://learn.microsoft.com/en-us/azure/machine-learning/concept-workspace?view=azureml-api-2) is the top-level resource for Azure Machine Learning, providing a centralized place to work with all the artifacts you create when you use Azure Machine Learning. In this section we will connect to the workspace in which the job will be run." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "nteract": { - "transient": { - "deleting": false - } - } - }, - "source": [ - "## 1.1. Import the required libraries" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false, - "gather": { - "logged": 1686846457572 - }, - "jupyter": { - "outputs_hidden": false, - "source_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - } - }, - "outputs": [], - "source": [ - "# Import required libraries\n", - "from azure.identity import DefaultAzureCredential\n", - "from azure.ai.ml import MLClient\n", - "\n", - "from azure.ai.ml.automl import SearchSpace, ObjectDetectionPrimaryMetrics\n", - "from azure.ai.ml import automl, dsl" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Enter details of your AML workspace\n", - "subscription_id = \"\"\n", - "resource_group = \"\"\n", - "workspace = \"\"" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "nteract": { - "transient": { - "deleting": false - } - } - }, - "source": [ - "## 1.2. Configure workspace details and get a handle to the workspace\n", - "\n", - "###### To connect to a workspace, we need identifier parameters - a subscription, resource group and workspace name. We will use these details in the `MLClient` from `azure.ai.ml` to get a handle to the required Azure Machine Learning workspace. We use the default [default azure authentication](https://docs.microsoft.com/en-us/python/api/azure-identity/azure.identity.defaultazurecredential?view=azure-python) for this tutorial. Check the [configuration notebook](https://github.com/Azure/azureml-examples/blob/d8070da9dec29978e9322e727d1740f5486aa56e/sdk/python/jobs/configuration.ipynb) for more details on how to configure credentials and connect to a workspace." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false, - "gather": { - "logged": 1686846461097 - }, - "jupyter": { - "outputs_hidden": false, - "source_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - } - }, - "outputs": [], - "source": [ - "# Handle to the workspace\n", - "from azure.ai.ml import MLClient\n", - "from azure.identity import DefaultAzureCredential\n", - "\n", - "try:\n", - " credential = DefaultAzureCredential()\n", - " ml_client = MLClient(\n", - " credential=credential,\n", - " subscription_id=subscription_id,\n", - " resource_group_name=resource_group,\n", - " workspace_name=workspace,\n", - " )\n", - "except Exception:\n", - " # If in compute instance we can get the config automatically\n", - " from azureml.core import Workspace\n", - "\n", - " workspace = Workspace.from_config()\n", - " workspace.write_config()\n", - " ml_client = MLClient.from_config(\n", - " credential=DefaultAzureCredential(exclude_shared_token_cache_credential=True),\n", - " logging_enable=True,\n", - " )\n", - "\n", - "print(ml_client)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "nteract": { - "transient": { - "deleting": false - } - } - }, - "source": [ - "# 2. Compute target setup\n", - "\n", - "###### You will need to provide a [Compute Target](https://docs.microsoft.com/en-us/azure/machine-learning/concept-azure-machine-learning-architecture#computes) that will be used for your AutoML model training. AutoML models for image tasks require [GPU SKUs](https://docs.microsoft.com/en-us/azure/virtual-machines/sizes-gpu) such as the ones from the NC, NCv2, NCv3, ND, NDv2 and NCasT4 series. We recommend using the NCsv3-series (with v100 GPUs) for faster training. Using a compute target with a multi-GPU VM SKU will leverage the multiple GPUs to speed up training. Additionally, setting up a compute target with multiple nodes will allow for faster model training by leveraging parallelism, when tuning hyperparameters for your model." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false, - "gather": { - "logged": 1686846463639 - }, - "jupyter": { - "outputs_hidden": false, - "source_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - } - }, - "outputs": [], - "source": [ - "from azure.ai.ml.entities import AmlCompute\n", - "from azure.core.exceptions import ResourceNotFoundError\n", - "\n", - "try:\n", - " _ = ml_client.compute.get(train_compute_name)\n", - " print(\"Found existing compute target.\")\n", - "except ResourceNotFoundError:\n", - " print(\"Creating a new compute target...\")\n", - " compute_config = AmlCompute(\n", - " name=train_compute_name,\n", - " type=\"amlcompute\",\n", - " size=\"Standard_NC6s_v3\",\n", - " idle_time_before_scale_down=120,\n", - " min_instances=0,\n", - " max_instances=4,\n", - " )\n", - " ml_client.begin_create_or_update(compute_config).result()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false, - "gather": { - "logged": 1686846466881 - }, - "jupyter": { - "outputs_hidden": false, - "source_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - } - }, - "outputs": [], - "source": [ - "from azure.ai.ml.entities import AmlCompute\n", - "\n", - "all_compute_names = [x.name for x in ml_client.compute.list()]\n", - "\n", - "if rai_compute_name in all_compute_names:\n", - " print(f\"Found existing compute: {rai_compute_name}\")\n", - "else:\n", - " rai_compute_config = AmlCompute(\n", - " name=rai_compute_name,\n", - " size=\"STANDARD_DS3_V2\",\n", - " min_instances=0,\n", - " max_instances=4,\n", - " idle_time_before_scale_down=3600,\n", - " )\n", - " ml_client.compute.begin_create_or_update(rai_compute_config)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 3. MLTable with input Training Data\n", - "\n", - "###### In order to generate models for computer vision tasks with automated machine learning, you need to bring labeled image data as input for model training in the form of an MLTable. You can create an MLTable from labeled training data in JSONL format. If your labeled training data is in a different format (like, pascal VOC or COCO), you can use a conversion script to first convert it to JSONL, and then create an MLTable. Alternatively, you can use Azure Machine Learning's [data labeling tool](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-create-image-labeling-projects) to manually label images, and export the labeled data to use for training your AutoML model.\n", - "\n", - "###### In this notebook, we use a toy dataset called Fridge Objects, which consists of 128 images of 4 labels of beverage container {`can`, `carton`, `milk bottle`, `water bottle`} photos taken on different backgrounds.\n", - "\n", - "###### All images in this notebook are hosted in [this repository](https://github.com/microsoft/computervision-recipes) and are made available under the [MIT license](https://github.com/microsoft/computervision-recipes/blob/master/LICENSE)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3.1. Download the Data\n", - "###### We first download and unzip the data locally. By default, the data would be downloaded in `./data` folder in current directory. \n", - "###### If you prefer to download the data at a different location, update it in `dataset_parent_dir = ...` in the next cell." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import urllib\n", - "from zipfile import ZipFile\n", - "\n", - "# Change to a different location if you prefer\n", - "dataset_parent_dir = \"./data\"\n", - "\n", - "# create data folder if it doesnt exist.\n", - "os.makedirs(dataset_parent_dir, exist_ok=True)\n", - "\n", - "# download data\n", - "download_url = \"https://publictestdatasets.blob.core.windows.net/computervision/odFridgeObjects.zip\"\n", - "\n", - "# Extract current dataset name from dataset url\n", - "dataset_name = os.path.split(download_url)[-1].split(\".\")[0]\n", - "# Get dataset path for later use\n", - "dataset_dir = os.path.join(dataset_parent_dir, dataset_name)\n", - "\n", - "# Get the data zip file path\n", - "data_file = os.path.join(dataset_parent_dir, f\"{dataset_name}.zip\")\n", - "\n", - "# Download the dataset\n", - "urllib.request.urlretrieve(download_url, filename=data_file)\n", - "\n", - "# extract files\n", - "with ZipFile(data_file, \"r\") as zip:\n", - " print(\"extracting files...\")\n", - " zip.extractall(path=dataset_parent_dir)\n", - " print(\"done\")\n", - "# delete zip file\n", - "os.remove(data_file)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This is a sample image from this dataset:\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from IPython.display import Image\n", - "\n", - "sample_image = os.path.join(dataset_dir, \"images\", \"31.jpg\")\n", - "Image(filename=sample_image)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3.2. Upload the images to Datastore through an AML Data asset (URI Folder)\n", - "\n", - "###### In order to use the data for training in Azure ML, we upload it to our default Azure Blob Storage of our Azure ML Workspace.\n", - "\n", - "###### [Check this notebook for AML data asset example](https://github.com/Azure/azureml-examples/blob/d8070da9dec29978e9322e727d1740f5486aa56e/sdk/python/assets/data/data.ipynb)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Uploading image files by creating a 'data asset URI FOLDER':\n", - "\n", - "from azure.ai.ml.entities import Data\n", - "from azure.ai.ml.constants import AssetTypes, InputOutputModes\n", - "from azure.ai.ml import Input\n", - "\n", - "my_data = Data(\n", - " path=dataset_dir,\n", - " type=AssetTypes.URI_FOLDER,\n", - " description=\"Fridge-items images Object detection\",\n", - " name=\"fridge-items-images-object-detection\",\n", - ")\n", - "\n", - "uri_folder_data_asset = ml_client.data.create_or_update(my_data)\n", - "\n", - "print(uri_folder_data_asset)\n", - "print(\"\")\n", - "print(\"Path to folder in Blob Storage:\")\n", - "print(uri_folder_data_asset.path)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3.3. Convert the downloaded data to JSONL\n", - "\n", - "###### In this example, the fridge object dataset is annotated in Pascal VOC format, where each image corresponds to an xml file. Each xml file contains information on where its corresponding image file is located and also contains information about the bounding boxes and the object labels. \n", - "\n", - "###### For documentation on preparing the datasets beyond this notebook, please refer to the [documentation on how to prepare datasets](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-prepare-datasets-for-automl-images).\n", - "\n", - "\n", - "###### In order to use this data to create an AzureML MLTable, we first need to convert it to the required JSONL format. The following script is creating two `.jsonl` files (one for training and one for validation) in the corresponding MLTable folder. The train / validation ratio corresponds to 20% of the data going into the validation file. For further details on jsonl file used for image classification task in automated ml, please refer to the [data schema documentation for image object-detection task](https://learn.microsoft.com/en-us/azure/machine-learning/reference-automl-images-schema#object-detection)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### First generate JSONL files\n", - "\n", - "###### The JSONL Conversion helpers require pycocotools and simplification packages" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%pip install pycocotools==2.0.6\n", - "%pip install simplification==0.6.11\n", - "%pip install scikit-image==0.19.3" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import sys\n", - "\n", - "# use the jsonl-conversion files from automl examples folder\n", - "sys.path.insert(0, \"../../jobs/automl-standalone-jobs/jsonl-conversion/\")\n", - "from base_jsonl_converter import write_json_lines\n", - "from voc_jsonl_converter import VOCJSONLConverter\n", - "\n", - "base_url = os.path.join(uri_folder_data_asset.path, \"images/\")\n", - "converter = VOCJSONLConverter(base_url, os.path.join(dataset_dir, \"annotations\"))\n", - "jsonl_annotations = os.path.join(dataset_dir, \"annotations_voc.jsonl\")\n", - "write_json_lines(converter, jsonl_annotations)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Then split into train and validation" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "\n", - "# We'll copy each JSONL file within its related MLTable folder\n", - "training_mltable_path = os.path.join(dataset_parent_dir, \"training-mltable-folder\")\n", - "validation_mltable_path = os.path.join(dataset_parent_dir, \"validation-mltable-folder\")\n", - "\n", - "# First, let's create the folders if they don't exist\n", - "os.makedirs(training_mltable_path, exist_ok=True)\n", - "os.makedirs(validation_mltable_path, exist_ok=True)\n", - "\n", - "train_validation_ratio = 5\n", - "\n", - "# Path to the training and validation files\n", - "train_annotations_file = os.path.join(training_mltable_path, \"train_annotations.jsonl\")\n", - "validation_annotations_file = os.path.join(\n", - " validation_mltable_path, \"validation_annotations.jsonl\"\n", - ")\n", - "\n", - "with open(jsonl_annotations, \"r\") as annot_f:\n", - " json_lines = annot_f.readlines()\n", - "\n", - "index = 0\n", - "with open(train_annotations_file, \"w\") as train_f:\n", - " with open(validation_annotations_file, \"w\") as validation_f:\n", - " for json_line in json_lines:\n", - " if index % train_validation_ratio == 0:\n", - " # validation annotation\n", - " validation_f.write(json_line)\n", - " else:\n", - " # train annotation\n", - " train_f.write(json_line)\n", - " index += 1" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3.4. Convert annotation file from COCO to JSONL\n", - "###### If you want to try with a dataset in COCO format, see the example shown in Section 2 (specifically 2.4) of this [AutoML training notebook](https://github.com/Azure/azureml-examples/blob/main/sdk/python/jobs/automl-standalone-jobs/automl-image-object-detection-task-fridge-items/automl-image-object-detection-task-fridge-items.ipynb)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Visualize bounding boxes\n", - "###### Please refer to the \"Visualize data\" section in the following [tutorial](https://docs.microsoft.com/en-us/azure/machine-learning/tutorial-auto-train-image-models#visualize-data) to see how to easily visualize your ground truth bounding boxes before starting to train." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3.5. Create MLTable data input\n", - "###### Create MLTable data input using the jsonl files created above.\n", - "\n", - "###### For documentation on creating your own MLTable assets for jobs beyond this notebook, please refer to below resources\n", - "###### - [MLTable YAML Schema](https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-mltable) - covers how to write MLTable YAML, which is required for each MLTable asset.\n", - "###### - [Create MLTable data asset](https://learn.microsoft.com/en-us/azure/machine-learning/how-to-create-data-assets?tabs=Python-SDK#create-a-mltable-data-asset) - covers how to create MLTable data asset. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def create_ml_table_file(filename):\n", - " \"\"\"Create ML Table definition\"\"\"\n", - "\n", - " return (\n", - " \"paths:\\n\"\n", - " \" - file: ./{0}\\n\"\n", - " \"transformations:\\n\"\n", - " \" - read_json_lines:\\n\"\n", - " \" encoding: utf8\\n\"\n", - " \" invalid_lines: error\\n\"\n", - " \" include_path_column: false\\n\"\n", - " \" - convert_column_types:\\n\"\n", - " \" - columns: image_url\\n\"\n", - " \" column_type: stream_info\"\n", - " ).format(filename)\n", - "\n", - "\n", - "def save_ml_table_file(output_path, mltable_file_contents):\n", - " with open(os.path.join(output_path, \"MLTable\"), \"w\") as f:\n", - " f.write(mltable_file_contents)\n", - "\n", - "\n", - "# Create and save train mltable\n", - "train_mltable_file_contents = create_ml_table_file(\n", - " os.path.basename(train_annotations_file)\n", - ")\n", - "save_ml_table_file(training_mltable_path, train_mltable_file_contents)\n", - "\n", - "# Save train and validation mltable\n", - "validation_mltable_file_contents = create_ml_table_file(\n", - " os.path.basename(validation_annotations_file)\n", - ")\n", - "save_ml_table_file(validation_mltable_path, validation_mltable_file_contents)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Training MLTable defined locally, with local data to be uploaded\n", - "my_training_data_input = Input(type=AssetTypes.MLTABLE, path=training_mltable_path)\n", - "\n", - "# Validation MLTable defined locally, with local data to be uploaded\n", - "my_validation_data_input = Input(type=AssetTypes.MLTABLE, path=validation_mltable_path)\n", - "\n", - "# WITH REMOTE PATH: If available already in the cloud/workspace-blob-store\n", - "# my_training_data_input = Input(type=AssetTypes.MLTABLE, path=\"azureml://datastores/workspaceblobstore/paths/vision-classification/train\")\n", - "# my_validation_data_input = Input(type=AssetTypes.MLTABLE, path=\"azureml://datastores/workspaceblobstore/paths/vision-classification/valid\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "###### Specify the name of the label/target column containing the classes and bbox information. (NOTE: This variable is used later in the RAI Dashboard section of the notebook as well.)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "target_column_name = \"label\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 4. Configure and run the AutoML for Images Object Detection training job\n", - "\n", - "###### AutoML allows you to easily train models for Image Classification, Object Detection & Instance Segmentation on your image data. You can control the model algorithm and hyperparameters to be used, perform a sweep over a manually specified hyperparameter space, or the system can automatically perform a hyperparameter sweep for you.\n", - "\n", - "###### To explore different training options with AutoML, have a look at Section of 4 of this [example notebook that demonstrates how to train AutoML models for Object Detection.](https://github.com/Azure/azureml-examples/blob/main/sdk/python/jobs/automl-standalone-jobs/automl-image-object-detection-task-fridge-items/automl-image-object-detection-task-fridge-items.ipynb)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Individual AutoML training run\n", - "\n", - "###### You can launch individual runs to explore model algorithms; we provide sensible default hyperparameters for each algorithm. You can also launch individual runs for the same model algorithm and different hyperparameter combinations. The model algorithm is specified using the model_name parameter. Please refer to the [documentation](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models?tabs=CLI-v2#configure-model-algorithms-and-hyperparameters) for the list of supported model algorithms.\n", - "\n", - "###### The following function can be used to configure AutoML jobs for individual runs:\n", - "### set_training_parameters() function parameters:\n", - "###### This is an optional configuration method to configure fixed settings or parameters that don't change during the parameter space sweep. Some of the key parameters of this function are:\n", - "\n", - "###### - `model_name` - The name of the ML algorithm that we want to use in training job. Please refer to this [documentation](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models?tabs=CLI-v2#supported-model-algorithms) for supported model algorithm.\n", - "###### - `number_of_epochs` - The number of training epochs. It must be positive integer (default value is 15).\n", - "###### - `layers_to_freeze` - The number of layers to freeze in model for transfer learning. It must be a positive integer (default value is 0).\n", - "###### - `early_stopping` - It enable early stopping logic during training, It must be boolean value (default is True). \n", - "###### - `optimizer` - Type of optimizer to use in training. It must be either sgd, adam, adamw (default is sgd).\n", - "###### - `distributed` - It enable distributed training if compute target contain multiple GPUs. It must be boolean value (default is True).\n", - "\n", - "###### If you wish to use the default hyperparameter values for a given algorithm (say `yolov5`), you can specify the job for your AutoML Image runs as follows:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# general job parameters\n", - "exp_name = \"dpv2-odfridge-automl-training\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Create the AutoML job with the related factory-function.\n", - "image_object_detection_job = automl.image_object_detection(\n", - " compute=train_compute_name,\n", - " experiment_name=exp_name,\n", - " training_data=my_training_data_input,\n", - " validation_data=my_validation_data_input,\n", - " target_column_name=target_column_name,\n", - " primary_metric=ObjectDetectionPrimaryMetrics.MEAN_AVERAGE_PRECISION,\n", - " tags={\"data\": \"ODFridge\", \"model type\": \"AutoML\"},\n", - ")\n", - "\n", - "# Set limits\n", - "image_object_detection_job.set_limits(timeout_minutes=60)\n", - "\n", - "# Pass the fixed settings or parameters\n", - "image_object_detection_job.set_training_parameters(\n", - " model_name=\"fasterrcnn_resnet50_fpn\",\n", - " early_stopping=1,\n", - " number_of_epochs=1,\n", - " learning_rate=0.09,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Submit the AutoML job\n", - "returned_job = ml_client.jobs.create_or_update(image_object_detection_job)\n", - "\n", - "print(f\"Created job: {returned_job}\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ml_client.jobs.stream(returned_job.name)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 5. Retrieve the Best Trial (Best Model's trial/run) and Register best model\n", - "###### Use the MLFLowClient to access the results (such as Models, Artifacts, Metrics) of a previously completed AutoML Trial." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 5.1 Initialize MLFlow Client and Obtain the tracking URI for MLFlow\n", - "\n", - "###### The models and artifacts that are produced by AutoML can be accessed via the MLFlow interface.\n", - "###### Initialize the MLFlow client here, and set the backend as Azure ML, via. the MLFlow Client.\n", - "\n", - "###### IMPORTANT, you need to have installed the latest MLFlow packages with:\n", - "\n", - " pip install azureml-mlflow\n", - "\n", - " pip install mlflow" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import mlflow\n", - "\n", - "# Obtain the tracking URL from MLClient\n", - "MLFLOW_TRACKING_URI = ml_client.workspaces.get(\n", - " name=ml_client.workspace_name\n", - ").mlflow_tracking_uri\n", - "\n", - "print(MLFLOW_TRACKING_URI)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Set the MLFLOW TRACKING URI\n", - "mlflow.set_tracking_uri(MLFLOW_TRACKING_URI)\n", - "print(f\"\\nCurrent tracking uri: {mlflow.get_tracking_uri()}\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from mlflow.tracking.client import MlflowClient\n", - "\n", - "# Initialize MLFlow client\n", - "mlflow_client = MlflowClient()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 5.2 Get the AutoML parent Job and Get the AutoML best child run" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "job_name = returned_job.name\n", - "\n", - "# # Example if providing an specific Job name/ID\n", - "# job_name = \"happy_yam_40fq53m7c2\" #\"ashy_net_gdd31zf2fq\"\n", - "\n", - "# Get the parent run\n", - "mlflow_parent_run = mlflow_client.get_run(job_name)\n", - "\n", - "print(\"Parent Run: \")\n", - "print(mlflow_parent_run)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Print parent run tags. 'automl_best_child_run_id' tag should be there.\n", - "print(mlflow_parent_run.data.tags.keys())" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Get the best model's child run\n", - "\n", - "best_child_run_id = mlflow_parent_run.data.tags[\"automl_best_child_run_id\"]\n", - "print(f\"Found best child run id: {best_child_run_id}\")\n", - "\n", - "best_run = mlflow_client.get_run(best_child_run_id)\n", - "\n", - "print(\"Best child run: \")\n", - "print(best_run)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "\n", - "# Access the results (such as Models, Artifacts, Metrics) of a previously completed AutoML Run.\n", - "pd.DataFrame(best_run.data.metrics, index=[0]).T" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 5.3 Download the best model locally\n", - "###### Access the results (such as Models, Artifacts, Metrics) of a previously completed AutoML Run." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Create local folder\n", - "import os\n", - "\n", - "local_dir = \"./artifact_downloads\"\n", - "if not os.path.exists(local_dir):\n", - " os.mkdir(local_dir)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Download run's artifacts/outputs\n", - "local_path = mlflow_client.download_artifacts(\n", - " best_run.info.run_id, \"outputs\", local_dir\n", - ")\n", - "print(f\"Artifacts downloaded in: {local_path}\")\n", - "print(f\"Artifacts: {os.listdir(local_path)}\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "\n", - "mlflow_model_dir = os.path.join(local_dir, \"outputs\", \"mlflow-model\")\n", - "\n", - "# Show the contents of the MLFlow model folder\n", - "os.listdir(mlflow_model_dir)\n", - "\n", - "# You should see a list of files such as the following:\n", - "# ['artifacts', 'conda.yaml', 'MLmodel', 'python_env.yaml', 'python_model.pkl', 'requirements.txt']" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 5.4 Register Model" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# import required libraries\n", - "from azure.ai.ml.entities import Model\n", - "\n", - "model_name = \"automl-fasterrcnn-odfridge-model\"\n", - "\n", - "model = Model(\n", - " path=f\"azureml://jobs/{best_run.info.run_id}/outputs/artifacts/outputs/mlflow-model/\",\n", - " name=model_name,\n", - " description=\"AutoML FasterRCNN model trained on OD Fridge Dataset\",\n", - " type=AssetTypes.MLFLOW_MODEL,\n", - ")\n", - "\n", - "# for downloaded file\n", - "# model = Model(\n", - "# path=mlflow_model_dir,\n", - "# name=model_name,\n", - "# description=\"AutoML FasterRCNN model trained on OD Fridge Dataset\",\n", - "# type=AssetTypes.MLFLOW_MODEL,\n", - "# )\n", - "\n", - "registered_model = ml_client.models.create_or_update(model)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "registered_model.id" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 5.5 (Optional) If you already have a registered AutoML Object Detection model" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# model_name = \"automl-fasterrcnn-odfridge-model\"\n", - "# version_number = 4\n", - "\n", - "# registered_model = ml_client.models.get(model_name, version=version_number)\n", - "# print(registered_model.name, registered_model.version)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "###### Specify the name of the label/target column containing the classes and bbox information. (NOTE: This variable is used later in the RAI Dashboard section of the notebook as well.)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# target_column_name = \"label\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 6. Creating the RAI Vision Insights" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "###### Now that we have our model, we can generate RAI Vision insights for it. We will need the `id` of the registered model, which will be as follows:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "expected_model_id = f\"{registered_model.name}:{registered_model.version}\"\n", - "azureml_model_id = f\"azureml:{expected_model_id}\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 6.1 Prepare the data you want to analyze with the RAI Dashboard\n", - "#### NOTE: This example notebook demonstrates how to use a private OD Fridge Dataset in AzureML" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "try:\n", - " from urllib import urlretrieve\n", - "except ImportError:\n", - " from urllib.request import urlretrieve\n", - "\n", - "# from enum import Enum\n", - "import xml.etree.ElementTree as ET\n", - "import pandas as pd\n", - "from zipfile import ZipFile\n", - "\n", - "from azure.ai.ml import Input\n", - "from azure.ai.ml.constants import AssetTypes" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fridge_test_mltable = Input(type=AssetTypes.MLTABLE, path=validation_mltable_path)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 6.3 Load the RAI Components\n", - "###### Next, we load the RAI components, so that we can construct a pipeline:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "registry_name = \"azureml\"\n", - "credential = DefaultAzureCredential()\n", - "\n", - "ml_client_registry = MLClient(\n", - " credential=credential,\n", - " subscription_id=ml_client.subscription_id,\n", - " resource_group_name=ml_client.resource_group_name,\n", - " registry_name=registry_name,\n", - ")\n", - "\n", - "rai_vision_insights_component = ml_client_registry.components.get(\n", - " name=\"rai_vision_insights\", version=version_string\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 6.4 Constructing the pipeline in sdk\n", - "###### We can now specify our pipeline. Complex objects (such as lists of column names) have to be converted to JSON strings before being passed to the components." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import json\n", - "from azure.ai.ml import Input\n", - "from azure.ai.ml.constants import AssetTypes\n", - "\n", - "\n", - "@dsl.pipeline(\n", - " compute=rai_compute_name,\n", - " description=\"Example RAI computation on Fridge data with AutoML FasterRCNN Object Detection model\",\n", - " experiment_name=f\"RAI_Fridge_Example_RAIInsights_Computation_{expected_model_id}\",\n", - ")\n", - "def rai_fridge_object_detection_pipeline(target_column_name, test_data, classes):\n", - " # Initiate the RAIInsights\n", - " rai_image_job = rai_vision_insights_component(\n", - " task_type=\"object_detection\",\n", - " model_info=expected_model_id,\n", - " model_input=Input(type=AssetTypes.MLFLOW_MODEL, path=azureml_model_id),\n", - " test_dataset=test_data,\n", - " target_column_name=target_column_name,\n", - " classes=classes,\n", - " model_type=\"pyfunc\",\n", - " precompute_explanation=True,\n", - " dataset_type=\"private\",\n", - " enable_error_analysis=False,\n", - " maximum_rows_for_test_dataset=5000,\n", - " num_masks=300,\n", - " mask_res=4,\n", - " )\n", - " rai_image_job.set_limits(timeout=24000)\n", - "\n", - " rai_image_job.outputs.dashboard.mode = \"upload\"\n", - " rai_image_job.outputs.ux_json.mode = \"upload\"\n", - "\n", - " return {\n", - " \"dashboard\": rai_image_job.outputs.dashboard,\n", - " \"ux_json\": rai_image_job.outputs.ux_json,\n", - " }" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "###### Next, we define the pipeline object itself, and ensure that the outputs will be available for download:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import uuid\n", - "from azure.ai.ml import Output\n", - "\n", - "insights_pipeline_job = rai_fridge_object_detection_pipeline(\n", - " target_column_name=target_column_name,\n", - " test_data=fridge_test_mltable,\n", - " classes='[\"can\", \"carton\", \"milk_bottle\", \"water_bottle\"]', # Ensure the class order matches that of used while creating the jsonl file for the test data\n", - ")\n", - "\n", - "rand_path = str(uuid.uuid4())\n", - "insights_pipeline_job.outputs.dashboard = Output(\n", - " path=f\"azureml://datastores/workspaceblobstore/paths/{rand_path}/dashboard/\",\n", - " mode=\"upload\",\n", - " type=\"uri_folder\",\n", - ")\n", - "insights_pipeline_job.outputs.ux_json = Output(\n", - " path=f\"azureml://datastores/workspaceblobstore/paths/{rand_path}/ux_json/\",\n", - " mode=\"upload\",\n", - " type=\"uri_folder\",\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "###### And submit the pipeline to AzureML for execution:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Submit the RAI Vision Insights job\n", - "returned_job = ml_client.jobs.create_or_update(insights_pipeline_job)\n", - "\n", - "print(f\"Created job: {returned_job}\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ml_client.jobs.stream(returned_job.name)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "###### The dashboard should appear in the AzureML portal in the registered model view. The following cell computes the expected URI:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "sub_id = ml_client._operation_scope.subscription_id\n", - "rg_name = ml_client._operation_scope.resource_group_name\n", - "ws_name = ml_client.workspace_name\n", - "\n", - "expected_uri = f\"https://ml.azure.com/model/{expected_model_id}/model_analysis?wsid=/subscriptions/{sub_id}/resourcegroups/{rg_name}/workspaces/{ws_name}\"\n", - "\n", - "print(f\"Please visit {expected_uri} to see your analysis\")" - ] - } - ], - "metadata": { - "kernel_info": { - "name": "python310-sdkv2" - }, - "kernelspec": { - "display_name": "Python 3.10 - SDK V2", - "language": "python", - "name": "python310-sdkv2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.17" - }, - "microsoft": { - "host": { - "AzureML": { - "notebookHasBeenCompleted": true - } - }, - "ms_spell_check": { - "ms_spell_check_language": "en" - } - }, - "nteract": { - "version": "nteract-front-end@1.0.0" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/sdk/python/responsible-ai/vision/responsibleaidashboard-image-classification-fridge.ipynb b/sdk/python/responsible-ai/vision/responsibleaidashboard-image-classification-fridge.ipynb deleted file mode 100644 index dff666202a..0000000000 --- a/sdk/python/responsible-ai/vision/responsibleaidashboard-image-classification-fridge.ipynb +++ /dev/null @@ -1,996 +0,0 @@ -{ - "cells": [ - { - "attachments": {}, - "cell_type": "markdown", - "id": "98605bcd", - "metadata": {}, - "source": [ - "# Image Classification scenario with RAI Dashboard\n", - "\n", - "The [fridge dataset](https://github.com/microsoft/computervision-recipes/tree/master/scenarios/classification) classifies images into four types of items commonly found in the Microsoft New England R&D office refrigerator - carton, water bottle, can and milk bottle. This example notebook demonstrates how to use a fine-tuned fastai computer vision model on the dataset to evaluate the model in AzureML.\n", - "\n", - "First, we need to specify the version of the RAI components which are available in the workspace. This was specified when the components were uploaded." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "53b4eeac", - "metadata": {}, - "outputs": [], - "source": [ - "version_string = \"0.0.21\"" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "06008690", - "metadata": {}, - "source": [ - "We also need to give the name of the compute cluster we want to use in AzureML. Later in this notebook, we will create it if it does not already exist:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f1ad79f9", - "metadata": {}, - "outputs": [], - "source": [ - "compute_name = \"cpucluster\"" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "9fc65dc7", - "metadata": {}, - "source": [ - "Finally, we need to specify a version for the data and components we will create while running this notebook. This should be unique for the workspace, but the specific value doesn't matter:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "78053935", - "metadata": {}, - "outputs": [], - "source": [ - "rai_example_version_string = \"22\"" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "73be2b63", - "metadata": {}, - "source": [ - "## Accessing the Data\n", - "\n", - "We supply the data as a pair of parquet files and accompanying `MLTable` file. We can download them, preprocess them, and take a brief look:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5f875f18", - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import pandas as pd\n", - "\n", - "try:\n", - " from urllib import urlretrieve\n", - "except ImportError:\n", - " from urllib.request import urlretrieve" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "8f9ce4ae", - "metadata": {}, - "source": [ - "First we download the fridge dataset:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0eac648c", - "metadata": {}, - "outputs": [], - "source": [ - "def download_fridge_dataset(data_path, annotations_file):\n", - " os.makedirs(data_path, exist_ok=True)\n", - "\n", - " # download data\n", - " base_url = \"https://publictestdatasets.blob.core.windows.net/\"\n", - " fridge_folder = \"computervision/fridgeObjects/\"\n", - "\n", - " data_url = base_url + fridge_folder + annotations_file\n", - " data_output_path = os.path.join(data_path, annotations_file)\n", - " urlretrieve(data_url, filename=data_output_path)\n", - "\n", - "\n", - "test_annotations = \"test_annotations.jsonl\"\n", - "test_data_path = \"fridge_test_data\"\n", - "download_fridge_dataset(test_data_path, test_annotations)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "17d53df4", - "metadata": {}, - "source": [ - "Now create the mltable:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f39d2ea8", - "metadata": {}, - "outputs": [], - "source": [ - "def create_ml_table_file(filename):\n", - " return (\n", - " \"$schema: http://azureml/sdk-2-0/MLTable.json\\n\"\n", - " \"type: mltable\\n\"\n", - " \"paths:\\n\"\n", - " \" - file: ./{0}\\n\"\n", - " \"transformations:\\n\"\n", - " \" - read_json_lines:\\n\"\n", - " \" encoding: utf8\\n\"\n", - " \" invalid_lines: error\\n\"\n", - " \" include_path_column: false\\n\"\n", - " ).format(filename)\n", - "\n", - "\n", - "def save_ml_table_file(output_path, ml_table_data):\n", - " mltable_file_contents = create_ml_table_file(ml_table_data)\n", - " with open(os.path.join(output_path, \"MLTable\"), \"w\") as f:\n", - " f.write(mltable_file_contents)\n", - "\n", - "\n", - "save_ml_table_file(test_data_path, test_annotations)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "a2c4ebb4", - "metadata": {}, - "source": [ - "Load some data for a quick view:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1027fa92", - "metadata": {}, - "outputs": [], - "source": [ - "import mltable\n", - "\n", - "tbl = mltable.load(test_data_path)\n", - "test_df: pd.DataFrame = tbl.to_pandas_dataframe()\n", - "\n", - "display(test_df)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "1115ac59", - "metadata": {}, - "source": [ - "The label column contains the classes:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5b42df3d", - "metadata": {}, - "outputs": [], - "source": [ - "target_column_name = \"label\"" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "52e79b04", - "metadata": {}, - "source": [ - "First, we need to upload the datasets to our workspace. We start by creating an `MLClient` for interactions with AzureML:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "180ad111", - "metadata": {}, - "outputs": [], - "source": [ - "# Enter details of your AML workspace\n", - "subscription_id = \"\"\n", - "resource_group = \"\"\n", - "workspace = \"\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "395435fc", - "metadata": {}, - "outputs": [], - "source": [ - "# Handle to the workspace\n", - "from azure.ai.ml import MLClient\n", - "from azure.identity import DefaultAzureCredential\n", - "\n", - "try:\n", - " credential = DefaultAzureCredential()\n", - " ml_client = MLClient(\n", - " credential=credential,\n", - " subscription_id=subscription_id,\n", - " resource_group_name=resource_group,\n", - " workspace_name=workspace,\n", - " )\n", - "except Exception:\n", - " # If in compute instance we can get the config automatically\n", - " from azureml.core import Workspace\n", - "\n", - " workspace = Workspace.from_config()\n", - " workspace.write_config()\n", - " ml_client = MLClient.from_config(\n", - " credential=DefaultAzureCredential(exclude_shared_token_cache_credential=True),\n", - " logging_enable=True,\n", - " )\n", - "\n", - "print(ml_client)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "7b501735", - "metadata": {}, - "source": [ - "We can now upload the data to AzureML:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "62eb02a2", - "metadata": {}, - "outputs": [], - "source": [ - "from azure.ai.ml.entities import Data\n", - "from azure.ai.ml.constants import AssetTypes\n", - "\n", - "input_test_data = \"Fridge_Test_MLTable\"\n", - "\n", - "try:\n", - " test_data = ml_client.data.get(\n", - " name=input_test_data,\n", - " version=rai_example_version_string,\n", - " )\n", - "except Exception:\n", - " test_data = Data(\n", - " path=test_data_path,\n", - " type=AssetTypes.MLTABLE,\n", - " description=\"RAI Fridge test data\",\n", - " name=input_test_data,\n", - " version=rai_example_version_string,\n", - " )\n", - " ml_client.data.create_or_update(test_data)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "6815ba75", - "metadata": {}, - "source": [ - "# Creating the Model\n", - "\n", - "To simplify the model creation process, we're going to use a pipeline.\n", - "\n", - "We create a directory for the training script:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e78d869b", - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "\n", - "os.makedirs(\"fridge_component_src\", exist_ok=True)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "ea86e55d", - "metadata": {}, - "source": [ - "Next, we write out our script to retrieve the trained model:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a523f144", - "metadata": {}, - "outputs": [], - "source": [ - "%%writefile fridge_component_src/training_script.py\n", - "\n", - "import argparse\n", - "import logging\n", - "import json\n", - "import os\n", - "import time\n", - "\n", - "\n", - "import mlflow\n", - "import mlflow.pyfunc\n", - "\n", - "from azureml.core import Run\n", - "\n", - "from fastai.learner import load_learner\n", - "\n", - "from raiutils.common.retries import retry_function\n", - "\n", - "try:\n", - " from urllib import urlretrieve\n", - "except ImportError:\n", - " from urllib.request import urlretrieve\n", - "\n", - "_logger = logging.getLogger(__file__)\n", - "logging.basicConfig(level=logging.INFO)\n", - "\n", - "FRIDGE_MODEL_NAME = 'fridge_model'\n", - "\n", - "\n", - "def parse_args():\n", - " # setup arg parser\n", - " parser = argparse.ArgumentParser()\n", - "\n", - " # add arguments\n", - " parser.add_argument(\n", - " \"--model_output_path\", type=str, help=\"Path to write model info JSON\"\n", - " )\n", - " parser.add_argument(\n", - " \"--model_base_name\", type=str, help=\"Name of the registered model\"\n", - " )\n", - " parser.add_argument(\n", - " \"--model_name_suffix\", type=int, help=\"Set negative to use epoch_secs\"\n", - " )\n", - " parser.add_argument(\n", - " \"--device\", type=int, help=(\n", - " \"Device for CPU/GPU supports. Setting this to -1 will leverage \"\n", - " \"CPU, >=0 will run the model on the associated CUDA device id.\")\n", - " )\n", - "\n", - " # parse args\n", - " args = parser.parse_args()\n", - "\n", - " # return args\n", - " return args\n", - "\n", - "\n", - "class FetchModel(object):\n", - " def __init__(self):\n", - " pass\n", - "\n", - " def fetch(self):\n", - " url = ('https://publictestdatasets.blob.core.windows.net/models/' +\n", - " FRIDGE_MODEL_NAME)\n", - " urlretrieve(url, FRIDGE_MODEL_NAME)\n", - "\n", - "\n", - "def main(args):\n", - " current_experiment = Run.get_context().experiment\n", - " tracking_uri = current_experiment.workspace.get_mlflow_tracking_uri()\n", - " _logger.info(\"tracking_uri: {0}\".format(tracking_uri))\n", - " mlflow.set_tracking_uri(tracking_uri)\n", - " mlflow.set_experiment(current_experiment.name)\n", - "\n", - " _logger.info(\"Getting device\")\n", - " device = args.device\n", - "\n", - " _logger.info(\"Loading parquet input\")\n", - "\n", - " # Load the fridge fastai model\n", - " fetcher = FetchModel()\n", - " action_name = \"Model download\"\n", - " err_msg = \"Failed to download model\"\n", - " max_retries = 4\n", - " retry_delay = 60\n", - " retry_function(fetcher.fetch, action_name, err_msg,\n", - " max_retries=max_retries,\n", - " retry_delay=retry_delay)\n", - " model = load_learner(FRIDGE_MODEL_NAME)\n", - "\n", - " if device >= 0:\n", - " model = model.cuda()\n", - "\n", - " if args.model_name_suffix < 0:\n", - " suffix = int(time.time())\n", - " else:\n", - " suffix = args.model_name_suffix\n", - " registered_name = \"{0}_{1}\".format(args.model_base_name, suffix)\n", - " _logger.info(f\"Registering model as {registered_name}\")\n", - "\n", - " # Saving model with mlflow\n", - " _logger.info(\"Saving with mlflow\")\n", - "\n", - " mlflow.fastai.log_model(\n", - " model,\n", - " artifact_path=registered_name,\n", - " registered_model_name=registered_name\n", - " )\n", - "\n", - " _logger.info(\"Writing JSON\")\n", - " dict = {\"id\": \"{0}:1\".format(registered_name)}\n", - " output_path = os.path.join(args.model_output_path, \"model_info.json\")\n", - " with open(output_path, \"w\") as of:\n", - " json.dump(dict, fp=of)\n", - "\n", - "\n", - "# run script\n", - "if __name__ == \"__main__\":\n", - " # add space in logs\n", - " print(\"*\" * 60)\n", - " print(\"\\n\\n\")\n", - "\n", - " # parse args\n", - " args = parse_args()\n", - "\n", - " # run main function\n", - " main(args)\n", - "\n", - " # add space in logs\n", - " print(\"*\" * 60)\n", - " print(\"\\n\\n\")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "e115dd6e", - "metadata": {}, - "source": [ - "Now, we can build this into an AzureML component:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3d54e43f", - "metadata": {}, - "outputs": [], - "source": [ - "from azure.ai.ml import load_component\n", - "\n", - "yaml_contents = f\"\"\"\n", - "$schema: http://azureml/sdk-2-0/CommandComponent.json\n", - "name: rai_fridge_training_component\n", - "display_name: Fridge training component for RAI example\n", - "version: {rai_example_version_string}\n", - "type: command\n", - "inputs:\n", - " model_base_name:\n", - " type: string\n", - " model_name_suffix: # Set negative to use epoch_secs\n", - " type: integer\n", - " default: -1\n", - " device: # set to >= 0 to use GPU\n", - " type: integer\n", - " default: 0\n", - "outputs:\n", - " model_output_path:\n", - " type: path\n", - "code: ./fridge_component_src/\n", - "environment: azureml://registries/azureml/environments/responsibleai-vision/versions/15\n", - "command: >-\n", - " python training_script.py\n", - " --model_base_name ${{{{inputs.model_base_name}}}}\n", - " --model_name_suffix ${{{{inputs.model_name_suffix}}}}\n", - " --device ${{{{inputs.device}}}}\n", - " --model_output_path ${{{{outputs.model_output_path}}}}\n", - "\"\"\"\n", - "\n", - "yaml_filename = \"FridgeVisionTrainingComp.yaml\"\n", - "\n", - "with open(yaml_filename, \"w\") as f:\n", - " f.write(yaml_contents)\n", - "\n", - "train_component_definition = load_component(source=yaml_filename)\n", - "\n", - "ml_client.components.create_or_update(train_component_definition)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "6d165e2b", - "metadata": {}, - "source": [ - "We need a compute target on which to run our jobs. The following checks whether the compute specified above is present; if not, then the compute target is created." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1e40fc38", - "metadata": {}, - "outputs": [], - "source": [ - "from azure.ai.ml.entities import AmlCompute\n", - "\n", - "all_compute_names = [x.name for x in ml_client.compute.list()]\n", - "\n", - "if compute_name in all_compute_names:\n", - " print(f\"Found existing compute: {compute_name}\")\n", - "else:\n", - " my_compute = AmlCompute(\n", - " name=compute_name,\n", - " size=\"STANDARD_DS3_V2\",\n", - " min_instances=0,\n", - " max_instances=4,\n", - " idle_time_before_scale_down=3600,\n", - " )\n", - " ml_client.compute.begin_create_or_update(my_compute)\n", - " print(\"Initiated compute creation\")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "9d8eb868", - "metadata": {}, - "source": [ - "## Running a training pipeline\n", - "\n", - "Now that we have our training component, we can run it. We begin by generating a unique name for the model." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ad76242b", - "metadata": {}, - "outputs": [], - "source": [ - "import time\n", - "\n", - "model_base_name = \"fridge_model\"\n", - "model_name_suffix = \"12492\"\n", - "device = -1" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "d49615a7", - "metadata": {}, - "source": [ - "Next, we define our training pipeline. This has two components. The first is the training component which we defined above. The second is a component to register the model in AzureML:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "cb6c6cec", - "metadata": {}, - "outputs": [], - "source": [ - "from azure.ai.ml import dsl, Input\n", - "\n", - "train_model_component = ml_client.components.get(\n", - " name=\"rai_fridge_training_component\", version=rai_example_version_string\n", - ")\n", - "\n", - "\n", - "@dsl.pipeline(\n", - " compute=compute_name,\n", - " description=\"Register Model for RAI Fridge example\",\n", - " experiment_name=f\"RAI_Fridge_Example_Model_Training_{model_name_suffix}\",\n", - ")\n", - "def my_training_pipeline(model_base_name, model_name_suffix, device):\n", - " trained_model = train_component_definition(\n", - " model_base_name=model_base_name,\n", - " model_name_suffix=model_name_suffix,\n", - " device=device,\n", - " )\n", - " trained_model.set_limits(timeout=3600)\n", - "\n", - " return {}\n", - "\n", - "\n", - "model_registration_pipeline_job = my_training_pipeline(\n", - " model_base_name, model_name_suffix, device\n", - ")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "2fa66ea6", - "metadata": {}, - "source": [ - "With the training pipeline defined, we can submit it for execution in AzureML. We define a helper function to wait for the job to complete:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f854eef5", - "metadata": {}, - "outputs": [], - "source": [ - "from azure.ai.ml.entities import PipelineJob\n", - "\n", - "\n", - "def submit_and_wait(ml_client, pipeline_job) -> PipelineJob:\n", - " created_job = ml_client.jobs.create_or_update(pipeline_job)\n", - " assert created_job is not None\n", - "\n", - " while created_job.status not in [\n", - " \"Completed\",\n", - " \"Failed\",\n", - " \"Canceled\",\n", - " \"NotResponding\",\n", - " ]:\n", - " time.sleep(30)\n", - " created_job = ml_client.jobs.get(created_job.name)\n", - " print(\"Latest status : {0}\".format(created_job.status))\n", - " assert created_job.status == \"Completed\"\n", - " return created_job\n", - "\n", - "\n", - "# This is the actual submission\n", - "training_job = submit_and_wait(ml_client, model_registration_pipeline_job)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "0722395e", - "metadata": {}, - "source": [ - "## Creating the RAI Vision Insights\n", - "\n", - "Now that we have our model, we can generate RAI Vision insights for it. We will need the `id` of the registered model, which will be as follows:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7d3e6e6e", - "metadata": {}, - "outputs": [], - "source": [ - "expected_model_id = f\"{model_base_name}_{model_name_suffix}:1\"\n", - "azureml_model_id = f\"azureml:{expected_model_id}\"" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "310aa659", - "metadata": {}, - "source": [ - "Next, we load the RAI components, so that we can construct a pipeline:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d67b942e", - "metadata": {}, - "outputs": [], - "source": [ - "fridge_test_mltable = Input(\n", - " type=\"mltable\",\n", - " path=f\"{input_test_data}:{rai_example_version_string}\",\n", - " mode=\"download\",\n", - ")\n", - "\n", - "registry_name = \"azureml\"\n", - "credential = DefaultAzureCredential()\n", - "\n", - "ml_client_registry = MLClient(\n", - " credential=credential,\n", - " subscription_id=ml_client.subscription_id,\n", - " resource_group_name=ml_client.resource_group_name,\n", - " registry_name=registry_name,\n", - ")\n", - "\n", - "rai_vision_insights_component = ml_client_registry.components.get(\n", - " name=\"rai_vision_insights\", version=version_string\n", - ")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "c98cd2d9", - "metadata": {}, - "source": [ - "We can now specify our pipeline. Complex objects (such as lists of column names) have to be converted to JSON strings before being passed to the components." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a62105a7", - "metadata": {}, - "outputs": [], - "source": [ - "import json\n", - "from azure.ai.ml import Input\n", - "from azure.ai.ml.constants import AssetTypes\n", - "\n", - "\n", - "@dsl.pipeline(\n", - " compute=compute_name,\n", - " description=\"Example RAI computation on Fridge data\",\n", - " experiment_name=f\"RAI_Fridge_Example_RAIInsights_Computation_{model_name_suffix}\",\n", - ")\n", - "def rai_fridge_image_classification_pipeline(\n", - " target_column_name,\n", - " test_data,\n", - " classes,\n", - " use_model_dependency,\n", - "):\n", - " # Initiate the RAIInsights\n", - " rai_image_job = rai_vision_insights_component(\n", - " task_type=\"image_classification\",\n", - " model_info=expected_model_id,\n", - " model_input=Input(type=AssetTypes.MLFLOW_MODEL, path=azureml_model_id),\n", - " test_dataset=test_data,\n", - " target_column_name=target_column_name,\n", - " classes=classes,\n", - " model_type=\"fastai\",\n", - " use_model_dependency=use_model_dependency,\n", - " )\n", - " rai_image_job.set_limits(timeout=7200)\n", - "\n", - " rai_image_job.outputs.dashboard.mode = \"upload\"\n", - " rai_image_job.outputs.ux_json.mode = \"upload\"\n", - "\n", - " return {\n", - " \"dashboard\": rai_image_job.outputs.dashboard,\n", - " \"ux_json\": rai_image_job.outputs.ux_json,\n", - " }" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "6b5b14a9", - "metadata": {}, - "source": [ - "Next, we define the pipeline object itself, and ensure that the outputs will be available for download:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e4d86ec2", - "metadata": {}, - "outputs": [], - "source": [ - "import uuid\n", - "from azure.ai.ml import Output\n", - "\n", - "insights_pipeline_job = rai_fridge_image_classification_pipeline(\n", - " target_column_name=target_column_name,\n", - " test_data=fridge_test_mltable,\n", - " classes=\"[]\",\n", - " use_model_dependency=True,\n", - ")\n", - "\n", - "rand_path = str(uuid.uuid4())\n", - "insights_pipeline_job.outputs.dashboard = Output(\n", - " path=f\"azureml://datastores/workspaceblobstore/paths/{rand_path}/dashboard/\",\n", - " mode=\"upload\",\n", - " type=\"uri_folder\",\n", - ")\n", - "insights_pipeline_job.outputs.ux_json = Output(\n", - " path=f\"azureml://datastores/workspaceblobstore/paths/{rand_path}/ux_json/\",\n", - " mode=\"upload\",\n", - " type=\"uri_folder\",\n", - ")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "25f34573", - "metadata": {}, - "source": [ - "And submit the pipeline to AzureML for execution:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2ca757f7", - "metadata": {}, - "outputs": [], - "source": [ - "insights_job = submit_and_wait(ml_client, insights_pipeline_job)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "1381768a", - "metadata": {}, - "source": [ - "The dashboard should appear in the AzureML portal in the registered model view. The following cell computes the expected URI:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e86ab611", - "metadata": {}, - "outputs": [], - "source": [ - "sub_id = ml_client._operation_scope.subscription_id\n", - "rg_name = ml_client._operation_scope.resource_group_name\n", - "ws_name = ml_client.workspace_name\n", - "\n", - "expected_uri = f\"https://ml.azure.com/model/{expected_model_id}/model_analysis?wsid=/subscriptions/{sub_id}/resourcegroups/{rg_name}/workspaces/{ws_name}\"\n", - "\n", - "print(f\"Please visit {expected_uri} to see your analysis\")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "93a8dff9", - "metadata": {}, - "source": [ - "## Constructing the pipeline in YAML\n", - "\n", - "It is also possible to specify the pipeline as a YAML file, and submit that using the command line. We will now create a YAML specification of the above pipeline and submit that:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "624bb0cd", - "metadata": {}, - "outputs": [], - "source": [ - "yaml_contents = f\"\"\"\n", - "$schema: https://azuremlschemas.azureedge.net/latest/pipelineJob.schema.json\n", - "experiment_name: AML_RAI_Vision_Sample_{rai_example_version_string}\n", - "type: pipeline\n", - "\n", - "compute: azureml:cpucluster\n", - "\n", - "inputs:\n", - " registered_model_name: fridge_model\n", - " fridge_model_info: {expected_model_id}\n", - " my_test_data:\n", - " type: mltable\n", - " path: azureml:{input_test_data}:{rai_example_version_string}\n", - " mode: download\n", - "\n", - "settings:\n", - " default_datastore: azureml:workspaceblobstore\n", - " default_compute: azureml:cpucluster\n", - " continue_on_step_failure: false\n", - "\n", - "jobs:\n", - " analyse_model:\n", - " type: command\n", - " component: azureml://registries/azureml/components/rai_vision_insights/versions/{version_string}\n", - " inputs:\n", - " task_type: image_classification\n", - " model_input:\n", - " type: mlflow_model\n", - " path: {azureml_model_id}\n", - " model_info: ${{{{parent.inputs.fridge_model_info}}}}\n", - " test_dataset: ${{{{parent.inputs.my_test_data}}}}\n", - " target_column_name: {target_column_name}\n", - " maximum_rows_for_test_dataset: 5000\n", - " classes: '[]'\n", - "\"\"\"\n", - "\n", - "yaml_pipeline_filename = \"rai_vision_example.yaml\"\n", - "\n", - "with open(yaml_pipeline_filename, \"w\") as f:\n", - " f.write(yaml_contents)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "1fd5f2dd", - "metadata": {}, - "source": [ - "The created file can then be submitted using the Azure CLI:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3bf9bb1c", - "metadata": {}, - "outputs": [], - "source": [ - "cmd_line = [\n", - " \"az\",\n", - " \"ml\",\n", - " \"job\",\n", - " \"create\",\n", - " \"--resource-group\",\n", - " rg_name,\n", - " \"--workspace\",\n", - " ws_name,\n", - " \"--file\",\n", - " yaml_pipeline_filename,\n", - "]\n", - "\n", - "import subprocess\n", - "\n", - "try:\n", - " cmd = subprocess.run(cmd_line, check=True, shell=True, capture_output=True)\n", - "except subprocess.CalledProcessError as cpe:\n", - " print(f\"Error invoking: {cpe.args}\")\n", - " print(cpe.stdout)\n", - " print(cpe.stderr)\n", - " raise\n", - "else:\n", - " print(\"Azure CLI submission completed\")" - ] - } - ], - "metadata": { - "celltoolbar": "Raw Cell Format", - "kernelspec": { - "display_name": "Python 3.10 - SDK V2", - "language": "python", - "name": "python310-sdkv2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.15" - }, - "vscode": { - "interpreter": { - "hash": "8fd340b5477ca1a0b454d48a3973beff39fee032ada47a04f6f3725b469a8988" - } - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/sdk/python/responsible-ai/vision/responsibleaidashboard-image-flower-classification.ipynb b/sdk/python/responsible-ai/vision/responsibleaidashboard-image-flower-classification.ipynb deleted file mode 100644 index d07b12b4c1..0000000000 --- a/sdk/python/responsible-ai/vision/responsibleaidashboard-image-flower-classification.ipynb +++ /dev/null @@ -1,1325 +0,0 @@ -{ - "cells": [ - { - "attachments": {}, - "cell_type": "markdown", - "id": "98605bcd", - "metadata": {}, - "source": [ - "# Image Classification scenario with RAI Dashboard\n", - "\n", - "The Flower dataset classifies images into 5 distinct flower categories, each commonly found in the United Kingdom.. This example notebook demonstrates how to use a fine-tuned fastai computer vision model on the dataset to evaluate the model in AzureML.\n", - "\n", - "First, we need to specify the version of the RAI components which are available in the workspace. This was specified when the components were uploaded." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "53b4eeac", - "metadata": { - "gather": { - "logged": 1700481891472 - } - }, - "outputs": [], - "source": [ - "%pip install azure-ai-ml\n", - "%pip install fastai" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f035b8eb", - "metadata": {}, - "outputs": [], - "source": [ - "version_string = \"0.0.21\"" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "06008690", - "metadata": {}, - "source": [ - "We also need to give the name of the compute cluster we want to use in AzureML. Later in this notebook, we will create it if it does not already exist:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f1ad79f9", - "metadata": { - "gather": { - "logged": 1700481893606 - } - }, - "outputs": [], - "source": [ - "compute_name = \"ResponsibleAI\"" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "9fc65dc7", - "metadata": {}, - "source": [ - "Finally, we need to specify a version for the data and components we will create while running this notebook. This should be unique for the workspace, but the specific value doesn't matter:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "78053935", - "metadata": { - "gather": { - "logged": 1700481895693 - } - }, - "outputs": [], - "source": [ - "rai_example_version_string = \"14\"" - ] - }, - { - "cell_type": "markdown", - "id": "0ba978b3-3779-4561-a3cd-0704db497a91", - "metadata": { - "nteract": { - "transient": { - "deleting": false - } - } - }, - "source": [ - "# 1. Connect to Azure Machine Learning Workspace\n", - "\n", - "The [workspace](https://docs.microsoft.com/en-us/azure/machine-learning/concept-workspace) is the top-level resource for Azure Machine Learning, providing a centralized place to work with all the artifacts you create when you use Azure Machine Learning. In this section we will connect to the workspace in which the job will be run." - ] - }, - { - "cell_type": "markdown", - "id": "8123a6d4-bb8c-4f4d-ba7f-b04cc27b675a", - "metadata": { - "nteract": { - "transient": { - "deleting": false - } - } - }, - "source": [ - "First, we need to upload the datasets to our workspace. We start by creating an `MLClient` for interactions with AzureML:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3c18eaeb-82d9-412f-b86e-305e509fceb1", - "metadata": { - "gather": { - "logged": 1700481898493 - }, - "jupyter": { - "outputs_hidden": false, - "source_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - } - }, - "outputs": [], - "source": [ - "# Enter details of your AML workspace\n", - "subscription_id = \"\"\n", - "resource_group = \"\"\n", - "workspace = \"\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2bd9adab-57a2-4ac5-90a0-e63abde900bb", - "metadata": { - "gather": { - "logged": 1700481901485 - }, - "jupyter": { - "outputs_hidden": false, - "source_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - } - }, - "outputs": [], - "source": [ - "# Handle to the workspace\n", - "from azure.ai.ml import MLClient\n", - "from azure.identity import DefaultAzureCredential\n", - "\n", - "credential = DefaultAzureCredential()\n", - "ml_client = MLClient(\n", - " credential=credential,\n", - " subscription_id=subscription_id,\n", - " resource_group_name=resource_group,\n", - " workspace_name=workspace,\n", - ")\n", - "\n", - "print(ml_client)" - ] - }, - { - "cell_type": "markdown", - "id": "68d97c28-e7e6-401f-90cb-976ad7b67c13", - "metadata": { - "nteract": { - "transient": { - "deleting": false - } - } - }, - "source": [ - "We can now upload the data to AzureML:" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "73be2b63", - "metadata": {}, - "source": [ - "# 2. Accessing the Data\n", - "\n", - "We supply the data as a pair of parquet files and accompanying `MLTable` file. We can download them, preprocess them, and take a brief look:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5f875f18", - "metadata": { - "gather": { - "logged": 1700481905894 - } - }, - "outputs": [], - "source": [ - "import os\n", - "import pandas as pd\n", - "import json\n", - "\n", - "try:\n", - " from urllib import urlretrieve\n", - "except ImportError:\n", - " from urllib.request import urlretrieve\n", - "\n", - "from zipfile import ZipFile" - ] - }, - { - "cell_type": "markdown", - "id": "83f65ca9-d112-4e47-8420-f7cc2e7becf6", - "metadata": { - "nteract": { - "transient": { - "deleting": false - } - } - }, - "source": [ - "## 2.1 Download Data" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1742abf4-d2f9-490a-9eaa-173fe91245e0", - "metadata": { - "gather": { - "logged": 1700481925085 - }, - "jupyter": { - "outputs_hidden": false, - "source_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - } - }, - "outputs": [], - "source": [ - "import os\n", - "import urllib\n", - "from zipfile import ZipFile\n", - "\n", - "# Change to a different location if you prefer\n", - "dataset_parent_dir = \"./data\"\n", - "\n", - "# create data folder if it doesnt exist.\n", - "os.makedirs(dataset_parent_dir, exist_ok=True)\n", - "\n", - "# download data\n", - "download_url = \"https://publictestdatasets.blob.core.windows.net/data/image_test_classification.zip\"\n", - "\n", - "# Extract current dataset name from dataset url\n", - "dataset_name = os.path.split(download_url)[-1].split(\".\")[0]\n", - "# Get dataset path for later use\n", - "dataset_dir = os.path.join(dataset_parent_dir, dataset_name)\n", - "os.makedirs(dataset_dir, exist_ok=True)\n", - "\n", - "# Get the data zip file path\n", - "data_file = os.path.join(dataset_parent_dir, f\"{dataset_name}.zip\")\n", - "\n", - "# Download the dataset\n", - "urllib.request.urlretrieve(download_url, filename=data_file)\n", - "\n", - "# extract files\n", - "with ZipFile(data_file, \"r\") as zip:\n", - " print(\"extracting files...\")\n", - " zip.extractall(path=dataset_dir)\n", - " print(\"done\")\n", - "\n", - "\n", - "# delete zip file\n", - "os.remove(data_file)" - ] - }, - { - "cell_type": "markdown", - "id": "579ed90b-58c0-4040-99aa-8faeff1e4cbe", - "metadata": { - "nteract": { - "transient": { - "deleting": false - } - } - }, - "source": [ - "## 2.2. Upload the images to Datastore through an AML Data asset (URI Folder) for training an AutomatedML Model\n", - "\n", - "In order to use the data for training in Azure ML, we upload it to our default Azure Blob Storage of our Azure ML Workspace.\n", - "\n", - "Reference to URI FOLDER data asset example for further details: https://github.com/Azure/azureml-examples/blob/samuel100/data-samples/sdk/assets/data/data.ipynb" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f130f65c-d11b-4db4-b9a9-ce4f89828892", - "metadata": { - "gather": { - "logged": 1700481970331 - }, - "jupyter": { - "outputs_hidden": false, - "source_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - } - }, - "outputs": [], - "source": [ - "# Uploading image files by creating a 'data asset URI FOLDER':\n", - "\n", - "from azure.ai.ml.entities import Data\n", - "from azure.ai.ml.constants import AssetTypes, InputOutputModes\n", - "from azure.ai.ml import Input\n", - "\n", - "input_test_data_folder = \"image-classification-flowers-new\"\n", - "\n", - "try:\n", - " uri_folder_data_asset = ml_client.data.get(\n", - " name=input_test_data_folder,\n", - " version=rai_example_version_string,\n", - " )\n", - "except Exception:\n", - " my_data = Data(\n", - " path=dataset_dir,\n", - " type=AssetTypes.URI_FOLDER,\n", - " description=\"Images of Flowers image classification\",\n", - " name=input_test_data_folder,\n", - " version=rai_example_version_string,\n", - " )\n", - "\n", - " uri_folder_data_asset = ml_client.data.create_or_update(my_data)\n", - "\n", - "print(uri_folder_data_asset)\n", - "print(\"\")\n", - "print(\"Path to folder in Blob Storage:\")\n", - "print(uri_folder_data_asset.path)" - ] - }, - { - "cell_type": "markdown", - "id": "4a3c7dd0-0a2f-4154-9927-dc0dc6c7807b", - "metadata": { - "nteract": { - "transient": { - "deleting": false - } - } - }, - "source": [ - "## 2.3. Convert the downloaded data to JSONL\n", - "\n", - "In this example, the fridge object dataset is stored in a directory. There are four different folders inside:\n", - "- /daisy\n", - "- /dandelion\n", - "- /rose\n", - "- /sunflower\n", - "- /tulip\n", - "\n", - "This is the most common data format for multiclass image classification. Each folder title corresponds to the image label for the images contained inside. In order to use this data to create an AzureML MLTable, we first need to convert it to the required JSONL format. Please refer to the [documentation on how to prepare datasets](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-prepare-datasets-for-automl-images).\n", - "\n", - "\n", - "The following script is creating a .jsonl file in the corresponding MLTable folder. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "fb3520cc-bc32-49d0-9200-f1f446b7313e", - "metadata": { - "gather": { - "logged": 1700481975389 - }, - "jupyter": { - "outputs_hidden": false, - "source_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - } - }, - "outputs": [], - "source": [ - "import json\n", - "import os\n", - "\n", - "# We'll copy each JSONL file within its related MLTable folder\n", - "validation_mltable_path = os.path.join(dataset_parent_dir, \"validation-mltable-folder\")\n", - "\n", - "# First, let's create the folder if it doesn't exist\n", - "os.makedirs(validation_mltable_path, exist_ok=True)\n", - "\n", - "# Path to the combined JSONL file\n", - "validation_annotations_file = os.path.join(\n", - " validation_mltable_path, \"validation_annotations.jsonl\"\n", - ")\n", - "\n", - "# Baseline of json line dictionary\n", - "json_line_sample = {\n", - " \"image_url\": uri_folder_data_asset.path,\n", - " \"label\": \"\",\n", - "}\n", - "\n", - "# Scan each subdirectory and generate a jsonl line per image, combined in a single JSONL file\n", - "with open(validation_annotations_file, \"w\") as combined_f:\n", - " for class_name in os.listdir(dataset_dir):\n", - " sub_dir = os.path.join(dataset_dir, class_name)\n", - " if not os.path.isdir(sub_dir):\n", - " continue\n", - "\n", - " # Scan each subdirectory\n", - " print(f\"Parsing {sub_dir}\")\n", - " for image in os.listdir(sub_dir):\n", - " if image.endswith(\".jpg\"):\n", - " json_line = dict(json_line_sample)\n", - " json_line[\"image_url\"] += f\"{class_name}/{image}\"\n", - " json_line[\"label\"] = class_name\n", - "\n", - " # Write each json line to the combined JSONL file\n", - " combined_f.write(json.dumps(json_line) + \"\\n\")" - ] - }, - { - "cell_type": "markdown", - "id": "efacc176-87a0-433c-9d45-e8384759926e", - "metadata": { - "nteract": { - "transient": { - "deleting": false - } - } - }, - "source": [ - "## 2.4. Create MLTable data input for training an AutomatedML Model\n", - "\n", - "Create MLTable data input using the jsonl files created above.\n", - "\n", - "For documentation on creating your own MLTable assets for jobs beyond this notebook, please refer to below resources\n", - "- [MLTable YAML Schema](https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-mltable) - covers how to write MLTable YAML, which is required for each MLTable asset.\n", - "- [Create MLTable data asset](https://learn.microsoft.com/en-us/azure/machine-learning/how-to-create-data-assets?tabs=Python-SDK#create-a-mltable-data-asset) - covers how to create mltable data asset. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ec5e326f-003a-44db-becc-2f3a2d68947a", - "metadata": { - "gather": { - "logged": 1700481980484 - }, - "jupyter": { - "outputs_hidden": false, - "source_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - } - }, - "outputs": [], - "source": [ - "def create_ml_table_file(filename):\n", - " return (\n", - " \"$schema: https://azureml/sdk-2-0/MLTable.json\\n\"\n", - " \"type: mltable\\n\"\n", - " \"paths:\\n\"\n", - " \" - file: ./{0}\\n\"\n", - " \"transformations:\\n\"\n", - " \" - read_json_lines:\\n\"\n", - " \" encoding: utf8\\n\"\n", - " \" invalid_lines: error\\n\"\n", - " \" include_path_column: false\\n\"\n", - " \" - convert_column_types:\\n\"\n", - " \" - columns: image_url\\n\"\n", - " \" column_type: stream_info\"\n", - " ).format(filename)\n", - "\n", - "\n", - "def save_ml_table_file(output_path, mltable_file_contents):\n", - " with open(os.path.join(output_path, \"MLTable\"), \"w\") as f:\n", - " f.write(mltable_file_contents)\n", - "\n", - "\n", - "# Create and save validation mltable\n", - "validation_mltable_file_contents = create_ml_table_file(\n", - " os.path.basename(validation_annotations_file)\n", - ")\n", - "save_ml_table_file(validation_mltable_path, validation_mltable_file_contents)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "a2c4ebb4", - "metadata": {}, - "source": [ - "Load some data for a quick view:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c32fdb88-3a17-468b-aaea-df71dad5b251", - "metadata": { - "gather": { - "logged": 1700482011206 - }, - "jupyter": { - "outputs_hidden": false, - "source_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - } - }, - "outputs": [], - "source": [ - "import mltable\n", - "\n", - "tbl = mltable.load(validation_mltable_path)\n", - "test_df: pd.DataFrame = tbl.to_pandas_dataframe()\n", - "\n", - "display(test_df)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "1115ac59", - "metadata": {}, - "source": [ - "The label columns contain the classes:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5b42df3d", - "metadata": { - "gather": { - "logged": 1700482044985 - } - }, - "outputs": [], - "source": [ - "target_column_name = \"label\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "62eb02a2", - "metadata": { - "gather": { - "logged": 1700482188622 - } - }, - "outputs": [], - "source": [ - "from azure.ai.ml.entities import Data\n", - "from azure.ai.ml.constants import AssetTypes\n", - "\n", - "input_test_data = \"Flower_classification_new_MLTable_1\"\n", - "\n", - "try:\n", - " test_data = ml_client.data.get(\n", - " name=input_test_data,\n", - " version=rai_example_version_string,\n", - " )\n", - "except Exception:\n", - " test_data = Data(\n", - " path=validation_mltable_path,\n", - " type=AssetTypes.MLTABLE,\n", - " description=\"RAI Flower classification test data\",\n", - " name=input_test_data,\n", - " version=rai_example_version_string,\n", - " )\n", - " ml_client.data.create_or_update(test_data)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "6815ba75", - "metadata": {}, - "source": [ - "# 3. Creating the Model\n", - "\n", - "A restnet50 model trained on approximately 3000 images from the 5 classes is used in this scenario.\n", - "\n", - "To simplify the model creation process, we're going to use a pipeline. We create a directory for the training script:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e78d869b", - "metadata": { - "gather": { - "logged": 1700482191940 - } - }, - "outputs": [], - "source": [ - "import os\n", - "\n", - "os.makedirs(\"component_src\", exist_ok=True)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "ea86e55d", - "metadata": {}, - "source": [ - "Next, we write out our script to retrieve the trained model:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a523f144", - "metadata": {}, - "outputs": [], - "source": [ - "%%writefile component_src/training_script.py\n", - "\n", - "import argparse\n", - "import logging\n", - "import json\n", - "import os\n", - "import time\n", - "from zipfile import ZipFile\n", - "\n", - "import mlflow\n", - "import mlflow.pyfunc\n", - "\n", - "from azureml.core import Run\n", - "\n", - "from fastai.learner import load_learner\n", - "\n", - "from raiutils.common.retries import retry_function\n", - "\n", - "try:\n", - " from urllib import urlretrieve\n", - "except ImportError:\n", - " from urllib.request import urlretrieve\n", - "\n", - "_logger = logging.getLogger(__file__)\n", - "logging.basicConfig(level=logging.INFO)\n", - "\n", - "MODEL_NAME = \"flower_classification_model.pkl\"\n", - "\n", - "def parse_args():\n", - " # setup arg parser\n", - " parser = argparse.ArgumentParser()\n", - "\n", - " # add arguments\n", - " parser.add_argument(\n", - " \"--model_output_path\", type=str, help=\"Path to write model info JSON\"\n", - " )\n", - " parser.add_argument(\n", - " \"--model_base_name\", type=str, help=\"Name of the registered model\"\n", - " )\n", - " parser.add_argument(\n", - " \"--model_name_suffix\", type=int, help=\"Set negative to use epoch_secs\"\n", - " )\n", - " parser.add_argument(\n", - " \"--device\", type=int, help=(\n", - " \"Device for CPU/GPU supports. Setting this to -1 will leverage \"\n", - " \"CPU, >=0 will run the model on the associated CUDA device id.\")\n", - " )\n", - "\n", - " # parse args\n", - " args = parser.parse_args()\n", - "\n", - " # return args\n", - " return args\n", - "\n", - "\n", - "class FetchModel(object):\n", - " def __init__(self):\n", - " pass\n", - "\n", - " def fetch(self):\n", - " # Blobstore link to the trained model\n", - " url = (\"https://publictestdatasets.blob.core.windows.net/models/\"+\n", - " \"flower_classification_model.zip\")\n", - " urlretrieve(url, filename=\"flower_classification_model.zip\")\n", - " data_file = os.path.join(\"./\", \"flower_classification_model.zip\")\n", - "\n", - " with ZipFile(data_file, \"r\") as zip:\n", - " zip.extractall(path=\"./\")\n", - " \n", - " os.remove(data_file)\n", - "\n", - "\n", - "def main(args):\n", - " current_experiment = Run.get_context().experiment\n", - " tracking_uri = current_experiment.workspace.get_mlflow_tracking_uri()\n", - " _logger.info(\"tracking_uri: {0}\".format(tracking_uri))\n", - " mlflow.set_tracking_uri(tracking_uri)\n", - " mlflow.set_experiment(current_experiment.name)\n", - "\n", - " _logger.info(\"Getting device\")\n", - " device = args.device\n", - "\n", - " _logger.info(\"Loading parquet input\")\n", - "\n", - " # Load the fastai model\n", - " fetcher = FetchModel()\n", - " action_name = \"Model download\"\n", - " err_msg = \"Failed to download model\"\n", - " max_retries = 4\n", - " retry_delay = 60\n", - " retry_function(fetcher.fetch, action_name, err_msg,\n", - " max_retries=max_retries,\n", - " retry_delay=retry_delay)\n", - " model = load_learner(MODEL_NAME)\n", - "\n", - " if device >= 0:\n", - " model = model.cuda()\n", - "\n", - " if args.model_name_suffix < 0:\n", - " suffix = int(time.time())\n", - " else:\n", - " suffix = args.model_name_suffix\n", - " registered_name = \"{0}_{1}\".format(args.model_base_name, suffix)\n", - " _logger.info(f\"Registering model as {registered_name}\")\n", - "\n", - " # Saving model with mlflow\n", - " _logger.info(\"Saving with mlflow\")\n", - "\n", - " mlflow.fastai.log_model(\n", - " model,\n", - " artifact_path=registered_name,\n", - " registered_model_name=registered_name\n", - " )\n", - "\n", - " _logger.info(\"Writing JSON\")\n", - " dict = {\"id\": \"{0}:1\".format(registered_name)}\n", - " output_path = os.path.join(args.model_output_path, \"model_info.json\")\n", - " with open(output_path, \"w\") as of:\n", - " json.dump(dict, fp=of)\n", - "\n", - "\n", - "# run script\n", - "if __name__ == \"__main__\":\n", - " # add space in logs\n", - " print(\"*\" * 60)\n", - " print(\"\\n\\n\")\n", - "\n", - " # parse args\n", - " args = parse_args()\n", - "\n", - " # run main function\n", - " main(args)\n", - "\n", - " # add space in logs\n", - " print(\"*\" * 60)\n", - " print(\"\\n\\n\")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "e115dd6e", - "metadata": {}, - "source": [ - "Now, we can build this into an AzureML component:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3d54e43f", - "metadata": { - "gather": { - "logged": 1700482292209 - } - }, - "outputs": [], - "source": [ - "from azure.ai.ml import load_component\n", - "\n", - "yaml_contents = f\"\"\"\n", - "$schema: http://azureml/sdk-2-0/CommandComponent.json\n", - "name: rai_flower_classification_training_component_new_1\n", - "display_name: RAI Flower classification training component\n", - "version: {rai_example_version_string}\n", - "type: command\n", - "inputs:\n", - " model_base_name:\n", - " type: string\n", - " model_name_suffix: # Set negative to use epoch_secs\n", - " type: integer\n", - " default: -1\n", - " device: # set to >= 0 to use GPU\n", - " type: integer\n", - " default: 0\n", - "outputs:\n", - " model_output_path:\n", - " type: path\n", - "code: ./component_src/\n", - "environment: azureml://registries/azureml/environments/responsibleai-vision/versions/15\n", - "command: >-\n", - " python training_script.py\n", - " --model_base_name ${{{{inputs.model_base_name}}}}\n", - " --model_name_suffix ${{{{inputs.model_name_suffix}}}}\n", - " --device ${{{{inputs.device}}}}\n", - " --model_output_path ${{{{outputs.model_output_path}}}}\n", - "\"\"\"\n", - "\n", - "yaml_filename = \"TrainingComponent.yaml\"\n", - "\n", - "with open(yaml_filename, \"w\") as f:\n", - " f.write(yaml_contents)\n", - "\n", - "train_component_definition = load_component(source=yaml_filename)\n", - "\n", - "ml_client.components.create_or_update(train_component_definition)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "6d165e2b", - "metadata": {}, - "source": [ - "We need a compute target on which to run our jobs. The following checks whether the compute specified above is present; if not, then the compute target is created." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1e40fc38", - "metadata": { - "gather": { - "logged": 1700482299156 - } - }, - "outputs": [], - "source": [ - "from azure.ai.ml.entities import AmlCompute\n", - "\n", - "all_compute_names = [x.name for x in ml_client.compute.list()]\n", - "\n", - "if compute_name in all_compute_names:\n", - " print(f\"Found existing compute: {compute_name}\")\n", - "else:\n", - " my_compute = AmlCompute(\n", - " name=compute_name,\n", - " size=\"STANDARD_DS13_V2\",\n", - " min_instances=0,\n", - " max_instances=4,\n", - " idle_time_before_scale_down=3600,\n", - " )\n", - " ml_client.compute.begin_create_or_update(my_compute)\n", - " print(\"Initiated compute creation\")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "9d8eb868", - "metadata": {}, - "source": [ - "## 3.1. Running a training pipeline\n", - "\n", - "Now that we have our training component, we can run it. We begin by generating a unique name for the model." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ad76242b", - "metadata": { - "gather": { - "logged": 1700482305489 - } - }, - "outputs": [], - "source": [ - "import time\n", - "\n", - "model_base_name = \"flower_classification_model\"\n", - "model_name_suffix = \"12492\"\n", - "device = -1" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "d49615a7", - "metadata": {}, - "source": [ - "Next, we define our training pipeline. This has two components. The first is the training component which we defined above. The second is a component to register the model in AzureML:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "cb6c6cec", - "metadata": { - "gather": { - "logged": 1700482313283 - } - }, - "outputs": [], - "source": [ - "from azure.ai.ml import dsl, Input\n", - "\n", - "train_model_component = ml_client.components.get(\n", - " name=\"rai_flower_classification_training_component_new_1\",\n", - " version=rai_example_version_string,\n", - ")\n", - "\n", - "\n", - "@dsl.pipeline(\n", - " compute=compute_name,\n", - " description=\"Register Model for RAI Flower classification\",\n", - " experiment_name=f\"RAI_Flower_classification_Model_Training_{model_name_suffix}\",\n", - ")\n", - "def my_training_pipeline(model_base_name, model_name_suffix, device):\n", - " trained_model = train_component_definition(\n", - " model_base_name=model_base_name,\n", - " model_name_suffix=model_name_suffix,\n", - " device=device,\n", - " )\n", - " trained_model.set_limits(timeout=7200)\n", - "\n", - " return {}\n", - "\n", - "\n", - "model_registration_pipeline_job = my_training_pipeline(\n", - " model_base_name, model_name_suffix, device\n", - ")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "2fa66ea6", - "metadata": {}, - "source": [ - "With the training pipeline defined, we can submit it for execution in AzureML. We define a helper function to wait for the job to complete:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "682cf6ab-fe46-45fd-8177-d1ceae06c6db", - "metadata": { - "gather": { - "logged": 1700482316296 - }, - "jupyter": { - "outputs_hidden": false, - "source_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - } - }, - "outputs": [], - "source": [ - "from azure.ai.ml.entities import PipelineJob\n", - "from IPython.core.display import HTML\n", - "from IPython.display import display\n", - "\n", - "\n", - "def submit_and_wait(ml_client, pipeline_job) -> PipelineJob:\n", - " created_job = ml_client.jobs.create_or_update(pipeline_job)\n", - " assert created_job is not None\n", - "\n", - " print(\"Pipeline job can be accessed in the following URL:\")\n", - " display(HTML('{0}'.format(created_job.studio_url)))\n", - "\n", - " while created_job.status not in [\n", - " \"Completed\",\n", - " \"Failed\",\n", - " \"Canceled\",\n", - " \"NotResponding\",\n", - " ]:\n", - " time.sleep(30)\n", - " created_job = ml_client.jobs.get(created_job.name)\n", - " print(\"Latest status : {0}\".format(created_job.status))\n", - " assert created_job.status == \"Completed\"\n", - " return created_job" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f854eef5", - "metadata": { - "gather": { - "logged": 1700482353473 - } - }, - "outputs": [], - "source": [ - "# This is the actual submission\n", - "training_job = submit_and_wait(ml_client, model_registration_pipeline_job)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "0722395e", - "metadata": {}, - "source": [ - "# 4. Creating the RAI Vision Insights\n", - "\n", - "Now that we have our model, we can generate RAI Vision insights for it. We will need the `id` of the registered model, which will be as follows:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7d3e6e6e", - "metadata": { - "gather": { - "logged": 1700482353767 - } - }, - "outputs": [], - "source": [ - "expected_model_id = f\"{model_base_name}_{model_name_suffix}:1\"\n", - "azureml_model_id = f\"azureml:{expected_model_id}\"" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "310aa659", - "metadata": {}, - "source": [ - "Next, we load the RAI components, so that we can construct a pipeline:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "cf25558f-72df-4bba-8543-a5d3d5bf1da0", - "metadata": { - "gather": { - "logged": 1700487342945 - }, - "jupyter": { - "outputs_hidden": false, - "source_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - } - }, - "outputs": [], - "source": [ - "test_mltable = Input(\n", - " type=\"mltable\",\n", - " path=f\"{input_test_data}:{rai_example_version_string}\",\n", - " mode=\"download\",\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d67b942e", - "metadata": { - "gather": { - "logged": 1700487346200 - } - }, - "outputs": [], - "source": [ - "registry_name = \"azureml\"\n", - "credential = DefaultAzureCredential()\n", - "\n", - "ml_client_registry = MLClient(\n", - " credential=credential,\n", - " subscription_id=ml_client.subscription_id,\n", - " resource_group_name=ml_client.resource_group_name,\n", - " registry_name=registry_name,\n", - ")\n", - "\n", - "rai_vision_insights_component = ml_client_registry.components.get(\n", - " name=\"rai_vision_insights\", version=version_string\n", - ")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "c98cd2d9", - "metadata": {}, - "source": [ - "## 4.1 Constructing the pipeline in sdk\n", - "\n", - "We can now specify our pipeline. Complex objects (such as lists of column names) have to be converted to JSON strings before being passed to the components." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a62105a7", - "metadata": { - "gather": { - "logged": 1700487348739 - } - }, - "outputs": [], - "source": [ - "import json\n", - "from azure.ai.ml import Input\n", - "from azure.ai.ml.constants import AssetTypes\n", - "\n", - "\n", - "@dsl.pipeline(\n", - " compute=compute_name,\n", - " description=\"Example RAI computation on Flower classification\",\n", - " experiment_name=f\"RAI_Flower_classification_RAIInsights_Computation_{model_name_suffix}\",\n", - ")\n", - "def image_classification_pipeline(\n", - " target_column_name,\n", - " test_data,\n", - " classes,\n", - " use_model_dependency,\n", - "):\n", - " # Initiate the RAIInsights\n", - " rai_image_job = rai_vision_insights_component(\n", - " task_type=\"image_classification\",\n", - " model_info=expected_model_id,\n", - " model_input=Input(type=AssetTypes.MLFLOW_MODEL, path=azureml_model_id),\n", - " test_dataset=test_data,\n", - " target_column_name=target_column_name,\n", - " classes=classes,\n", - " model_type=\"fastai\",\n", - " dataset_type=\"private\",\n", - " use_model_dependency=use_model_dependency,\n", - " # Set this to true to be able to view explanations without computing\n", - " # them on demand - however, running the dashboard may take several hours\n", - " # for several hundred images.\n", - " precompute_explanation=False,\n", - " )\n", - " rai_image_job.set_limits(timeout=720000)\n", - "\n", - " rai_image_job.outputs.dashboard.mode = \"upload\"\n", - " rai_image_job.outputs.ux_json.mode = \"upload\"\n", - "\n", - " return {\n", - " \"dashboard\": rai_image_job.outputs.dashboard,\n", - " \"ux_json\": rai_image_job.outputs.ux_json,\n", - " }" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "6b5b14a9", - "metadata": {}, - "source": [ - "Next, we define the pipeline object itself, and ensure that the outputs will be available for download:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e4d86ec2", - "metadata": { - "gather": { - "logged": 1700487367354 - } - }, - "outputs": [], - "source": [ - "import uuid\n", - "from azure.ai.ml import Output\n", - "\n", - "insights_pipeline_job = image_classification_pipeline(\n", - " target_column_name=target_column_name,\n", - " test_data=test_mltable,\n", - " classes=\"[]\",\n", - " use_model_dependency=True,\n", - ")\n", - "\n", - "rand_path = str(uuid.uuid4())\n", - "insights_pipeline_job.outputs.dashboard = Output(\n", - " path=f\"azureml://datastores/workspaceblobstore/paths/{rand_path}/dashboard/\",\n", - " mode=\"upload\",\n", - " type=\"uri_folder\",\n", - ")\n", - "insights_pipeline_job.outputs.ux_json = Output(\n", - " path=f\"azureml://datastores/workspaceblobstore/paths/{rand_path}/ux_json/\",\n", - " mode=\"upload\",\n", - " type=\"uri_folder\",\n", - ")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "25f34573", - "metadata": {}, - "source": [ - "And submit the pipeline to AzureML for execution:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2ca757f7", - "metadata": { - "gather": { - "logged": 1700483346337 - } - }, - "outputs": [], - "source": [ - "insights_job = submit_and_wait(ml_client, insights_pipeline_job)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "1381768a", - "metadata": {}, - "source": [ - "The dashboard should appear in the AzureML portal in the registered model view. The following cell computes the expected URI:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e86ab611", - "metadata": { - "gather": { - "logged": 1700483150165 - } - }, - "outputs": [], - "source": [ - "sub_id = ml_client._operation_scope.subscription_id\n", - "rg_name = ml_client._operation_scope.resource_group_name\n", - "ws_name = ml_client.workspace_name\n", - "\n", - "expected_uri = f\"https://ml.azure.com/model/{expected_model_id}/model_analysis?wsid=/subscriptions/{sub_id}/resourcegroups/{rg_name}/workspaces/{ws_name}\"\n", - "\n", - "print(f\"Please visit {expected_uri} to see your analysis\")" - ] - } - ], - "metadata": { - "celltoolbar": "Raw Cell Format", - "kernel_info": { - "name": "python310-sdkv2" - }, - "kernelspec": { - "display_name": "Python 3.10 - SDK v2", - "language": "python", - "name": "python310-sdkv2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.11" - }, - "microsoft": { - "host": { - "AzureML": { - "notebookHasBeenCompleted": true - } - }, - "ms_spell_check": { - "ms_spell_check_language": "en" - } - }, - "nteract": { - "version": "nteract-front-end@1.0.0" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/sdk/python/responsible-ai/vision/responsibleaidashboard-image-multilabel-classification-fridge.ipynb b/sdk/python/responsible-ai/vision/responsibleaidashboard-image-multilabel-classification-fridge.ipynb deleted file mode 100644 index 974c82a30a..0000000000 --- a/sdk/python/responsible-ai/vision/responsibleaidashboard-image-multilabel-classification-fridge.ipynb +++ /dev/null @@ -1,999 +0,0 @@ -{ - "cells": [ - { - "attachments": {}, - "cell_type": "markdown", - "id": "98605bcd", - "metadata": {}, - "source": [ - "# Multilabel Image Classification scenario with RAI Dashboard\n", - "\n", - "The [fridge dataset](https://github.com/microsoft/computervision-recipes/tree/master/scenarios/classification) tags images with four types of items commonly found in the Microsoft New England R&D office refrigerator - carton, water bottle, can and milk bottle. This example notebook demonstrates how to use a fine-tuned fastai multilabel computer vision model on the dataset to evaluate the model in AzureML.\n", - "\n", - "First, we need to specify the version of the RAI components which are available in the workspace. This was specified when the components were uploaded." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "53b4eeac", - "metadata": {}, - "outputs": [], - "source": [ - "version_string = \"0.0.21\"" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "06008690", - "metadata": {}, - "source": [ - "We also need to give the name of the compute cluster we want to use in AzureML. Later in this notebook, we will create it if it does not already exist:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f1ad79f9", - "metadata": {}, - "outputs": [], - "source": [ - "compute_name = \"cpucluster\"" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "9fc65dc7", - "metadata": {}, - "source": [ - "Finally, we need to specify a version for the data and components we will create while running this notebook. This should be unique for the workspace, but the specific value doesn't matter:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "78053935", - "metadata": {}, - "outputs": [], - "source": [ - "rai_example_version_string = \"23\"" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "73be2b63", - "metadata": {}, - "source": [ - "## Accessing the Data\n", - "\n", - "We supply the data as a pair of parquet files and accompanying `MLTable` file. We can download them, preprocess them, and take a brief look:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5f875f18", - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import pandas as pd\n", - "import json\n", - "\n", - "try:\n", - " from urllib import urlretrieve\n", - "except ImportError:\n", - " from urllib.request import urlretrieve" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "8f9ce4ae", - "metadata": {}, - "source": [ - "First we download the fridge dataset:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0eac648c", - "metadata": {}, - "outputs": [], - "source": [ - "def download_fridge_dataset(data_path, annotations_file):\n", - " os.makedirs(data_path, exist_ok=True)\n", - "\n", - " # download data\n", - " base_url = \"https://publictestdatasets.blob.core.windows.net/\"\n", - " fridge_folder = \"computervision/multilabelFridgeObjects/\"\n", - "\n", - " data_url = base_url + fridge_folder + annotations_file\n", - " data_output_path = os.path.join(data_path, annotations_file)\n", - " urlretrieve(data_url, filename=data_output_path)\n", - "\n", - "\n", - "test_annotations = \"test_annotations.jsonl\"\n", - "\n", - "test_data_path = \"multilabel_fridge_test_data\"\n", - "\n", - "download_fridge_dataset(test_data_path, test_annotations)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "17d53df4", - "metadata": {}, - "source": [ - "Now create the mltable:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f39d2ea8", - "metadata": {}, - "outputs": [], - "source": [ - "def create_ml_table_file(filename):\n", - " return (\n", - " \"$schema: http://azureml/sdk-2-0/MLTable.json\\n\"\n", - " \"type: mltable\\n\"\n", - " \"paths:\\n\"\n", - " \" - file: ./{0}\\n\"\n", - " \"transformations:\\n\"\n", - " \" - read_json_lines:\\n\"\n", - " \" encoding: utf8\\n\"\n", - " \" invalid_lines: error\\n\"\n", - " \" include_path_column: false\\n\"\n", - " ).format(filename)\n", - "\n", - "\n", - "def save_ml_table_file(output_path, ml_table_data):\n", - " mltable_file_contents = create_ml_table_file(ml_table_data)\n", - " with open(os.path.join(output_path, \"MLTable\"), \"w\") as f:\n", - " f.write(mltable_file_contents)\n", - "\n", - "\n", - "save_ml_table_file(test_data_path, test_annotations)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "a2c4ebb4", - "metadata": {}, - "source": [ - "Load some data for a quick view:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1027fa92", - "metadata": {}, - "outputs": [], - "source": [ - "import mltable\n", - "\n", - "tbl = mltable.load(test_data_path)\n", - "test_df: pd.DataFrame = tbl.to_pandas_dataframe()\n", - "\n", - "display(test_df)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "1115ac59", - "metadata": {}, - "source": [ - "The label columns contain the classes:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5b42df3d", - "metadata": {}, - "outputs": [], - "source": [ - "target_column_name = [\"can\", \"carton\", \"milk_bottle\", \"water_bottle\"]\n", - "encoded_target_column_name = json.dumps(target_column_name)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "52e79b04", - "metadata": {}, - "source": [ - "First, we need to upload the datasets to our workspace. We start by creating an `MLClient` for interactions with AzureML:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f19acf3e", - "metadata": {}, - "outputs": [], - "source": [ - "# Enter details of your AML workspace\n", - "subscription_id = \"\"\n", - "resource_group = \"\"\n", - "workspace = \"\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "395435fc", - "metadata": {}, - "outputs": [], - "source": [ - "# Handle to the workspace\n", - "from azure.ai.ml import MLClient\n", - "from azure.identity import DefaultAzureCredential\n", - "\n", - "try:\n", - " credential = DefaultAzureCredential()\n", - " ml_client = MLClient(\n", - " credential=credential,\n", - " subscription_id=subscription_id,\n", - " resource_group_name=resource_group,\n", - " workspace_name=workspace,\n", - " )\n", - "except Exception:\n", - " # If in compute instance we can get the config automatically\n", - " from azureml.core import Workspace\n", - "\n", - " workspace = Workspace.from_config()\n", - " workspace.write_config()\n", - " ml_client = MLClient.from_config(\n", - " credential=DefaultAzureCredential(exclude_shared_token_cache_credential=True),\n", - " logging_enable=True,\n", - " )\n", - "\n", - "print(ml_client)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "7b501735", - "metadata": {}, - "source": [ - "We can now upload the data to AzureML:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "62eb02a2", - "metadata": {}, - "outputs": [], - "source": [ - "from azure.ai.ml.entities import Data\n", - "from azure.ai.ml.constants import AssetTypes\n", - "\n", - "input_test_data = \"Multilabel_Fridge_Test_MLTable\"\n", - "\n", - "try:\n", - " test_data = ml_client.data.get(\n", - " name=input_test_data,\n", - " version=rai_example_version_string,\n", - " )\n", - "except Exception:\n", - " test_data = Data(\n", - " path=test_data_path,\n", - " type=AssetTypes.MLTABLE,\n", - " description=\"RAI Multilabel Fridge test data\",\n", - " name=input_test_data,\n", - " version=rai_example_version_string,\n", - " )\n", - " ml_client.data.create_or_update(test_data)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "6815ba75", - "metadata": {}, - "source": [ - "# Creating the Model\n", - "\n", - "To simplify the model creation process, we're going to use a pipeline.\n", - "\n", - "We create a directory for the training script:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e78d869b", - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "\n", - "os.makedirs(\"fridge_component_src\", exist_ok=True)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "ea86e55d", - "metadata": {}, - "source": [ - "Next, we write out our script to retrieve the trained model:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a523f144", - "metadata": {}, - "outputs": [], - "source": [ - "%%writefile fridge_component_src/training_script.py\n", - "\n", - "import argparse\n", - "import logging\n", - "import json\n", - "import os\n", - "import time\n", - "\n", - "\n", - "import mlflow\n", - "import mlflow.pyfunc\n", - "\n", - "from azureml.core import Run\n", - "\n", - "from fastai.learner import load_learner\n", - "\n", - "from raiutils.common.retries import retry_function\n", - "\n", - "try:\n", - " from urllib import urlretrieve\n", - "except ImportError:\n", - " from urllib.request import urlretrieve\n", - "\n", - "_logger = logging.getLogger(__file__)\n", - "logging.basicConfig(level=logging.INFO)\n", - "\n", - "MULTILABEL_FRIDGE_MODEL_NAME = 'multilabel_fridge_model'\n", - "\n", - "\n", - "def parse_args():\n", - " # setup arg parser\n", - " parser = argparse.ArgumentParser()\n", - "\n", - " # add arguments\n", - " parser.add_argument(\n", - " \"--model_output_path\", type=str, help=\"Path to write model info JSON\"\n", - " )\n", - " parser.add_argument(\n", - " \"--model_base_name\", type=str, help=\"Name of the registered model\"\n", - " )\n", - " parser.add_argument(\n", - " \"--model_name_suffix\", type=int, help=\"Set negative to use epoch_secs\"\n", - " )\n", - " parser.add_argument(\n", - " \"--device\", type=int, help=(\n", - " \"Device for CPU/GPU supports. Setting this to -1 will leverage \"\n", - " \"CPU, >=0 will run the model on the associated CUDA device id.\")\n", - " )\n", - "\n", - " # parse args\n", - " args = parser.parse_args()\n", - "\n", - " # return args\n", - " return args\n", - "\n", - "\n", - "class FetchModel(object):\n", - " def __init__(self):\n", - " pass\n", - "\n", - " def fetch(self):\n", - " url = ('https://publictestdatasets.blob.core.windows.net/models/' +\n", - " MULTILABEL_FRIDGE_MODEL_NAME)\n", - " urlretrieve(url, MULTILABEL_FRIDGE_MODEL_NAME)\n", - "\n", - "\n", - "def main(args):\n", - " current_experiment = Run.get_context().experiment\n", - " tracking_uri = current_experiment.workspace.get_mlflow_tracking_uri()\n", - " _logger.info(\"tracking_uri: {0}\".format(tracking_uri))\n", - " mlflow.set_tracking_uri(tracking_uri)\n", - " mlflow.set_experiment(current_experiment.name)\n", - "\n", - " _logger.info(\"Getting device\")\n", - " device = args.device\n", - "\n", - " _logger.info(\"Loading parquet input\")\n", - "\n", - " # Load the fridge fastai model\n", - " fetcher = FetchModel()\n", - " action_name = \"Model download\"\n", - " err_msg = \"Failed to download model\"\n", - " max_retries = 4\n", - " retry_delay = 60\n", - " retry_function(fetcher.fetch, action_name, err_msg,\n", - " max_retries=max_retries,\n", - " retry_delay=retry_delay)\n", - " model = load_learner(MULTILABEL_FRIDGE_MODEL_NAME)\n", - "\n", - " if device >= 0:\n", - " model = model.cuda()\n", - "\n", - " if args.model_name_suffix < 0:\n", - " suffix = int(time.time())\n", - " else:\n", - " suffix = args.model_name_suffix\n", - " registered_name = \"{0}_{1}\".format(args.model_base_name, suffix)\n", - " _logger.info(f\"Registering model as {registered_name}\")\n", - "\n", - " # Saving model with mlflow\n", - " _logger.info(\"Saving with mlflow\")\n", - "\n", - " mlflow.fastai.log_model(\n", - " model,\n", - " artifact_path=registered_name,\n", - " registered_model_name=registered_name\n", - " )\n", - "\n", - " _logger.info(\"Writing JSON\")\n", - " dict = {\"id\": \"{0}:1\".format(registered_name)}\n", - " output_path = os.path.join(args.model_output_path, \"model_info.json\")\n", - " with open(output_path, \"w\") as of:\n", - " json.dump(dict, fp=of)\n", - "\n", - "\n", - "# run script\n", - "if __name__ == \"__main__\":\n", - " # add space in logs\n", - " print(\"*\" * 60)\n", - " print(\"\\n\\n\")\n", - "\n", - " # parse args\n", - " args = parse_args()\n", - "\n", - " # run main function\n", - " main(args)\n", - "\n", - " # add space in logs\n", - " print(\"*\" * 60)\n", - " print(\"\\n\\n\")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "e115dd6e", - "metadata": {}, - "source": [ - "Now, we can build this into an AzureML component:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3d54e43f", - "metadata": {}, - "outputs": [], - "source": [ - "from azure.ai.ml import load_component\n", - "\n", - "yaml_contents = f\"\"\"\n", - "$schema: http://azureml/sdk-2-0/CommandComponent.json\n", - "name: rai_multilabel_fridge_training_component\n", - "display_name: Multilabel Fridge training component for RAI example\n", - "version: {rai_example_version_string}\n", - "type: command\n", - "inputs:\n", - " model_base_name:\n", - " type: string\n", - " model_name_suffix: # Set negative to use epoch_secs\n", - " type: integer\n", - " default: -1\n", - " device: # set to >= 0 to use GPU\n", - " type: integer\n", - " default: 0\n", - "outputs:\n", - " model_output_path:\n", - " type: path\n", - "code: ./fridge_component_src/\n", - "environment: azureml://registries/azureml/environments/responsibleai-vision/versions/15\n", - "command: >-\n", - " python training_script.py\n", - " --model_base_name ${{{{inputs.model_base_name}}}}\n", - " --model_name_suffix ${{{{inputs.model_name_suffix}}}}\n", - " --device ${{{{inputs.device}}}}\n", - " --model_output_path ${{{{outputs.model_output_path}}}}\n", - "\"\"\"\n", - "\n", - "yaml_filename = \"FridgeMultilabelVisionTrainingComp.yaml\"\n", - "\n", - "with open(yaml_filename, \"w\") as f:\n", - " f.write(yaml_contents)\n", - "\n", - "train_component_definition = load_component(source=yaml_filename)\n", - "\n", - "ml_client.components.create_or_update(train_component_definition)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "6d165e2b", - "metadata": {}, - "source": [ - "We need a compute target on which to run our jobs. The following checks whether the compute specified above is present; if not, then the compute target is created." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1e40fc38", - "metadata": {}, - "outputs": [], - "source": [ - "from azure.ai.ml.entities import AmlCompute\n", - "\n", - "all_compute_names = [x.name for x in ml_client.compute.list()]\n", - "\n", - "if compute_name in all_compute_names:\n", - " print(f\"Found existing compute: {compute_name}\")\n", - "else:\n", - " my_compute = AmlCompute(\n", - " name=compute_name,\n", - " size=\"STANDARD_DS3_V2\",\n", - " min_instances=0,\n", - " max_instances=4,\n", - " idle_time_before_scale_down=3600,\n", - " )\n", - " ml_client.compute.begin_create_or_update(my_compute)\n", - " print(\"Initiated compute creation\")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "9d8eb868", - "metadata": {}, - "source": [ - "## Running a training pipeline\n", - "\n", - "Now that we have our training component, we can run it. We begin by generating a unique name for the model." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ad76242b", - "metadata": {}, - "outputs": [], - "source": [ - "import time\n", - "\n", - "model_base_name = \"multilabel_fridge_model\"\n", - "model_name_suffix = \"12492\"\n", - "device = -1" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "d49615a7", - "metadata": {}, - "source": [ - "Next, we define our training pipeline. This has two components. The first is the training component which we defined above. The second is a component to register the model in AzureML:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "cb6c6cec", - "metadata": {}, - "outputs": [], - "source": [ - "from azure.ai.ml import dsl, Input\n", - "\n", - "train_model_component = ml_client.components.get(\n", - " name=\"rai_multilabel_fridge_training_component\", version=rai_example_version_string\n", - ")\n", - "\n", - "\n", - "@dsl.pipeline(\n", - " compute=compute_name,\n", - " description=\"Register Model for RAI Multilabel Fridge example\",\n", - " experiment_name=f\"RAI_Multilabel_Fridge_Example_Model_Training_{model_name_suffix}\",\n", - ")\n", - "def my_training_pipeline(model_base_name, model_name_suffix, device):\n", - " trained_model = train_component_definition(\n", - " model_base_name=model_base_name,\n", - " model_name_suffix=model_name_suffix,\n", - " device=device,\n", - " )\n", - " trained_model.set_limits(timeout=3600)\n", - "\n", - " return {}\n", - "\n", - "\n", - "model_registration_pipeline_job = my_training_pipeline(\n", - " model_base_name, model_name_suffix, device\n", - ")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "2fa66ea6", - "metadata": {}, - "source": [ - "With the training pipeline defined, we can submit it for execution in AzureML. We define a helper function to wait for the job to complete:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f854eef5", - "metadata": {}, - "outputs": [], - "source": [ - "from azure.ai.ml.entities import PipelineJob\n", - "\n", - "\n", - "def submit_and_wait(ml_client, pipeline_job) -> PipelineJob:\n", - " created_job = ml_client.jobs.create_or_update(pipeline_job)\n", - " assert created_job is not None\n", - "\n", - " while created_job.status not in [\n", - " \"Completed\",\n", - " \"Failed\",\n", - " \"Canceled\",\n", - " \"NotResponding\",\n", - " ]:\n", - " time.sleep(30)\n", - " created_job = ml_client.jobs.get(created_job.name)\n", - " print(\"Latest status : {0}\".format(created_job.status))\n", - " assert created_job.status == \"Completed\"\n", - " return created_job\n", - "\n", - "\n", - "# This is the actual submission\n", - "training_job = submit_and_wait(ml_client, model_registration_pipeline_job)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "0722395e", - "metadata": {}, - "source": [ - "## Creating the RAI Vision Insights\n", - "\n", - "Now that we have our model, we can generate RAI Vision insights for it. We will need the `id` of the registered model, which will be as follows:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7d3e6e6e", - "metadata": {}, - "outputs": [], - "source": [ - "expected_model_id = f\"{model_base_name}_{model_name_suffix}:1\"\n", - "azureml_model_id = f\"azureml:{expected_model_id}\"" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "310aa659", - "metadata": {}, - "source": [ - "Next, we load the RAI components, so that we can construct a pipeline:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d67b942e", - "metadata": {}, - "outputs": [], - "source": [ - "fridge_test_mltable = Input(\n", - " type=\"mltable\",\n", - " path=f\"{input_test_data}:{rai_example_version_string}\",\n", - " mode=\"download\",\n", - ")\n", - "\n", - "registry_name = \"azureml\"\n", - "credential = DefaultAzureCredential()\n", - "\n", - "ml_client_registry = MLClient(\n", - " credential=credential,\n", - " subscription_id=ml_client.subscription_id,\n", - " resource_group_name=ml_client.resource_group_name,\n", - " registry_name=registry_name,\n", - ")\n", - "\n", - "rai_vision_insights_component = ml_client_registry.components.get(\n", - " name=\"rai_vision_insights\", version=version_string\n", - ")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "c98cd2d9", - "metadata": {}, - "source": [ - "We can now specify our pipeline. Complex objects (such as lists of column names) have to be converted to JSON strings before being passed to the components." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a62105a7", - "metadata": {}, - "outputs": [], - "source": [ - "import json\n", - "from azure.ai.ml import Input\n", - "from azure.ai.ml.constants import AssetTypes\n", - "\n", - "\n", - "@dsl.pipeline(\n", - " compute=compute_name,\n", - " description=\"Example RAI computation on Multilabel Fridge data\",\n", - " experiment_name=f\"RAI_Multilable_Fridge_Example_RAIInsights_Computation_{model_name_suffix}\",\n", - ")\n", - "def rai_fridge_image_classification_pipeline(\n", - " target_column_name,\n", - " test_data,\n", - " classes,\n", - " use_model_dependency,\n", - "):\n", - " # Initiate the RAIInsights\n", - " rai_image_job = rai_vision_insights_component(\n", - " task_type=\"multilabel_image_classification\",\n", - " model_info=expected_model_id,\n", - " model_input=Input(type=AssetTypes.MLFLOW_MODEL, path=azureml_model_id),\n", - " test_dataset=test_data,\n", - " target_column_name=target_column_name,\n", - " classes=classes,\n", - " model_type=\"fastai\",\n", - " use_model_dependency=use_model_dependency,\n", - " )\n", - " rai_image_job.set_limits(timeout=7200)\n", - "\n", - " rai_image_job.outputs.dashboard.mode = \"upload\"\n", - " rai_image_job.outputs.ux_json.mode = \"upload\"\n", - "\n", - " return {\n", - " \"dashboard\": rai_image_job.outputs.dashboard,\n", - " \"ux_json\": rai_image_job.outputs.ux_json,\n", - " }" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "6b5b14a9", - "metadata": {}, - "source": [ - "Next, we define the pipeline object itself, and ensure that the outputs will be available for download:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e4d86ec2", - "metadata": {}, - "outputs": [], - "source": [ - "import uuid\n", - "from azure.ai.ml import Output\n", - "\n", - "insights_pipeline_job = rai_fridge_image_classification_pipeline(\n", - " target_column_name=encoded_target_column_name,\n", - " test_data=fridge_test_mltable,\n", - " classes=\"[]\",\n", - " use_model_dependency=True,\n", - ")\n", - "\n", - "rand_path = str(uuid.uuid4())\n", - "insights_pipeline_job.outputs.dashboard = Output(\n", - " path=f\"azureml://datastores/workspaceblobstore/paths/{rand_path}/dashboard/\",\n", - " mode=\"upload\",\n", - " type=\"uri_folder\",\n", - ")\n", - "insights_pipeline_job.outputs.ux_json = Output(\n", - " path=f\"azureml://datastores/workspaceblobstore/paths/{rand_path}/ux_json/\",\n", - " mode=\"upload\",\n", - " type=\"uri_folder\",\n", - ")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "25f34573", - "metadata": {}, - "source": [ - "And submit the pipeline to AzureML for execution:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2ca757f7", - "metadata": {}, - "outputs": [], - "source": [ - "insights_job = submit_and_wait(ml_client, insights_pipeline_job)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "1381768a", - "metadata": {}, - "source": [ - "The dashboard should appear in the AzureML portal in the registered model view. The following cell computes the expected URI:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e86ab611", - "metadata": {}, - "outputs": [], - "source": [ - "sub_id = ml_client._operation_scope.subscription_id\n", - "rg_name = ml_client._operation_scope.resource_group_name\n", - "ws_name = ml_client.workspace_name\n", - "\n", - "expected_uri = f\"https://ml.azure.com/model/{expected_model_id}/model_analysis?wsid=/subscriptions/{sub_id}/resourcegroups/{rg_name}/workspaces/{ws_name}\"\n", - "\n", - "print(f\"Please visit {expected_uri} to see your analysis\")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "93a8dff9", - "metadata": {}, - "source": [ - "## Constructing the pipeline in YAML\n", - "\n", - "It is also possible to specify the pipeline as a YAML file, and submit that using the command line. We will now create a YAML specification of the above pipeline and submit that:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "624bb0cd", - "metadata": {}, - "outputs": [], - "source": [ - "yaml_contents = f\"\"\"\n", - "$schema: https://azuremlschemas.azureedge.net/latest/pipelineJob.schema.json\n", - "experiment_name: AML_RAI_Multilabel_Vision_Sample_{rai_example_version_string}\n", - "type: pipeline\n", - "\n", - "compute: azureml:cpucluster\n", - "\n", - "inputs:\n", - " fridge_model_info: {expected_model_id}\n", - " my_test_data:\n", - " type: mltable\n", - " path: azureml:{input_test_data}:{rai_example_version_string}\n", - " mode: download\n", - "\n", - "settings:\n", - " default_datastore: azureml:workspaceblobstore\n", - " default_compute: azureml:cpucluster\n", - " continue_on_step_failure: false\n", - "\n", - "jobs:\n", - " analyse_model:\n", - " type: command\n", - " component: azureml://registries/azureml/components/rai_vision_insights/versions/{version_string}\n", - " inputs:\n", - " task_type: multilabel_image_classification\n", - " model_input:\n", - " type: mlflow_model\n", - " path: {azureml_model_id}\n", - " model_info: ${{{{parent.inputs.fridge_model_info}}}}\n", - " test_dataset: ${{{{parent.inputs.my_test_data}}}}\n", - " target_column_name: {encoded_target_column_name}\n", - " maximum_rows_for_test_dataset: 5000\n", - " classes: '[]'\n", - "\"\"\"\n", - "\n", - "yaml_pipeline_filename = \"rai_vision_example.yaml\"\n", - "\n", - "with open(yaml_pipeline_filename, \"w\") as f:\n", - " f.write(yaml_contents)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "1fd5f2dd", - "metadata": {}, - "source": [ - "The created file can then be submitted using the Azure CLI:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3bf9bb1c", - "metadata": {}, - "outputs": [], - "source": [ - "cmd_line = [\n", - " \"az\",\n", - " \"ml\",\n", - " \"job\",\n", - " \"create\",\n", - " \"--resource-group\",\n", - " rg_name,\n", - " \"--workspace\",\n", - " ws_name,\n", - " \"--file\",\n", - " yaml_pipeline_filename,\n", - "]\n", - "\n", - "import subprocess\n", - "\n", - "try:\n", - " cmd = subprocess.run(cmd_line, check=True, shell=True, capture_output=True)\n", - "except subprocess.CalledProcessError as cpe:\n", - " print(f\"Error invoking: {cpe.args}\")\n", - " print(cpe.stdout)\n", - " print(cpe.stderr)\n", - " raise\n", - "else:\n", - " print(\"Azure CLI submission completed\")" - ] - } - ], - "metadata": { - "celltoolbar": "Raw Cell Format", - "kernelspec": { - "display_name": "Python 3.10 - SDK V2", - "language": "python", - "name": "python310-sdkv2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.15" - }, - "vscode": { - "interpreter": { - "hash": "8fd340b5477ca1a0b454d48a3973beff39fee032ada47a04f6f3725b469a8988" - } - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/sdk/python/responsible-ai/vision/responsibleaidashboard-object-detection-MSCOCO.ipynb b/sdk/python/responsible-ai/vision/responsibleaidashboard-object-detection-MSCOCO.ipynb deleted file mode 100644 index 7989e13c4e..0000000000 --- a/sdk/python/responsible-ai/vision/responsibleaidashboard-object-detection-MSCOCO.ipynb +++ /dev/null @@ -1,773 +0,0 @@ -{ - "cells": [ - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Image Object Detection scenario with RAI Dashboard\n", - "\n", - "The [object detection fridge dataset](https://github.com/microsoft/computervision-recipes/tree/master/scenarios/detection) provides images and bounding boxes with four types of items commonly found in the Microsoft New England R&D office refrigerator - carton, water bottle, can and milk bottle. This example notebook demonstrates how to use a Faster R-CNN ResNet 50 FPN computer vision model from torchvision on the dataset to evaluate the model in AzureML.\n", - "\n", - "First, we need to specify the version of the RAI components which are available in the workspace. This was specified when the components were uploaded." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "version_string = \"0.0.20\"\n", - "compute_name = \"cpucluster\"\n", - "rai_example_version_string = \"21\"" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The above cell will specify the version of RAI components for the workspace as well as the compute cluster to utilize in AzureML. The rai string is to specify a version for teh data and components." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import urllib\n", - "import os\n", - "import sys\n", - "from zipfile import ZipFile\n", - "import numpy as np\n", - "import pandas as pd\n", - "import json\n", - "\n", - "try:\n", - " from urllib import urlretrieve\n", - "except ImportError:\n", - " from urllib.request import urlretrieve\n", - "\n", - "%pip install torchvision\n", - "\n", - "\n", - "def download_mscoco_dataset(data_path, annotations_file):\n", - " os.makedirs(data_path, exist_ok=True)\n", - "\n", - " base_url = \"https://publictestdatasets.blob.core.windows.net/\"\n", - " coco = \"computervision/smallMSCOCO/\"\n", - " data_url = base_url + coco + annotations_file\n", - "\n", - " data_output_path = os.path.join(data_path, annotations_file)\n", - " urlretrieve(data_url, filename=data_output_path)\n", - "\n", - "\n", - "annotations = \"msCOCOValExample7.jsonl\"\n", - "\n", - "data_path = \"./dataMSCOCO\"\n", - "\n", - "\n", - "download_mscoco_dataset(data_path, annotations)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In the above cell of code, this is downloading the json file from the azure blob storage. Plus it will create a directory call dataMSCOCO and place the ms coco json file. Note that this is just 25 images." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def create_ml_table_file(filename):\n", - " return (\n", - " \"$schema: http://azureml/sdk-2-0/MLTable.json\\n\"\n", - " \"type: mltable\\n\"\n", - " \"paths:\\n\"\n", - " \" - file: ./{0}\\n\"\n", - " \"transformations:\\n\"\n", - " \" - read_json_lines:\\n\"\n", - " \" encoding: utf8\\n\"\n", - " \" invalid_lines: error\\n\"\n", - " \" include_path_column: false\\n\"\n", - " ).format(filename)\n", - "\n", - "\n", - "def save_ml_table_file(output_path, ml_table_data):\n", - " mltable_file_contents = create_ml_table_file(ml_table_data)\n", - " with open(os.path.join(output_path, \"MLTable\"), \"w\") as f:\n", - " f.write(mltable_file_contents)\n", - "\n", - "\n", - "save_ml_table_file(data_path, annotations)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The above creates the ML table using the json file that we downloaded." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import mltable\n", - "\n", - "tbl = mltable.load(data_path)\n", - "\n", - "val_df: pd.DataFrame = tbl.to_pandas_dataframe()" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Taking the mltable and putting it to a pandas dataframe" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "target_column_name = \"label\"" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The ground truth of the bounding boxes, this will be used for the dashboard and other components" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Enter details of your AML workspace\n", - "subscription_id = \"\"\n", - "resource_group = \"\"\n", - "workspace = \"\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Handle to the workspace\n", - "from azure.ai.ml import MLClient\n", - "from azure.identity import DefaultAzureCredential\n", - "\n", - "try:\n", - " credential = DefaultAzureCredential()\n", - " ml_client = MLClient(\n", - " credential=credential,\n", - " subscription_id=subscription_id,\n", - " resource_group_name=resource_group,\n", - " workspace_name=workspace,\n", - " )\n", - "except Exception:\n", - " # If in compute instance we can get the config automatically\n", - " from azureml.core import Workspace\n", - "\n", - " workspace = Workspace.from_config()\n", - " workspace.write_config()\n", - " ml_client = MLClient.from_config(\n", - " credential=DefaultAzureCredential(exclude_shared_token_cache_credential=True),\n", - " logging_enable=True,\n", - " )\n", - "\n", - "print(ml_client)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To upload the dataset (mltable) so we create an MLClient with AzureML" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azure.ai.ml.entities import Data\n", - "from azure.ai.ml.constants import AssetTypes\n", - "\n", - "input_test_data = \"MSCOCO_Test_MLTable_OD18\"\n", - "\n", - "try:\n", - " test_data = ml_client.data.get(\n", - " name=input_test_data,\n", - " version=rai_example_version_string,\n", - " )\n", - "except Exception:\n", - " test_data = Data(\n", - " path=data_path,\n", - " type=AssetTypes.MLTABLE,\n", - " description=\"RAI MSCOCO data\",\n", - " name=input_test_data,\n", - " version=rai_example_version_string,\n", - " )\n", - " ml_client.data.create_or_update(test_data)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The MLClient will upload the data to AzureML. One typical error is the name of input_test_data, if you have used that string name, an error will occur saying that it has been used. To remedy this error change the input_test_data string name" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "\n", - "os.makedirs(\"mscoco_component_src_od_1\", exist_ok=True)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "create directory to place the script file" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%writefile mscoco_component_src_od_1/model_script.py\n", - "\n", - "import argparse\n", - "import logging\n", - "import json\n", - "import os\n", - "import time\n", - "\n", - "\n", - "import mlflow\n", - "import mlflow.pyfunc\n", - "\n", - "from azureml.core import Run\n", - "\n", - "import torchvision\n", - "\n", - "from raiutils.common.retries import retry_function\n", - "\n", - "try:\n", - " from urllib import urlretrieve\n", - "except ImportError:\n", - " from urllib.request import urlretrieve\n", - "\n", - "_logger = logging.getLogger(__file__)\n", - "logging.basicConfig(level=logging.INFO)\n", - "\n", - "MSCOCO_MODEL_NAME = 'mscoco_model'\n", - "\n", - "\n", - "def parse_args():\n", - " # setup arg parser\n", - " parser = argparse.ArgumentParser()\n", - "\n", - " # add arguments\n", - " parser.add_argument(\n", - " \"--model_output_path\", type=str, help=\"Path to write model info JSON\"\n", - " )\n", - " parser.add_argument(\n", - " \"--model_base_name\", type=str, help=\"Name of the registered model\"\n", - " )\n", - " parser.add_argument(\n", - " \"--model_name_suffix\", type=int, help=\"Set negative to use epoch_secs\"\n", - " )\n", - " parser.add_argument(\n", - " \"--device\", type=int, help=(\n", - " \"Device for CPU/GPU supports. Setting this to -1 will leverage \"\n", - " \"CPU, >=0 will run the model on the associated CUDA device id.\")\n", - " )\n", - "\n", - " # parse args\n", - " args = parser.parse_args()\n", - "\n", - " # return args\n", - " return args\n", - "\n", - "\n", - "def main(args):\n", - " current_experiment = Run.get_context().experiment\n", - " tracking_uri = current_experiment.workspace.get_mlflow_tracking_uri()\n", - " _logger.info(\"tracking_uri: {0}\".format(tracking_uri))\n", - " mlflow.set_tracking_uri(tracking_uri)\n", - " mlflow.set_experiment(current_experiment.name)\n", - "\n", - " _logger.info(\"Getting device\")\n", - " device = args.device\n", - "\n", - " _logger.info(\"Loading parquet input\")\n", - "\n", - " model = torchvision.models.detection.fasterrcnn_resnet50_fpn(pretrained=True) \n", - "\n", - " if device >= 0:\n", - " model = model.cuda()\n", - "\n", - " if args.model_name_suffix < 0:\n", - " suffix = int(time.time())\n", - " else:\n", - " suffix = args.model_name_suffix\n", - " registered_name = \"{0}_{1}\".format(args.model_base_name, suffix)\n", - " _logger.info(f\"Registering model as {registered_name}\")\n", - "\n", - " # Saving model with mlflow\n", - " _logger.info(\"Saving with mlflow\")\n", - "\n", - " mlflow.pytorch.log_model(\n", - " model,\n", - " artifact_path=registered_name,\n", - " registered_model_name=registered_name\n", - " )\n", - "\n", - " _logger.info(\"Writing JSON\")\n", - " dict = {\"id\": \"{0}:1\".format(registered_name)}\n", - " output_path = os.path.join(args.model_output_path, \"model_info.json\")\n", - " with open(output_path, \"w\") as of:\n", - " json.dump(dict, fp=of)\n", - "\n", - "\n", - "# run script\n", - "if __name__ == \"__main__\":\n", - " # add space in logs\n", - " print(\"*\" * 60)\n", - " print(\"\\n\\n\\n\")\n", - "\n", - " # parse args\n", - " args = parse_args()\n", - "\n", - " # run main function\n", - " main(args)\n", - "\n", - " # add space in logs\n", - " print(\"*\" * 60)\n", - " print(\"\\n\\n\\n\")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This will create the python file to load the pre-trained pytorch fasterrcnn model that was trained on MS COCO" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import time\n", - "\n", - "model_base_name = \"mscoco_model\"\n", - "model_name_suffix = \"12492\"\n", - "device = -1" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "this is to specify what type of model and model name for logging" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azure.ai.ml import load_component\n", - "\n", - "yaml_contents = f\"\"\"\n", - "$schema: http://azureml/sdk-2-0/CommandComponent.json\n", - "name: mscoco_component_pytorch_13\n", - "display_name: MSCOCO component for RAI OD ex.\n", - "version: {rai_example_version_string}\n", - "type: command\n", - "inputs:\n", - " model_base_name:\n", - " type: string\n", - " model_name_suffix: # Set negative to use epoch_secs\n", - " type: integer\n", - " default: -1\n", - " device: # set to >= 0 to use GPU\n", - " type: integer\n", - " default: 0\n", - "outputs:\n", - " model_output_path:\n", - " type: path\n", - "code: ./mscoco_component_src_od_1/\n", - "environment: azureml://registries/azureml/environments/responsibleai-vision/versions/15\n", - "command: >-\n", - " python model_script.py\n", - " --model_base_name ${{{{inputs.model_base_name}}}}\n", - " --model_name_suffix ${{{{inputs.model_name_suffix}}}}\n", - " --device ${{{{inputs.device}}}}\n", - " --model_output_path ${{{{outputs.model_output_path}}}}\n", - "\"\"\"\n", - "\n", - "yaml_filename = \"test_mscoco1.yaml\"\n", - "\n", - "\n", - "with open(yaml_filename, \"w\") as f:\n", - " f.write(yaml_contents)\n", - "\n", - "test_component_definition = load_component(source=yaml_filename)\n", - "\n", - "ml_client.components.create_or_update(test_component_definition)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "yaml file to send it as an ML component" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azure.ai.ml.entities import AmlCompute\n", - "\n", - "all_compute_names = [x.name for x in ml_client.compute.list()]\n", - "\n", - "if compute_name in all_compute_names:\n", - " print(f\"Found existing compute: {compute_name}\")\n", - "else:\n", - " my_compute = AmlCompute(\n", - " name=compute_name,\n", - " size=\"STANDARD_DS3_V2\",\n", - " min_instances=0,\n", - " max_instances=4,\n", - " idle_time_before_scale_down=3600,\n", - " )\n", - " ml_client.compute.begin_create_or_update(my_compute)\n", - " print(\"Initiated compute creation\")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "find compute target to run the job" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azure.ai.ml import dsl, Input\n", - "\n", - "test_model_component = ml_client.components.get(\n", - " name=\"mscoco_component_pytorch_13\", version=rai_example_version_string\n", - ")\n", - "\n", - "\n", - "@dsl.pipeline(\n", - " compute=compute_name,\n", - " description=\"Register Model for RAI MSCOCO example\",\n", - " experiment_name=f\"RAI_MSCOCO_Example_Model_{model_name_suffix}\",\n", - ")\n", - "def my_pipeline(model_base_name, model_name_suffix, device):\n", - " test_model = test_component_definition(\n", - " model_base_name=model_base_name,\n", - " model_name_suffix=model_name_suffix,\n", - " device=device,\n", - " )\n", - " test_model.set_limits(timeout=3600)\n", - "\n", - " return {}\n", - "\n", - "\n", - "model_registration_pipeline_job = my_pipeline(\n", - " model_base_name, model_name_suffix, device\n", - ")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This is a pipeline to load the model and register it, this is needed to create the RAI vision insights" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azure.ai.ml.entities import PipelineJob\n", - "\n", - "\n", - "def submit_and_wait(ml_client, pipeline_job) -> PipelineJob:\n", - " created_job = ml_client.jobs.create_or_update(pipeline_job)\n", - " assert created_job is not None\n", - "\n", - " while created_job.status not in [\n", - " \"Completed\",\n", - " \"Failed\",\n", - " \"Canceled\",\n", - " \"NotResponding\",\n", - " ]:\n", - " time.sleep(30)\n", - " created_job = ml_client.jobs.get(created_job.name)\n", - " print(\"Latest status : {0}\".format(created_job.status))\n", - " assert created_job.status == \"Completed\"\n", - " return created_job\n", - "\n", - "\n", - "# This is the actual submission\n", - "testing_job = submit_and_wait(ml_client, model_registration_pipeline_job)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Once submitted we can monitor the progress of it" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "expected_model_id = f\"{model_base_name}_{model_name_suffix}:1\"\n", - "azureml_model_id = f\"azureml:{expected_model_id}\"\n", - "# need these ids" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Collecting the name of the id, we will need for rai vision insights" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "mscoco_test_mltable = Input(\n", - " type=\"mltable\",\n", - " path=f\"{input_test_data}:{rai_example_version_string}\",\n", - " mode=\"download\",\n", - ")\n", - "\n", - "registry_name = \"azureml\"\n", - "credential = DefaultAzureCredential()\n", - "\n", - "ml_client_registry = MLClient(\n", - " credential=credential,\n", - " subscription_id=ml_client.subscription_id,\n", - " resource_group_name=ml_client.resource_group_name,\n", - " registry_name=registry_name,\n", - ")\n", - "\n", - "rai_vision_insights_component = ml_client_registry.components.get(\n", - " name=\"rai_vision_insights\", version=version_string\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import json\n", - "from azure.ai.ml import Input\n", - "from azure.ai.ml.constants import AssetTypes\n", - "\n", - "\n", - "@dsl.pipeline(\n", - " compute=compute_name,\n", - " description=\"Example RAI computation on MSCOCO data\",\n", - " experiment_name=f\"RAI_MSCOCO_Example_RAIInsights_Computation_{model_name_suffix}\",\n", - ")\n", - "def rai_mscoco_object_detection_pipeline(target_column_name, test_data, classes):\n", - " # Initiate the RAIInsights\n", - " rai_image_job = rai_vision_insights_component(\n", - " task_type=\"object_detection\",\n", - " model_info=expected_model_id,\n", - " model_input=Input(type=AssetTypes.MLFLOW_MODEL, path=azureml_model_id),\n", - " test_dataset=test_data,\n", - " target_column_name=target_column_name,\n", - " classes=classes,\n", - " model_type=\"pytorch\",\n", - " enable_error_analysis=False,\n", - " num_masks=300,\n", - " mask_res=4,\n", - " )\n", - " rai_image_job.set_limits(timeout=7200)\n", - "\n", - " rai_image_job.outputs.dashboard.mode = \"upload\"\n", - " rai_image_job.outputs.ux_json.mode = \"upload\"\n", - "\n", - " return {\n", - " \"dashboard\": rai_image_job.outputs.dashboard,\n", - " \"ux_json\": rai_image_job.outputs.ux_json,\n", - " }" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Specifying our pipeline" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import uuid\n", - "from azure.ai.ml import Output\n", - "\n", - "insights_pipeline_job = rai_mscoco_object_detection_pipeline(\n", - " target_column_name=target_column_name,\n", - " test_data=mscoco_test_mltable,\n", - " classes='[\"person\", \"bicycle\", \"car\", \"motorcycle\",\"airplane\", \"bus\", \"train\", \"truck\", \"boat\", \"traffic light\", \"fire hydrant\",\"street sign\", \"stop sign\", \"parking meter\", \"bench\", \"bird\", \"cat\", \"dog\", \"horse\",\"sheep\", \"cow\", \"elephant\", \"bear\", \"zebra\", \"giraffe\", \"hat\", \"backpack\",\"umbrella\", \"shoe\", \"eye glasses\", \"handbag\", \"tie\", \"suitcase\", \"frisbee\", \"skis\",\"snowboard\", \"sports ball\", \"kite\", \"baseball bat\", \"baseball glove\", \"skateboard\",\"surfboard\", \"tennis racket\", \"bottle\", \"plate\", \"wine glass\", \"cup\", \"fork\", \"knife\",\"spoon\", \"bowl\", \"banana\", \"apple\", \"sandwich\", \"orange\", \"broccoli\", \"carrot\", \"hot dog\",\"pizza\", \"donut\", \"cake\", \"chair\", \"couch\", \"potted plant\", \"bed\", \"mirror\", \"dining table\",\"window\", \"desk\", \"toilet\", \"door\", \"tv\", \"laptop\", \"mouse\", \"remote\", \"keyboard\",\"cell phone\", \"microwave\", \"oven\", \"toaster\", \"sink\", \"refrigerator\", \"blender\",\"book\", \"clock\", \"vase\", \"scissors\", \"teddy bear\", \"hair drier\", \"toothbrush\"]',\n", - ")\n", - "\n", - "rand_path = str(uuid.uuid4())\n", - "insights_pipeline_job.outputs.dashboard = Output(\n", - " path=f\"azureml://datastores/workspaceblobstore/paths/{rand_path}/dashboard/\",\n", - " mode=\"upload\",\n", - " type=\"uri_folder\",\n", - ")\n", - "insights_pipeline_job.outputs.ux_json = Output(\n", - " path=f\"azureml://datastores/workspaceblobstore/paths/{rand_path}/ux_json/\",\n", - " mode=\"upload\",\n", - " type=\"uri_folder\",\n", - ")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We are doing a pipeline of RAI object detection" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "insights_job = submit_and_wait(ml_client, insights_pipeline_job)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To monitor the progress, since this is on 25 ms coco images and to compute D-Rise this may take around 30-60 mins" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "sub_id = ml_client._operation_scope.subscription_id\n", - "rg_name = ml_client._operation_scope.resource_group_name\n", - "ws_name = ml_client.workspace_name\n", - "\n", - "expected_uri = f\"https://ml.azure.com/model/{expected_model_id}/model_analysis?wsid=/subscriptions/{sub_id}/resourcegroups/{rg_name}/workspaces/{ws_name}\"\n", - "\n", - "print(f\"Please visit {expected_uri} to see your analysis\")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To monitor the progress on azureml " - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3.10 - SDK V2", - "language": "python", - "name": "python310-sdkv2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.16" - }, - "orig_nbformat": 4 - }, - "nbformat": 4, - "nbformat_minor": 2 -} From 2cd3cb627ac1a59aaac42c02b8718f1ed91aa776 Mon Sep 17 00:00:00 2001 From: qusongms <142928570+qusongms@users.noreply.github.com> Date: Thu, 19 Jun 2025 09:32:43 -0700 Subject: [PATCH 16/37] remove rai-text notebooks and related workflows (#3622) --- ...label-text-classification-covid-events.yml | 96 - ...idashboard-text-classification-DBPedia.yml | 96 - ...board-text-classification-blbooksgenre.yml | 96 - ...ard-text-classification-financial-news.yml | 90 - ...ashboard-text-question-answering-squad.yml | 96 - 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--- a/.github/workflows/sdk-responsible-ai-text-responsibleaidashboard-multilabel-text-classification-covid-events.yml +++ /dev/null @@ -1,96 +0,0 @@ -# This code is autogenerated. -# Code is generated by running custom script: python3 readme.py -# Any manual changes to this file may cause incorrect behavior. -# Any manual changes will be overwritten if the code is regenerated. - -name: sdk-responsible-ai-text-responsibleaidashboard-multilabel-text-classification-covid-events -# This file is created by sdk/python/readme.py. -# Please do not edit directly. -on: - workflow_dispatch: - schedule: - - cron: "16 7/12 * * *" - pull_request: - branches: - - main - paths: - - sdk/python/responsible-ai/text/** - - .github/workflows/sdk-responsible-ai-text-responsibleaidashboard-multilabel-text-classification-covid-events.yml - - sdk/python/dev-requirements.txt - - infra/bootstrapping/** - - sdk/python/setup.sh - -permissions: - id-token: write -concurrency: - group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }} - cancel-in-progress: true -jobs: - build: - runs-on: ubuntu-latest - steps: - - name: check out repo - uses: actions/checkout@v2 - - name: setup python - uses: actions/setup-python@v2 - with: - python-version: "3.10" - - name: pip install notebook reqs - run: pip install -r sdk/python/dev-requirements.txt - - name: pip install mlflow reqs - run: pip install -r sdk/python/mlflow-requirements.txt - - name: azure login - uses: azure/login@v1 - with: - client-id: ${{ secrets.OIDC_AZURE_CLIENT_ID }} - tenant-id: ${{ secrets.OIDC_AZURE_TENANT_ID }} - subscription-id: ${{ secrets.OIDC_AZURE_SUBSCRIPTION_ID }} - - name: bootstrap resources - run: | - echo '${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }}'; - bash bootstrap.sh - working-directory: infra/bootstrapping - continue-on-error: false - - name: setup SDK - run: | - source "${{ github.workspace }}/infra/bootstrapping/sdk_helpers.sh"; - source "${{ github.workspace }}/infra/bootstrapping/init_environment.sh"; - bash setup.sh - working-directory: sdk/python - continue-on-error: true - - name: validate readme - run: | - python check-readme.py "${{ github.workspace }}/sdk/python/responsible-ai/text" - working-directory: infra/bootstrapping - continue-on-error: false - - name: setup-cli - run: | - source "${{ github.workspace }}/infra/bootstrapping/sdk_helpers.sh"; - source "${{ github.workspace }}/infra/bootstrapping/init_environment.sh"; - bash setup.sh - working-directory: cli - continue-on-error: true - - name: Eagerly cache access tokens for required scopes - run: | - # Workaround for azure-cli's lack of support for ID token refresh - # Taken from: https://github.com/Azure/login/issues/372#issuecomment-2056289617 - - # Management - az account get-access-token --scope https://management.azure.com/.default --output none - # ML - az account get-access-token --scope https://ml.azure.com/.default --output none - - name: run responsible-ai/text/responsibleaidashboard-multilabel-text-classification-covid-events.ipynb - run: | - source "${{ github.workspace }}/infra/bootstrapping/sdk_helpers.sh"; - source "${{ github.workspace }}/infra/bootstrapping/init_environment.sh"; - bash "${{ github.workspace }}/infra/bootstrapping/sdk_helpers.sh" generate_workspace_config "../../.azureml/config.json"; - bash "${{ github.workspace }}/infra/bootstrapping/sdk_helpers.sh" replace_template_values "responsibleaidashboard-multilabel-text-classification-covid-events.ipynb"; - [ -f "../../.azureml/config" ] && cat "../../.azureml/config"; - papermill -k python responsibleaidashboard-multilabel-text-classification-covid-events.ipynb responsibleaidashboard-multilabel-text-classification-covid-events.output.ipynb - working-directory: sdk/python/responsible-ai/text - - name: upload notebook's working folder as an artifact - if: ${{ always() }} - uses: ./.github/actions/upload-artifact - with: - name: responsibleaidashboard-multilabel-text-classification-covid-events - path: sdk/python/responsible-ai/text diff --git a/.github/workflows/sdk-responsible-ai-text-responsibleaidashboard-text-classification-DBPedia.yml b/.github/workflows/sdk-responsible-ai-text-responsibleaidashboard-text-classification-DBPedia.yml deleted file mode 100644 index d56ec98d3a..0000000000 --- a/.github/workflows/sdk-responsible-ai-text-responsibleaidashboard-text-classification-DBPedia.yml +++ /dev/null @@ -1,96 +0,0 @@ -# This code is autogenerated. -# Code is generated by running custom script: python3 readme.py -# Any manual changes to this file may cause incorrect behavior. -# Any manual changes will be overwritten if the code is regenerated. - -name: sdk-responsible-ai-text-responsibleaidashboard-text-classification-DBPedia -# This file is created by sdk/python/readme.py. -# Please do not edit directly. -on: - workflow_dispatch: - schedule: - - cron: "36 10/12 * * *" - pull_request: - branches: - - main - paths: - - sdk/python/responsible-ai/text/** - - .github/workflows/sdk-responsible-ai-text-responsibleaidashboard-text-classification-DBPedia.yml - - sdk/python/dev-requirements.txt - - infra/bootstrapping/** - - sdk/python/setup.sh - -permissions: - id-token: write -concurrency: - group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }} - cancel-in-progress: true -jobs: - build: - runs-on: ubuntu-latest - steps: - - name: check out repo - uses: actions/checkout@v2 - - name: setup python - uses: actions/setup-python@v2 - with: - python-version: "3.10" - - name: pip install notebook reqs - run: pip install -r sdk/python/dev-requirements.txt - - name: pip install mlflow reqs - run: pip install -r sdk/python/mlflow-requirements.txt - - name: azure login - uses: azure/login@v1 - with: - client-id: ${{ secrets.OIDC_AZURE_CLIENT_ID }} - tenant-id: ${{ secrets.OIDC_AZURE_TENANT_ID }} - subscription-id: ${{ secrets.OIDC_AZURE_SUBSCRIPTION_ID }} - - name: bootstrap resources - run: | - echo '${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }}'; - bash bootstrap.sh - working-directory: infra/bootstrapping - continue-on-error: false - - name: setup SDK - run: | - source "${{ github.workspace }}/infra/bootstrapping/sdk_helpers.sh"; - source "${{ github.workspace }}/infra/bootstrapping/init_environment.sh"; - bash setup.sh - working-directory: sdk/python - continue-on-error: true - - name: validate readme - run: | - python check-readme.py "${{ github.workspace }}/sdk/python/responsible-ai/text" - working-directory: infra/bootstrapping - continue-on-error: false - - name: setup-cli - run: | - source "${{ github.workspace }}/infra/bootstrapping/sdk_helpers.sh"; - source "${{ github.workspace }}/infra/bootstrapping/init_environment.sh"; - bash setup.sh - working-directory: cli - continue-on-error: true - - name: Eagerly cache access tokens for required scopes - run: | - # Workaround for azure-cli's lack of support for ID token refresh - # Taken from: https://github.com/Azure/login/issues/372#issuecomment-2056289617 - - # Management - az account get-access-token --scope https://management.azure.com/.default --output none - # ML - az account get-access-token --scope https://ml.azure.com/.default --output none - - name: run responsible-ai/text/responsibleaidashboard-text-classification-DBPedia.ipynb - run: | - source "${{ github.workspace }}/infra/bootstrapping/sdk_helpers.sh"; - source "${{ github.workspace }}/infra/bootstrapping/init_environment.sh"; - bash "${{ github.workspace }}/infra/bootstrapping/sdk_helpers.sh" generate_workspace_config "../../.azureml/config.json"; - bash "${{ github.workspace }}/infra/bootstrapping/sdk_helpers.sh" replace_template_values "responsibleaidashboard-text-classification-DBPedia.ipynb"; - [ -f "../../.azureml/config" ] && cat "../../.azureml/config"; - papermill -k python responsibleaidashboard-text-classification-DBPedia.ipynb responsibleaidashboard-text-classification-DBPedia.output.ipynb - working-directory: sdk/python/responsible-ai/text - - name: upload notebook's working folder as an artifact - if: ${{ always() }} - uses: ./.github/actions/upload-artifact - with: - name: responsibleaidashboard-text-classification-DBPedia - path: sdk/python/responsible-ai/text diff --git a/.github/workflows/sdk-responsible-ai-text-responsibleaidashboard-text-classification-blbooksgenre.yml b/.github/workflows/sdk-responsible-ai-text-responsibleaidashboard-text-classification-blbooksgenre.yml deleted file mode 100644 index e70baf29d4..0000000000 --- a/.github/workflows/sdk-responsible-ai-text-responsibleaidashboard-text-classification-blbooksgenre.yml +++ /dev/null @@ -1,96 +0,0 @@ -# This code is autogenerated. -# Code is generated by running custom script: python3 readme.py -# Any manual changes to this file may cause incorrect behavior. -# Any manual changes will be overwritten if the code is regenerated. - -name: sdk-responsible-ai-text-responsibleaidashboard-text-classification-blbooksgenre -# This file is created by sdk/python/readme.py. -# Please do not edit directly. -on: - workflow_dispatch: - schedule: - - cron: "12 9/12 * * *" - pull_request: - branches: - - main - paths: - - sdk/python/responsible-ai/text/** - - .github/workflows/sdk-responsible-ai-text-responsibleaidashboard-text-classification-blbooksgenre.yml - - sdk/python/dev-requirements.txt - - infra/bootstrapping/** - - sdk/python/setup.sh - -permissions: - id-token: write -concurrency: - group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }} - cancel-in-progress: true -jobs: - build: - runs-on: ubuntu-latest - steps: - - name: check out repo - uses: actions/checkout@v2 - - name: setup python - uses: actions/setup-python@v2 - with: - python-version: "3.10" - - name: pip install notebook reqs - run: pip install -r sdk/python/dev-requirements.txt - - name: pip install mlflow reqs - run: pip install -r sdk/python/mlflow-requirements.txt - - name: azure login - uses: azure/login@v1 - with: - client-id: ${{ secrets.OIDC_AZURE_CLIENT_ID }} - tenant-id: ${{ secrets.OIDC_AZURE_TENANT_ID }} - subscription-id: ${{ secrets.OIDC_AZURE_SUBSCRIPTION_ID }} - - name: bootstrap resources - run: | - echo '${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }}'; - bash bootstrap.sh - working-directory: infra/bootstrapping - continue-on-error: false - - name: setup SDK - run: | - source "${{ github.workspace }}/infra/bootstrapping/sdk_helpers.sh"; - source "${{ github.workspace }}/infra/bootstrapping/init_environment.sh"; - bash setup.sh - working-directory: sdk/python - continue-on-error: true - - name: validate readme - run: | - python check-readme.py "${{ github.workspace }}/sdk/python/responsible-ai/text" - working-directory: infra/bootstrapping - continue-on-error: false - - name: setup-cli - run: | - source "${{ github.workspace }}/infra/bootstrapping/sdk_helpers.sh"; - source "${{ github.workspace }}/infra/bootstrapping/init_environment.sh"; - bash setup.sh - working-directory: cli - continue-on-error: true - - name: Eagerly cache access tokens for required scopes - run: | - # Workaround for azure-cli's lack of support for ID token refresh - # Taken from: https://github.com/Azure/login/issues/372#issuecomment-2056289617 - - # Management - az account get-access-token --scope https://management.azure.com/.default --output none - # ML - az account get-access-token --scope https://ml.azure.com/.default --output none - - name: run responsible-ai/text/responsibleaidashboard-text-classification-blbooksgenre.ipynb - run: | - source "${{ github.workspace }}/infra/bootstrapping/sdk_helpers.sh"; - source "${{ github.workspace }}/infra/bootstrapping/init_environment.sh"; - bash "${{ github.workspace }}/infra/bootstrapping/sdk_helpers.sh" generate_workspace_config "../../.azureml/config.json"; - bash "${{ github.workspace }}/infra/bootstrapping/sdk_helpers.sh" replace_template_values "responsibleaidashboard-text-classification-blbooksgenre.ipynb"; - [ -f "../../.azureml/config" ] && cat "../../.azureml/config"; - papermill -k python responsibleaidashboard-text-classification-blbooksgenre.ipynb responsibleaidashboard-text-classification-blbooksgenre.output.ipynb - working-directory: sdk/python/responsible-ai/text - - name: upload notebook's working folder as an artifact - if: ${{ always() }} - uses: ./.github/actions/upload-artifact - with: - name: responsibleaidashboard-text-classification-blbooksgenre - path: sdk/python/responsible-ai/text diff --git a/.github/workflows/sdk-responsible-ai-text-responsibleaidashboard-text-classification-financial-news-responsibleaidashboard-text-classification-financial-news.yml b/.github/workflows/sdk-responsible-ai-text-responsibleaidashboard-text-classification-financial-news-responsibleaidashboard-text-classification-financial-news.yml deleted file mode 100644 index bb91277dde..0000000000 --- a/.github/workflows/sdk-responsible-ai-text-responsibleaidashboard-text-classification-financial-news-responsibleaidashboard-text-classification-financial-news.yml +++ /dev/null @@ -1,90 +0,0 @@ -# This code is autogenerated. -# Code is generated by running custom script: python3 readme.py -# Any manual changes to this file may cause incorrect behavior. -# Any manual changes will be overwritten if the code is regenerated. - -name: sdk-responsible-ai-text-responsibleaidashboard-text-classification-financial-news-responsibleaidashboard-text-classification-financial-news -# This file is created by sdk/python/readme.py. -# Please do not edit directly. -on: - workflow_dispatch: - schedule: - - cron: "49 0/12 * * *" - pull_request: - branches: - - main - paths: - - sdk/python/responsible-ai/text/responsibleaidashboard-text-classification-financial-news/** - - .github/workflows/sdk-responsible-ai-text-responsibleaidashboard-text-classification-financial-news-responsibleaidashboard-text-classification-financial-news.yml - - sdk/python/dev-requirements.txt - - infra/bootstrapping/** - - sdk/python/setup.sh -permissions: - id-token: write -concurrency: - group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }} - cancel-in-progress: true -jobs: - build: - runs-on: ubuntu-latest - steps: - - name: check out repo - uses: actions/checkout@v2 - - name: setup python - uses: actions/setup-python@v2 - with: - python-version: "3.10" - - name: pip install notebook reqs - run: pip install -r sdk/python/dev-requirements.txt - - name: pip install mlflow reqs - run: pip install -r sdk/python/mlflow-requirements.txt - - name: azure login - uses: azure/login@v1 - with: - client-id: ${{ secrets.OIDC_AZURE_CLIENT_ID }} - tenant-id: ${{ secrets.OIDC_AZURE_TENANT_ID }} - subscription-id: ${{ secrets.OIDC_AZURE_SUBSCRIPTION_ID }} - - name: bootstrap resources - run: | - echo '${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }}'; - bash bootstrap.sh - working-directory: infra/bootstrapping - continue-on-error: false - - name: setup SDK - run: | - source "${{ github.workspace }}/infra/bootstrapping/sdk_helpers.sh"; - source "${{ github.workspace }}/infra/bootstrapping/init_environment.sh"; - bash setup.sh - working-directory: sdk/python - continue-on-error: true - - name: setup-cli - run: | - source "${{ github.workspace }}/infra/bootstrapping/sdk_helpers.sh"; - source "${{ github.workspace }}/infra/bootstrapping/init_environment.sh"; - bash setup.sh - working-directory: cli - continue-on-error: true - - name: Eagerly cache access tokens for required scopes - run: | - # Workaround for azure-cli's lack of support for ID token refresh - # Taken from: https://github.com/Azure/login/issues/372#issuecomment-2056289617 - - # Management - az account get-access-token --scope https://management.azure.com/.default --output none - # ML - az account get-access-token --scope https://ml.azure.com/.default --output none - - name: run responsible-ai/text/responsibleaidashboard-text-classification-financial-news/responsibleaidashboard-text-classification-financial-news.ipynb - run: | - source "${{ github.workspace }}/infra/bootstrapping/sdk_helpers.sh"; - source "${{ github.workspace }}/infra/bootstrapping/init_environment.sh"; - bash "${{ github.workspace }}/infra/bootstrapping/sdk_helpers.sh" generate_workspace_config "../../.azureml/config.json"; - bash "${{ github.workspace }}/infra/bootstrapping/sdk_helpers.sh" replace_template_values "responsibleaidashboard-text-classification-financial-news.ipynb"; - [ -f "../../.azureml/config" ] && cat "../../.azureml/config"; - papermill -k python responsibleaidashboard-text-classification-financial-news.ipynb responsibleaidashboard-text-classification-financial-news.output.ipynb - working-directory: sdk/python/responsible-ai/text/responsibleaidashboard-text-classification-financial-news - - name: upload notebook's working folder as an artifact - if: ${{ always() }} - uses: ./.github/actions/upload-artifact - with: - name: responsibleaidashboard-text-classification-financial-news - path: sdk/python/responsible-ai/text/responsibleaidashboard-text-classification-financial-news diff --git a/.github/workflows/sdk-responsible-ai-text-responsibleaidashboard-text-question-answering-squad.yml b/.github/workflows/sdk-responsible-ai-text-responsibleaidashboard-text-question-answering-squad.yml deleted file mode 100644 index cd1a9fad06..0000000000 --- a/.github/workflows/sdk-responsible-ai-text-responsibleaidashboard-text-question-answering-squad.yml +++ /dev/null @@ -1,96 +0,0 @@ -# This code is autogenerated. -# Code is generated by running custom script: python3 readme.py -# Any manual changes to this file may cause incorrect behavior. -# Any manual changes will be overwritten if the code is regenerated. - -name: sdk-responsible-ai-text-responsibleaidashboard-text-question-answering-squad -# This file is created by sdk/python/readme.py. -# Please do not edit directly. -on: - workflow_dispatch: - schedule: - - cron: "50 9/12 * * *" - pull_request: - branches: - - main - paths: - - sdk/python/responsible-ai/text/** - - .github/workflows/sdk-responsible-ai-text-responsibleaidashboard-text-question-answering-squad.yml - - sdk/python/dev-requirements.txt - - infra/bootstrapping/** - - sdk/python/setup.sh - -permissions: - id-token: write -concurrency: - group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }} - cancel-in-progress: true -jobs: - build: - runs-on: ubuntu-latest - steps: - - name: check out repo - uses: actions/checkout@v2 - - name: setup python - uses: actions/setup-python@v2 - with: - python-version: "3.10" - - name: pip install notebook reqs - run: pip install -r sdk/python/dev-requirements.txt - - name: pip install mlflow reqs - run: pip install -r sdk/python/mlflow-requirements.txt - - name: azure login - uses: azure/login@v1 - with: - client-id: ${{ secrets.OIDC_AZURE_CLIENT_ID }} - tenant-id: ${{ secrets.OIDC_AZURE_TENANT_ID }} - subscription-id: ${{ secrets.OIDC_AZURE_SUBSCRIPTION_ID }} - - name: bootstrap resources - run: | - echo '${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }}'; - bash bootstrap.sh - working-directory: infra/bootstrapping - continue-on-error: false - - name: setup SDK - run: | - source "${{ github.workspace }}/infra/bootstrapping/sdk_helpers.sh"; - source "${{ github.workspace }}/infra/bootstrapping/init_environment.sh"; - bash setup.sh - working-directory: sdk/python - continue-on-error: true - - name: validate readme - run: | - python check-readme.py "${{ github.workspace }}/sdk/python/responsible-ai/text" - working-directory: infra/bootstrapping - continue-on-error: false - - name: setup-cli - run: | - source "${{ github.workspace }}/infra/bootstrapping/sdk_helpers.sh"; - source "${{ github.workspace }}/infra/bootstrapping/init_environment.sh"; - bash setup.sh - working-directory: cli - continue-on-error: true - - name: Eagerly cache access tokens for required scopes - run: | - # Workaround for azure-cli's lack of support for ID token refresh - # Taken from: https://github.com/Azure/login/issues/372#issuecomment-2056289617 - - # Management - az account get-access-token --scope https://management.azure.com/.default --output none - # ML - az account get-access-token --scope https://ml.azure.com/.default --output none - - name: run responsible-ai/text/responsibleaidashboard-text-question-answering-squad.ipynb - run: | - source "${{ github.workspace }}/infra/bootstrapping/sdk_helpers.sh"; - source "${{ github.workspace }}/infra/bootstrapping/init_environment.sh"; - bash "${{ github.workspace }}/infra/bootstrapping/sdk_helpers.sh" generate_workspace_config "../../.azureml/config.json"; - bash "${{ github.workspace }}/infra/bootstrapping/sdk_helpers.sh" replace_template_values "responsibleaidashboard-text-question-answering-squad.ipynb"; - [ -f "../../.azureml/config" ] && cat "../../.azureml/config"; - papermill -k python responsibleaidashboard-text-question-answering-squad.ipynb responsibleaidashboard-text-question-answering-squad.output.ipynb - working-directory: sdk/python/responsible-ai/text - - name: upload notebook's working folder as an artifact - if: ${{ always() }} - uses: ./.github/actions/upload-artifact - with: - name: responsibleaidashboard-text-question-answering-squad - path: sdk/python/responsible-ai/text diff --git a/sdk/python/README.md b/sdk/python/README.md index 3d5fe6e8dc..5ace8e1c06 100644 --- a/sdk/python/README.md +++ b/sdk/python/README.md @@ -296,11 +296,6 @@ Test Status is for branch - **_main_** |responsible-ai|tabular|[responsibleaidashboard-housing-classification-model-debugging](responsible-ai/tabular/responsibleaidashboard-housing-classification-model-debugging/responsibleaidashboard-housing-classification-model-debugging.ipynb)|*no description*|[![responsibleaidashboard-housing-classification-model-debugging](https://github.com/Azure/azureml-examples/actions/workflows/sdk-responsible-ai-tabular-responsibleaidashboard-housing-classification-model-debugging-responsibleaidashboard-housing-classification-model-debugging.yml/badge.svg?branch=main)](https://github.com/Azure/azureml-examples/actions/workflows/sdk-responsible-ai-tabular-responsibleaidashboard-housing-classification-model-debugging-responsibleaidashboard-housing-classification-model-debugging.yml)| |responsible-ai|tabular|[responsibleaidashboard-housing-decision-making](responsible-ai/tabular/responsibleaidashboard-housing-decision-making/responsibleaidashboard-housing-decision-making.ipynb)|*no description*|[![responsibleaidashboard-housing-decision-making](https://github.com/Azure/azureml-examples/actions/workflows/sdk-responsible-ai-tabular-responsibleaidashboard-housing-decision-making-responsibleaidashboard-housing-decision-making.yml/badge.svg?branch=main)](https://github.com/Azure/azureml-examples/actions/workflows/sdk-responsible-ai-tabular-responsibleaidashboard-housing-decision-making-responsibleaidashboard-housing-decision-making.yml)| |responsible-ai|tabular|[responsibleaidashboard-programmer-regression-model-debugging](responsible-ai/tabular/responsibleaidashboard-programmer-regression-model-debugging/responsibleaidashboard-programmer-regression-model-debugging.ipynb)|*no description*|[![responsibleaidashboard-programmer-regression-model-debugging](https://github.com/Azure/azureml-examples/actions/workflows/sdk-responsible-ai-tabular-responsibleaidashboard-programmer-regression-model-debugging-responsibleaidashboard-programmer-regression-model-debugging.yml/badge.svg?branch=main)](https://github.com/Azure/azureml-examples/actions/workflows/sdk-responsible-ai-tabular-responsibleaidashboard-programmer-regression-model-debugging-responsibleaidashboard-programmer-regression-model-debugging.yml)| -|responsible-ai|text|[responsibleaidashboard-multilabel-text-classification-covid-events](responsible-ai/text/responsibleaidashboard-multilabel-text-classification-covid-events.ipynb)|*no description*|[![responsibleaidashboard-multilabel-text-classification-covid-events](https://github.com/Azure/azureml-examples/actions/workflows/sdk-responsible-ai-text-responsibleaidashboard-multilabel-text-classification-covid-events.yml/badge.svg?branch=main)](https://github.com/Azure/azureml-examples/actions/workflows/sdk-responsible-ai-text-responsibleaidashboard-multilabel-text-classification-covid-events.yml)| -|responsible-ai|text|[responsibleaidashboard-text-classification-DBPedia](responsible-ai/text/responsibleaidashboard-text-classification-DBPedia.ipynb)|*no description*|[![responsibleaidashboard-text-classification-DBPedia](https://github.com/Azure/azureml-examples/actions/workflows/sdk-responsible-ai-text-responsibleaidashboard-text-classification-DBPedia.yml/badge.svg?branch=main)](https://github.com/Azure/azureml-examples/actions/workflows/sdk-responsible-ai-text-responsibleaidashboard-text-classification-DBPedia.yml)| -|responsible-ai|text|[responsibleaidashboard-text-classification-blbooksgenre](responsible-ai/text/responsibleaidashboard-text-classification-blbooksgenre.ipynb)|*no description*|[![responsibleaidashboard-text-classification-blbooksgenre](https://github.com/Azure/azureml-examples/actions/workflows/sdk-responsible-ai-text-responsibleaidashboard-text-classification-blbooksgenre.yml/badge.svg?branch=main)](https://github.com/Azure/azureml-examples/actions/workflows/sdk-responsible-ai-text-responsibleaidashboard-text-classification-blbooksgenre.yml)| -|responsible-ai|text|[responsibleaidashboard-text-classification-financial-news](responsible-ai/text/responsibleaidashboard-text-classification-financial-news/responsibleaidashboard-text-classification-financial-news.ipynb)|*no description*|[![responsibleaidashboard-text-classification-financial-news](https://github.com/Azure/azureml-examples/actions/workflows/sdk-responsible-ai-text-responsibleaidashboard-text-classification-financial-news-responsibleaidashboard-text-classification-financial-news.yml/badge.svg?branch=main)](https://github.com/Azure/azureml-examples/actions/workflows/sdk-responsible-ai-text-responsibleaidashboard-text-classification-financial-news-responsibleaidashboard-text-classification-financial-news.yml)| -|responsible-ai|text|[responsibleaidashboard-text-question-answering-squad](responsible-ai/text/responsibleaidashboard-text-question-answering-squad.ipynb)|*no description*|[![responsibleaidashboard-text-question-answering-squad](https://github.com/Azure/azureml-examples/actions/workflows/sdk-responsible-ai-text-responsibleaidashboard-text-question-answering-squad.yml/badge.svg?branch=main)](https://github.com/Azure/azureml-examples/actions/workflows/sdk-responsible-ai-text-responsibleaidashboard-text-question-answering-squad.yml)| |schedules|job-schedule.ipynb|[job-schedule](schedules/job-schedule.ipynb)|Create a component asset|[![job-schedule](https://github.com/Azure/azureml-examples/actions/workflows/sdk-schedules-job-schedule.yml/badge.svg?branch=main)](https://github.com/Azure/azureml-examples/actions/workflows/sdk-schedules-job-schedule.yml)| |using-mlflow|deploy|[mlflow_sdk_online_endpoints](using-mlflow/deploy/mlflow_sdk_online_endpoints.ipynb)|*no description* - _This sample is excluded from automated tests_|[![mlflow_sdk_online_endpoints](https://github.com/Azure/azureml-examples/actions/workflows/sdk-using-mlflow-deploy-mlflow_sdk_online_endpoints.yml/badge.svg?branch=main)](https://github.com/Azure/azureml-examples/actions/workflows/sdk-using-mlflow-deploy-mlflow_sdk_online_endpoints.yml)| |using-mlflow|deploy|[mlflow_sdk_online_endpoints_progresive](using-mlflow/deploy/mlflow_sdk_online_endpoints_progresive.ipynb)|*no description* - _This sample is excluded from automated tests_|[![mlflow_sdk_online_endpoints_progresive](https://github.com/Azure/azureml-examples/actions/workflows/sdk-using-mlflow-deploy-mlflow_sdk_online_endpoints_progresive.yml/badge.svg?branch=main)](https://github.com/Azure/azureml-examples/actions/workflows/sdk-using-mlflow-deploy-mlflow_sdk_online_endpoints_progresive.yml)| diff --git a/sdk/python/responsible-ai/README.md b/sdk/python/responsible-ai/README.md index bcb91523d9..a4fcce2d47 100644 --- a/sdk/python/responsible-ai/README.md +++ b/sdk/python/responsible-ai/README.md @@ -21,16 +21,10 @@ If not compatible, you can wrap your model's prediction function into a wrapper | Classification | [Kaggle Housing](https://www.kaggle.com/alphaepsilon/housing-prices-dataset) | Tabular | Explanation, Error Analysis, Causal analysis, Counterfactuals | [responsibleaidashboard-housing-classification-model-debugging.ipynb](./tabular/responsibleaidashboard-housing-classification-model-debugging/responsibleaidashboard-housing-classification-model-debugging.ipynb) | [Tabular Dashboard Generation](https://learn.microsoft.com/en-us/azure/machine-learning/how-to-responsible-ai-dashboard?view=azureml-api-2) | | Decision Making | [Kaggle Housing](https://www.kaggle.com/alphaepsilon/housing-prices-dataset) | Tabular | Causal analysis, Counterfactuals | [responsibleaidashboard-housing-decision-making.ipynb](./tabular/responsibleaidashboard-housing-decision-making/responsibleaidashboard-housing-decision-making.ipynb) | [Tabular Dashboard Generation](https://learn.microsoft.com/en-us/azure/machine-learning/how-to-responsible-ai-dashboard?view=azureml-api-2) | | Decision Making | [sklearn Diabetes](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_diabetes.html) | Tabular | Causal analysis, Counterfactuals | [responsibleaidashboard-diabetes-decision-making.ipynb](./tabular/responsibleaidashboard-diabetes-decision-making/responsibleaidashboard-diabetes-decision-making.ipynb) | [Tabular Dashboard Generation](https://learn.microsoft.com/en-us/azure/machine-learning/how-to-responsible-ai-dashboard?view=azureml-api-2) | -| Text Classification | [DBPedia dataset](https://huggingface.co/datasets/DeveloperOats/DBPedia_Classes) | Text | Explanation, Error Analysis | [responsibleaidashboard-text-classification-DBPedia.ipynb](./text/responsibleaidashboard-text-classification-DBPedia.ipynb) | [Text Dashboard Generation](https://learn.microsoft.com/en-us/azure/machine-learning/how-to-responsible-ai-text-dashboard?view=azureml-api-2) | -| Multi-label Text Classification | [Covid19 Emergency Event Dataset](https://huggingface.co/datasets/joelito/covid19_emergency_event) | Text | Explanation, Error Analysis | [responsibleaidashboard-multilabel-text-classification-covid-events.ipynb](./text/responsibleaidashboard-multilabel-text-classification-covid-events.ipynb) | [Text Dashboard Generation](https://learn.microsoft.com/en-us/azure/machine-learning/how-to-responsible-ai-text-dashboard?view=azureml-api-2) | -| Binary Text Classification | [blbooksgenre dataset](https://huggingface.co/datasets/blbooksgenre) | Text | Explanation, Error Analysis | [responsibleaidashboard-text-classification-blbooksgenre.ipynb](./responsibleaidashboard-text-classification-blbooksgenre.ipynb) | [Text Dashboard Generation](https://learn.microsoft.com/en-us/azure/machine-learning/how-to-responsible-ai-text-dashboard?view=azureml-api-2) | -| Text Classification | [News Article Classification](./text/responsibleaidashboard-text-classification-financial-news/Text_classification_dataset.xlsx) | Text | Explanation, Error Analysis | [Financial_News_Text_classifier.ipynb](./text/responsibleaidashboard-text-classification-financial-news/responsibleaidashboard-text-classification-financial-news.ipynb) | [Documentation](./text/responsibleaidashboard-text-classification-financial-news/readme.md) | -| Text Question Answering | [Stanford Question Answering Dataset (SQuAD)](https://huggingface.co/datasets/squad) | Text | Explanation, Error Analysis | [responsibleaidashboard-text-question-answering-squad.ipynb](./text/responsibleaidashboard-text-question-answering-squad.ipynb) | [Text Dashboard Generation](https://learn.microsoft.com/en-us/azure/machine-learning/how-to-responsible-ai-text-dashboard?view=azureml-api-2) | To learn more about the different types of Dashboard visit the below tutorials: 1) [Tabular Dashboard Generation](https://learn.microsoft.com/en-us/azure/machine-learning/how-to-responsible-ai-dashboard?view=azureml-api-2) -2) [Text Dashboard Generation](https://learn.microsoft.com/en-us/azure/machine-learning/how-to-responsible-ai-text-dashboard?view=azureml-api-2) ## Supportability 🧰 diff --git a/sdk/python/responsible-ai/text/README.md b/sdk/python/responsible-ai/text/README.md deleted file mode 100644 index d90006ca96..0000000000 --- a/sdk/python/responsible-ai/text/README.md +++ /dev/null @@ -1,54 +0,0 @@ ---- -page_type: sample -languages: -- python -products: -- azure-machine-learning -description: This sample shows how to create responsible ai dashboard for text datasets. ---- - -## Azure Machine Learning Responsible AI Dashboard and Scorecard - -### overview - -Read more about how to generate the Responsible AI (RAI) dashboard [here](https://learn.microsoft.com/en-us/azure/machine-learning/how-to-responsible-ai-dashboard-sdk-cli?tabs=yaml) and Responsible AI scorecard [here](https://learn.microsoft.com/en-us/azure/machine-learning/how-to-responsible-ai-scorecard). - -The Responsible AI components are supported for MLflow models with `scikit-learn` flavor that are trained on `pandas.DataFrame`. -The components accept both models and SciKit-Learn pipelines as input as long as the model or pipeline implements `predict` and `predict_proba` functions that conforms to the `scikit-learn` convention. -If not compatible, you can wrap your model's prediction function into a wrapper class that transforms the output into the format that is supported (`predict` and `predict_proba` of `scikit-learn`), and pass that wrapper class to modules in this repo. - -### objective -The main objective of this tutorial is to help users understand the process of creating responsible ai dashboard with explanations & error analysis for text dataset. - -### programming languages - - Python - -### directory 📖 - -| Scenario | Dataset | Data type | RAI component included | Link to sample | Documentation | -| --- | --- | --- | --- | --- | --- | -| Text Classification | [DBPedia dataset](https://huggingface.co/datasets/DeveloperOats/DBPedia_Classes) | Text | Explanation, Error Analysis | [responsibleaidashboard-text-classification-DBPedia.ipynb](./text/responsibleaidashboard-text-classification-DBPedia.ipynb) | [Text Dashboard Generation](https://learn.microsoft.com/en-us/azure/machine-learning/how-to-responsible-ai-text-dashboard?view=azureml-api-2) | -| Multi-label Text Classification | [Covid19 Emergency Event Dataset](https://huggingface.co/datasets/joelito/covid19_emergency_event) | Text | Explanation, Error Analysis | [responsibleaidashboard-multilabel-text-classification-covid-events.ipynb](./text/responsibleaidashboard-multilabel-text-classification-covid-events.ipynb) | [Text Dashboard Generation](https://learn.microsoft.com/en-us/azure/machine-learning/how-to-responsible-ai-text-dashboard?view=azureml-api-2) | -| Binary Text Classification | [blbooksgenre dataset](https://huggingface.co/datasets/blbooksgenre) | Text | Explanation, Error Analysis | [responsibleaidashboard-text-classification-blbooksgenre.ipynb](./responsibleaidashboard-text-classification-blbooksgenre.ipynb) | [Text Dashboard Generation](https://learn.microsoft.com/en-us/azure/machine-learning/how-to-responsible-ai-text-dashboard?view=azureml-api-2) | -| Text Classification | [News Article Classification](./text/responsibleaidashboard-text-classification-financial-news/Text_classification_dataset.xlsx) | Text | Explanation, Error Analysis | [Financial_News_Text_classifier.ipynb](./text/responsibleaidashboard-text-classification-financial-news/responsibleaidashboard-text-classification-financial-news.ipynb) | [Documentation](./text/responsibleaidashboard-text-classification-financial-news/readme.md) | -| Text Question Answering | [Stanford Question Answering Dataset (SQuAD)](https://huggingface.co/datasets/squad) | Text | Explanation, Error Analysis | [responsibleaidashboard-text-question-answering-squad.ipynb](./text/responsibleaidashboard-text-question-answering-squad.ipynb) | [Text Dashboard Generation](https://learn.microsoft.com/en-us/azure/machine-learning/how-to-responsible-ai-text-dashboard?view=azureml-api-2)| - - -To learn more about the different types of Dashboard visit the tutorial - -[Text Dashboard Generation](https://learn.microsoft.com/en-us/azure/machine-learning/how-to-responsible-ai-text-dashboard?view=azureml-api-2) - -### estimated runtime - -### supportability 🧰 -Currently, we support datasets having numerical and categorical features. The following table provides the scenarios supported for each of the four responsible AI components: -> **Note**: Model overview (performance metrics and fairness disparity metrics) and Data explorer are generated for every Responsible AI dashboard by default and do not require a component to be configured. - -| RAI component | Binary classification | Multi-class classification | Multilabel classification | Regression | Timeseries forecasting | Categorical features | Text features | Image Features | Recommender Systems | Reinforcement Learning | -| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | -- | -| Explainability | Yes | Yes | No | Yes | No | Yes | Yes | Yes | No | No | -| Error Analysis | Yes | Yes | No | Yes | No | Yes | Yes | Yes | No | No | -| Causal Analysis | Yes | No | No | Yes | No | Yes (max 5 features due to computational cost) | No | No | No | No | -| Counterfactual | Yes | Yes | No | Yes | No | Yes | No | No | No | No | - -Read more about how to use the Responsible AI dashboards [here](https://learn.microsoft.com/en-us/azure/machine-learning/how-to-responsible-ai-dashboard). - diff --git a/sdk/python/responsible-ai/text/responsibleaidashboard-multilabel-text-classification-covid-events.ipynb b/sdk/python/responsible-ai/text/responsibleaidashboard-multilabel-text-classification-covid-events.ipynb deleted file mode 100644 index 87f0fb1df1..0000000000 --- a/sdk/python/responsible-ai/text/responsibleaidashboard-multilabel-text-classification-covid-events.ipynb +++ /dev/null @@ -1,1086 +0,0 @@ -{ - "cells": [ - { - "attachments": {}, - "cell_type": "markdown", - "id": "98605bcd", - "metadata": {}, - "source": [ - "# Mulitlabel Text Classification scenario with RAI Dashboard\n", - "\n", - "The [Covid19 Emergency Event Dataset](https://huggingface.co/datasets/joelito/covid19_emergency_event) is a multilabel dataset that presents a corpus of legislative documents manually annotated for exceptional measures against COVID-19. Each document has 8 possible labels, where the document can be tagged with up to all 8 labels, and each label representing a specific measurement against COVID-19. The events are:\n", - "\n", - "event1: State of Emergency\n", - "event2: Restrictions of fundamental rights and civil liberties\n", - "event3: Restrictions of daily liberties\n", - "event4: Closures / lockdown\n", - "event5: Suspension of international cooperation and commitments\n", - "event6: Police mobilization\n", - "event7: Army mobilization\n", - "event8: Government oversight\n", - "\n" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "e870e274", - "metadata": {}, - "source": [ - "Install datasets to retrieve this dataset from huggingface:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9730612a", - "metadata": {}, - "outputs": [], - "source": [ - "%pip install datasets\n", - "%pip install \"pandas<2.0.0\"" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "e434de4d", - "metadata": {}, - "source": [ - "First, we need to specify the version of the RAI components which are available in the workspace. This was specified when the components were uploaded." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "53b4eeac", - "metadata": {}, - "outputs": [], - "source": [ - "version_string = \"0.0.20\"" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "06008690", - "metadata": {}, - "source": [ - "We also need to give the name of the compute cluster we want to use in AzureML. Later in this notebook, we will create it if it does not already exist:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f1ad79f9", - "metadata": {}, - "outputs": [], - "source": [ - "compute_name = \"cpucluster\"" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "9fc65dc7", - "metadata": {}, - "source": [ - "Finally, we need to specify a version for the data and components we will create while running this notebook. This should be unique for the workspace, but the specific value doesn't matter:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "78053935", - "metadata": {}, - "outputs": [], - "source": [ - "rai_example_version_string = \"23\"" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "73be2b63", - "metadata": {}, - "source": [ - "## Accessing the Data\n", - "\n", - "We supply the data as a pair of parquet files and accompanying `MLTable` file. We can download them, preprocess them, and take a brief look:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5f875f18", - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import json\n", - "import datasets\n", - "import pandas as pd\n", - "\n", - "from sklearn import preprocessing\n", - "\n", - "NUM_TEST_SAMPLES = 100" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ccbfd923", - "metadata": {}, - "outputs": [], - "source": [ - "def load_covid19_emergency_event_dataset(split):\n", - " dataset = datasets.load_dataset(\"joelito/covid19_emergency_event\", split=split)\n", - " dataset = pd.DataFrame(\n", - " {\n", - " \"language\": dataset[\"language\"],\n", - " \"text\": dataset[\"text\"],\n", - " \"event1\": dataset[\"event1\"],\n", - " \"event2\": dataset[\"event2\"],\n", - " \"event3\": dataset[\"event3\"],\n", - " \"event4\": dataset[\"event4\"],\n", - " \"event5\": dataset[\"event5\"],\n", - " \"event6\": dataset[\"event6\"],\n", - " \"event7\": dataset[\"event7\"],\n", - " \"event8\": dataset[\"event8\"],\n", - " }\n", - " )\n", - " dataset = dataset[dataset.language == \"en\"].reset_index(drop=True)\n", - " dataset = dataset.drop(columns=\"language\")\n", - " return dataset\n", - "\n", - "\n", - "pd_test_data = load_covid19_emergency_event_dataset(\"test\")\n", - "test_data = pd_test_data[:NUM_TEST_SAMPLES]" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "17d53df4", - "metadata": {}, - "source": [ - "Now create the mltable:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4c7bbe58", - "metadata": {}, - "outputs": [], - "source": [ - "pq_filename = \"covid_data.parquet\"\n", - "\n", - "\n", - "def create_ml_table_file_contents(pq_filename):\n", - " return (\n", - " \"$schema: http://azureml/sdk-2-0/MLTable.json\\n\"\n", - " \"type: mltable\\n\"\n", - " \"paths:\\n\"\n", - " \" - file: ./{0}\\n\"\n", - " \"transformations:\\n\"\n", - " \" - read_parquet\\n\"\n", - " ).format(pq_filename)\n", - "\n", - "\n", - "def write_to_parquet(data, path, pq_filename):\n", - " os.makedirs(path, exist_ok=True)\n", - " data.to_parquet(os.path.join(path, pq_filename), index=False)\n", - "\n", - "\n", - "def create_ml_table_file(path, contents):\n", - " with open(os.path.join(path, \"MLTable\"), \"w\") as f:\n", - " f.write(contents)\n", - "\n", - "\n", - "test_data_path = \"multilabel_covid_test_data\"\n", - "\n", - "write_to_parquet(test_data, test_data_path, pq_filename)\n", - "\n", - "mltable_file_contents = create_ml_table_file_contents(pq_filename)\n", - "create_ml_table_file(test_data_path, mltable_file_contents)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "a2c4ebb4", - "metadata": {}, - "source": [ - "Load some data for a quick view:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1027fa92", - "metadata": {}, - "outputs": [], - "source": [ - "import mltable\n", - "\n", - "tbl = mltable.load(test_data_path)\n", - "test_df: pd.DataFrame = tbl.to_pandas_dataframe()\n", - "\n", - "display(test_df)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "1115ac59", - "metadata": {}, - "source": [ - "The label column contains the classes:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5b42df3d", - "metadata": {}, - "outputs": [], - "source": [ - "target_column_name = [\n", - " \"event1\",\n", - " \"event2\",\n", - " \"event3\",\n", - " \"event4\",\n", - " \"event5\",\n", - " \"event6\",\n", - " \"event7\",\n", - " \"event8\",\n", - "]\n", - "encoded_target_column_name = json.dumps(target_column_name)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "52e79b04", - "metadata": {}, - "source": [ - "First, we need to upload the datasets to our workspace. We start by creating an `MLClient` for interactions with AzureML:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3a0bddc4", - "metadata": {}, - "outputs": [], - "source": [ - "# Enter details of your AML workspace\n", - "subscription_id = \"\"\n", - "resource_group = \"\"\n", - "workspace = \"\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "395435fc", - "metadata": {}, - "outputs": [], - "source": [ - "# Handle to the workspace\n", - "from azure.ai.ml import MLClient\n", - "from azure.identity import DefaultAzureCredential\n", - "\n", - "try:\n", - " credential = DefaultAzureCredential()\n", - " ml_client = MLClient(\n", - " credential=credential,\n", - " subscription_id=subscription_id,\n", - " resource_group_name=resource_group,\n", - " workspace_name=workspace,\n", - " )\n", - "except Exception:\n", - " # If in compute instance we can get the config automatically\n", - " from azureml.core import Workspace\n", - "\n", - " workspace = Workspace.from_config()\n", - " workspace.write_config()\n", - " ml_client = MLClient.from_config(\n", - " credential=DefaultAzureCredential(exclude_shared_token_cache_credential=True),\n", - " logging_enable=True,\n", - " )\n", - "\n", - "print(ml_client)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "7b501735", - "metadata": {}, - "source": [ - "We can now upload the data to AzureML:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "62eb02a2", - "metadata": {}, - "outputs": [], - "source": [ - "from azure.ai.ml.entities import Data\n", - "from azure.ai.ml.constants import AssetTypes\n", - "\n", - "input_test_data = \"Covid19_Events_Test_MLTable\"\n", - "\n", - "try:\n", - " test_data = ml_client.data.get(\n", - " name=input_test_data,\n", - " version=rai_example_version_string,\n", - " )\n", - "except Exception:\n", - " test_data = Data(\n", - " path=test_data_path,\n", - " type=AssetTypes.MLTABLE,\n", - " description=\"RAI Covid 19 Events Multilabel test data\",\n", - " name=input_test_data,\n", - " version=rai_example_version_string,\n", - " )\n", - " ml_client.data.create_or_update(test_data)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "6815ba75", - "metadata": {}, - "source": [ - "# Creating the Model\n", - "\n", - "To simplify the model creation process, we're going to use a pipeline.\n", - "\n", - "We create a directory for the training script:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e78d869b", - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "\n", - "os.makedirs(\"covid19_events_component_src\", exist_ok=True)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "ea86e55d", - "metadata": {}, - "source": [ - "Next, we write out our script to retrieve the trained model:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a523f144", - "metadata": {}, - "outputs": [], - "source": [ - "%%writefile covid19_events_component_src/training_script.py\n", - "\n", - "import argparse\n", - "import logging\n", - "import json\n", - "import os\n", - "import time\n", - "\n", - "\n", - "import mlflow\n", - "import mlflow.pyfunc\n", - "\n", - "import zipfile\n", - "from azureml.core import Run\n", - "\n", - "from transformers import AutoModelForSequenceClassification, \\\n", - " AutoTokenizer, pipeline\n", - "\n", - "from azureml.rai.utils import PyfuncModel\n", - "from raiutils.common.retries import retry_function\n", - "\n", - "try:\n", - " from urllib import urlretrieve\n", - "except ImportError:\n", - " from urllib.request import urlretrieve\n", - "\n", - "\n", - "_logger = logging.getLogger(__file__)\n", - "logging.basicConfig(level=logging.INFO)\n", - "\n", - "\n", - "COVID19_EVENTS_LABELS = [\"event1\", \"event2\", \"event3\", \"event4\",\n", - " \"event5\", \"event6\", \"event7\", \"event8\"]\n", - "COVID19_EVENTS_MODEL_NAME = \"covid19_events_model\"\n", - "\n", - "\n", - "def parse_args():\n", - " # setup arg parser\n", - " parser = argparse.ArgumentParser()\n", - "\n", - " # add arguments\n", - " parser.add_argument(\n", - " \"--model_output_path\", type=str, help=\"Path to write model info JSON\"\n", - " )\n", - " parser.add_argument(\n", - " \"--model_base_name\", type=str, help=\"Name of the registered model\"\n", - " )\n", - " parser.add_argument(\n", - " \"--model_name_suffix\", type=int, help=\"Set negative to use epoch_secs\"\n", - " )\n", - " parser.add_argument(\n", - " \"--device\", type=int, help=(\n", - " \"Device for CPU/GPU supports. Setting this to -1 will leverage \"\n", - " \"CPU, >=0 will run the model on the associated CUDA device id.\")\n", - " )\n", - "\n", - " # parse args\n", - " args = parser.parse_args()\n", - "\n", - " # return args\n", - " return args\n", - "\n", - "\n", - "class FetchModel(object):\n", - " def __init__(self):\n", - " pass\n", - "\n", - " def fetch(self):\n", - " zipfilename = COVID19_EVENTS_MODEL_NAME + '.zip'\n", - " url = ('https://publictestdatasets.blob.core.windows.net/models/' +\n", - " COVID19_EVENTS_MODEL_NAME + '.zip')\n", - " urlretrieve(url, zipfilename)\n", - " with zipfile.ZipFile(zipfilename, 'r') as unzip:\n", - " unzip.extractall(COVID19_EVENTS_MODEL_NAME)\n", - "\n", - "\n", - "def create_multilabel_pipeline(device):\n", - " fetcher = FetchModel()\n", - " action_name = \"Model download\"\n", - " err_msg = \"Failed to download model\"\n", - " max_retries = 4\n", - " retry_delay = 60\n", - " retry_function(fetcher.fetch, action_name, err_msg,\n", - " max_retries=max_retries,\n", - " retry_delay=retry_delay)\n", - " labels = COVID19_EVENTS_LABELS\n", - " num_labels = len(labels)\n", - " id2label = {idx: label for idx, label in enumerate(labels)}\n", - " label2id = {label: idx for idx, label in enumerate(labels)}\n", - " model = AutoModelForSequenceClassification.from_pretrained(\n", - " COVID19_EVENTS_MODEL_NAME, num_labels=num_labels,\n", - " problem_type=\"multi_label_classification\",\n", - " id2label=id2label,\n", - " label2id=label2id)\n", - "\n", - " if device >= 0:\n", - " model = model.cuda()\n", - "\n", - " tokenizer = AutoTokenizer.from_pretrained(\"bert-base-uncased\")\n", - " # build a pipeline object to do predictions\n", - " pred = pipeline(\n", - " \"text-classification\",\n", - " model=model,\n", - " tokenizer=tokenizer,\n", - " device=device,\n", - " return_all_scores=True\n", - " )\n", - " return pred\n", - "\n", - "\n", - "def main(args):\n", - " current_experiment = Run.get_context().experiment\n", - " tracking_uri = current_experiment.workspace.get_mlflow_tracking_uri()\n", - " _logger.info(\"tracking_uri: {0}\".format(tracking_uri))\n", - " mlflow.set_tracking_uri(tracking_uri)\n", - " mlflow.set_experiment(current_experiment.name)\n", - "\n", - " _logger.info(\"Getting device\")\n", - " device = args.device\n", - "\n", - " # build a pipeline object to do predictions\n", - " _logger.info(\"Building pipeline\")\n", - "\n", - " pred = create_multilabel_pipeline(device)\n", - "\n", - " if args.model_name_suffix < 0:\n", - " suffix = int(time.time())\n", - " else:\n", - " suffix = args.model_name_suffix\n", - " registered_name = \"{0}_{1}\".format(args.model_base_name, suffix)\n", - " _logger.info(f\"Registering model as {registered_name}\")\n", - "\n", - " my_mlflow = PyfuncModel(pred)\n", - "\n", - " # Saving model with mlflow\n", - " _logger.info(\"Saving with mlflow\")\n", - " mlflow.pyfunc.log_model(\n", - " python_model=my_mlflow,\n", - " registered_model_name=registered_name,\n", - " artifact_path=registered_name,\n", - " )\n", - "\n", - " _logger.info(\"Writing JSON\")\n", - " dict = {\"id\": \"{0}:1\".format(registered_name)}\n", - " output_path = os.path.join(args.model_output_path, \"model_info.json\")\n", - " with open(output_path, \"w\") as of:\n", - " json.dump(dict, fp=of)\n", - "\n", - "\n", - "# run script\n", - "if __name__ == \"__main__\":\n", - " # add space in logs\n", - " print(\"*\" * 60)\n", - " print(\"\\n\\n\")\n", - "\n", - " # parse args\n", - " args = parse_args()\n", - "\n", - " # run main function\n", - " main(args)\n", - "\n", - " # add space in logs\n", - " print(\"*\" * 60)\n", - " print(\"\\n\\n\")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "e115dd6e", - "metadata": {}, - "source": [ - "Now, we can build this into an AzureML component:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3d54e43f", - "metadata": {}, - "outputs": [], - "source": [ - "from azure.ai.ml import load_component\n", - "\n", - "yaml_contents = f\"\"\"\n", - "$schema: http://azureml/sdk-2-0/CommandComponent.json\n", - "name: rai_covid19_events_training_component\n", - "display_name: Covid 19 Events training component for RAI example\n", - "version: {rai_example_version_string}\n", - "type: command\n", - "inputs:\n", - " model_base_name:\n", - " type: string\n", - " model_name_suffix: # Set negative to use epoch_secs\n", - " type: integer\n", - " default: -1\n", - " device: # set to >= 0 to use GPU\n", - " type: integer\n", - " default: 0\n", - "outputs:\n", - " model_output_path:\n", - " type: path\n", - "code: ./covid19_events_component_src/\n", - "environment: azureml://registries/azureml/environments/responsibleai-text/versions/13\n", - "command: >-\n", - " python training_script.py\n", - " --model_base_name ${{{{inputs.model_base_name}}}}\n", - " --model_name_suffix ${{{{inputs.model_name_suffix}}}}\n", - " --device ${{{{inputs.device}}}}\n", - " --model_output_path ${{{{outputs.model_output_path}}}}\n", - "\"\"\"\n", - "\n", - "yaml_filename = \"Covid19EventsTextTrainingComp.yaml\"\n", - "\n", - "with open(yaml_filename, \"w\") as f:\n", - " f.write(yaml_contents)\n", - "\n", - "train_component_definition = load_component(source=yaml_filename)\n", - "\n", - "ml_client.components.create_or_update(train_component_definition)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "6d165e2b", - "metadata": {}, - "source": [ - "We need a compute target on which to run our jobs. The following checks whether the compute specified above is present; if not, then the compute target is created." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1e40fc38", - "metadata": {}, - "outputs": [], - "source": [ - "from azure.ai.ml.entities import AmlCompute\n", - "\n", - "all_compute_names = [x.name for x in ml_client.compute.list()]\n", - "\n", - "if compute_name in all_compute_names:\n", - " print(f\"Found existing compute: {compute_name}\")\n", - "else:\n", - " my_compute = AmlCompute(\n", - " name=compute_name,\n", - " size=\"STANDARD_DS3_V2\",\n", - " min_instances=0,\n", - " max_instances=4,\n", - " idle_time_before_scale_down=3600,\n", - " )\n", - " ml_client.compute.begin_create_or_update(my_compute)\n", - " print(\"Initiated compute creation\")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "9d8eb868", - "metadata": {}, - "source": [ - "## Running a training pipeline\n", - "\n", - "Now that we have our training component, we can run it. We begin by generating a unique name for the mode;" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ad76242b", - "metadata": {}, - "outputs": [], - "source": [ - "import time\n", - "\n", - "model_base_name = \"multilabel_hf_model\"\n", - "model_name_suffix = \"12492\"\n", - "device = -1" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "d49615a7", - "metadata": {}, - "source": [ - "Next, we define our training pipeline. This has two components. The first is the training component which we defined above. The second is a component to register the model in AzureML:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "cb6c6cec", - "metadata": {}, - "outputs": [], - "source": [ - "from azure.ai.ml import dsl, Input\n", - "\n", - "train_model_component = ml_client.components.get(\n", - " name=\"rai_covid19_events_training_component\", version=rai_example_version_string\n", - ")\n", - "\n", - "\n", - "@dsl.pipeline(\n", - " compute=compute_name,\n", - " description=\"Register Model for RAI Covid 19 Events Multilabel example\",\n", - " experiment_name=f\"RAI_Covid19_Events_Multilabel_Example_Model_Training_{model_name_suffix}\",\n", - ")\n", - "def my_training_pipeline(model_base_name, model_name_suffix, device):\n", - " trained_model = train_component_definition(\n", - " model_base_name=model_base_name,\n", - " model_name_suffix=model_name_suffix,\n", - " device=device,\n", - " )\n", - " trained_model.set_limits(timeout=3600)\n", - "\n", - " return {}\n", - "\n", - "\n", - "model_registration_pipeline_job = my_training_pipeline(\n", - " model_base_name, model_name_suffix, device\n", - ")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "2fa66ea6", - "metadata": {}, - "source": [ - "With the training pipeline defined, we can submit it for execution in AzureML. We define a helper function to wait for the job to complete:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f854eef5", - "metadata": {}, - "outputs": [], - "source": [ - "from azure.ai.ml.entities import PipelineJob\n", - "\n", - "\n", - "def submit_and_wait(ml_client, pipeline_job) -> PipelineJob:\n", - " created_job = ml_client.jobs.create_or_update(pipeline_job)\n", - " assert created_job is not None\n", - "\n", - " while created_job.status not in [\n", - " \"Completed\",\n", - " \"Failed\",\n", - " \"Canceled\",\n", - " \"NotResponding\",\n", - " ]:\n", - " time.sleep(30)\n", - " created_job = ml_client.jobs.get(created_job.name)\n", - " print(\"Latest status : {0}\".format(created_job.status))\n", - " assert created_job.status == \"Completed\"\n", - " return created_job\n", - "\n", - "\n", - "# This is the actual submission\n", - "training_job = submit_and_wait(ml_client, model_registration_pipeline_job)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "0722395e", - "metadata": {}, - "source": [ - "## Creating the RAI Text Insights\n", - "\n", - "Now that we have our model, we can generate RAI Text insights for it. We will need the `id` of the registered model, which will be as follows:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7d3e6e6e", - "metadata": {}, - "outputs": [], - "source": [ - "expected_model_id = f\"{model_base_name}_{model_name_suffix}:1\"\n", - "azureml_model_id = f\"azureml:{expected_model_id}\"" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "310aa659", - "metadata": {}, - "source": [ - "Next, we load the RAI components, so that we can construct a pipeline:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d67b942e", - "metadata": {}, - "outputs": [], - "source": [ - "covid19_test_mltable = Input(\n", - " type=\"mltable\",\n", - " path=f\"{input_test_data}:{rai_example_version_string}\",\n", - " mode=\"download\",\n", - ")\n", - "\n", - "registry_name = \"azureml\"\n", - "credential = DefaultAzureCredential()\n", - "\n", - "ml_client_registry = MLClient(\n", - " credential=credential,\n", - " subscription_id=ml_client.subscription_id,\n", - " resource_group_name=ml_client.resource_group_name,\n", - " registry_name=registry_name,\n", - ")\n", - "\n", - "rai_text_insights_component = ml_client_registry.components.get(\n", - " name=\"rai_text_insights\", version=version_string\n", - ")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "c98cd2d9", - "metadata": {}, - "source": [ - "We can now specify our pipeline. Complex objects (such as lists of column names) have to be converted to JSON strings before being passed to the components." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a62105a7", - "metadata": {}, - "outputs": [], - "source": [ - "import json\n", - "from azure.ai.ml import Input\n", - "from azure.ai.ml.constants import AssetTypes\n", - "\n", - "\n", - "@dsl.pipeline(\n", - " compute=compute_name,\n", - " description=\"Example RAI computation on Covid 19 Events Multilabel data\",\n", - " experiment_name=f\"RAI_Covid19_Events_Multilabel_Example_RAIInsights_Computation_{model_name_suffix}\",\n", - ")\n", - "def rai_covid19_text_classification_pipeline(\n", - " target_column_name,\n", - " test_data,\n", - " classes,\n", - " use_model_dependency,\n", - "):\n", - " # Initiate the RAIInsights\n", - " rai_text_job = rai_text_insights_component(\n", - " task_type=\"multilabel_text_classification\",\n", - " model_info=expected_model_id,\n", - " model_input=Input(type=AssetTypes.MLFLOW_MODEL, path=azureml_model_id),\n", - " test_dataset=test_data,\n", - " target_column_name=target_column_name,\n", - " classes=classes,\n", - " use_model_dependency=use_model_dependency,\n", - " )\n", - " rai_text_job.set_limits(timeout=7200)\n", - "\n", - " rai_text_job.outputs.dashboard.mode = \"upload\"\n", - " rai_text_job.outputs.ux_json.mode = \"upload\"\n", - "\n", - " return {\n", - " \"dashboard\": rai_text_job.outputs.dashboard,\n", - " \"ux_json\": rai_text_job.outputs.ux_json,\n", - " }" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "6b5b14a9", - "metadata": {}, - "source": [ - "Next, we define the pipeline object itself, and ensure that the outputs will be available for download:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e4d86ec2", - "metadata": {}, - "outputs": [], - "source": [ - "import uuid\n", - "from azure.ai.ml import Output\n", - "\n", - "insights_pipeline_job = rai_covid19_text_classification_pipeline(\n", - " target_column_name=encoded_target_column_name,\n", - " test_data=covid19_test_mltable,\n", - " classes=\"[]\",\n", - " use_model_dependency=True,\n", - ")\n", - "\n", - "rand_path = str(uuid.uuid4())\n", - "insights_pipeline_job.outputs.dashboard = Output(\n", - " path=f\"azureml://datastores/workspaceblobstore/paths/{rand_path}/dashboard/\",\n", - " mode=\"upload\",\n", - " type=\"uri_folder\",\n", - ")\n", - "insights_pipeline_job.outputs.ux_json = Output(\n", - " path=f\"azureml://datastores/workspaceblobstore/paths/{rand_path}/ux_json/\",\n", - " mode=\"upload\",\n", - " type=\"uri_folder\",\n", - ")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "25f34573", - "metadata": {}, - "source": [ - "And submit the pipeline to AzureML for execution:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2ca757f7", - "metadata": {}, - "outputs": [], - "source": [ - "insights_job = submit_and_wait(ml_client, insights_pipeline_job)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "1381768a", - "metadata": {}, - "source": [ - "The dashboard should appear in the AzureML portal in the registered model view. The following cell computes the expected URI:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e86ab611", - "metadata": {}, - "outputs": [], - "source": [ - "sub_id = ml_client._operation_scope.subscription_id\n", - "rg_name = ml_client._operation_scope.resource_group_name\n", - "ws_name = ml_client.workspace_name\n", - "\n", - "expected_uri = f\"https://ml.azure.com/model/{expected_model_id}/model_analysis?wsid=/subscriptions/{sub_id}/resourcegroups/{rg_name}/workspaces/{ws_name}\"\n", - "\n", - "print(f\"Please visit {expected_uri} to see your analysis\")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "93a8dff9", - "metadata": {}, - "source": [ - "## Constructing the pipeline in YAML\n", - "\n", - "It is also possible to specify the pipeline as a YAML file, and submit that using the command line. We will now create a YAML specification of the above pipeline and submit that:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "624bb0cd", - "metadata": {}, - "outputs": [], - "source": [ - "yaml_contents = f\"\"\"\n", - "$schema: https://azuremlschemas.azureedge.net/latest/pipelineJob.schema.json\n", - "experiment_name: AML_RAI_Multilabel_Text_Sample_{rai_example_version_string}\n", - "type: pipeline\n", - "\n", - "compute: azureml:cpucluster\n", - "\n", - "inputs:\n", - " hf_model_info: {expected_model_id}\n", - " my_test_data:\n", - " type: mltable\n", - " path: azureml:{input_test_data}:{rai_example_version_string}\n", - " mode: download\n", - "\n", - "settings:\n", - " default_datastore: azureml:workspaceblobstore\n", - " default_compute: azureml:cpucluster\n", - " continue_on_step_failure: false\n", - "\n", - "jobs:\n", - " analyse_model:\n", - " type: command\n", - " component: azureml://registries/azureml/components/rai_text_insights/versions/{version_string}\n", - " inputs:\n", - " task_type: multilabel_text_classification\n", - " model_input:\n", - " type: mlflow_model\n", - " path: {azureml_model_id}\n", - " model_info: ${{{{parent.inputs.hf_model_info}}}}\n", - " test_dataset: ${{{{parent.inputs.my_test_data}}}}\n", - " target_column_name: {target_column_name}\n", - " maximum_rows_for_test_dataset: 5000\n", - " classes: '[]'\n", - " enable_explanation: True\n", - " enable_error_analysis: True\n", - "\"\"\"\n", - "\n", - "yaml_pipeline_filename = \"rai_text_example.yaml\"\n", - "\n", - "with open(yaml_pipeline_filename, \"w\") as f:\n", - " f.write(yaml_contents)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "1fd5f2dd", - "metadata": {}, - "source": [ - "The created file can then be submitted using the Azure CLI:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3bf9bb1c", - "metadata": {}, - "outputs": [], - "source": [ - "cmd_line = [\n", - " \"az\",\n", - " \"ml\",\n", - " \"job\",\n", - " \"create\",\n", - " \"--resource-group\",\n", - " rg_name,\n", - " \"--workspace\",\n", - " ws_name,\n", - " \"--file\",\n", - " yaml_pipeline_filename,\n", - "]\n", - "\n", - "import subprocess\n", - "\n", - "try:\n", - " cmd = subprocess.run(cmd_line, check=True, shell=True, capture_output=True)\n", - "except subprocess.CalledProcessError as cpe:\n", - " print(f\"Error invoking: {cpe.args}\")\n", - " print(cpe.stdout)\n", - " print(cpe.stderr)\n", - " raise\n", - "else:\n", - " print(\"Azure CLI submission completed\")" - ] - } - ], - "metadata": { - "celltoolbar": "Raw Cell Format", - "kernelspec": { - "display_name": "Python 3.10 - SDK V2", - "language": "python", - "name": "python310-sdkv2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.15" - }, - "vscode": { - "interpreter": { - "hash": "8fd340b5477ca1a0b454d48a3973beff39fee032ada47a04f6f3725b469a8988" - } - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/sdk/python/responsible-ai/text/responsibleaidashboard-text-classification-DBPedia.ipynb b/sdk/python/responsible-ai/text/responsibleaidashboard-text-classification-DBPedia.ipynb deleted file mode 100644 index 9c08045a24..0000000000 --- a/sdk/python/responsible-ai/text/responsibleaidashboard-text-classification-DBPedia.ipynb +++ /dev/null @@ -1,1080 +0,0 @@ -{ - "cells": [ - { - "attachments": {}, - "cell_type": "markdown", - "id": "98605bcd", - "metadata": {}, - "source": [ - "# Text Classification scenario with RAI Dashboard\n", - "\n", - "The [DBPedia dataset](https://huggingface.co/datasets/DeveloperOats/DBPedia_Classes) classifies 342,782 articles from Wikipedia into multiple classes. There are three levels of classification and this notebook examines a model that was fine-tuned on the first level with 9 classes." - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "80399184", - "metadata": {}, - "source": [ - "Install datasets to retrieve this dataset from huggingface:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b0bc583b", - "metadata": {}, - "outputs": [], - "source": [ - "%pip install datasets\n", - "%pip install \"pandas<2.0.0\"" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "bdd9bfba", - "metadata": {}, - "source": [ - "First, we need to specify the version of the RAI components which are available in the workspace. This was specified when the components were uploaded." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "53b4eeac", - "metadata": {}, - "outputs": [], - "source": [ - "version_string = \"0.0.20\"" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "06008690", - "metadata": {}, - "source": [ - "We also need to give the name of the compute cluster we want to use in AzureML. Later in this notebook, we will create it if it does not already exist:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f1ad79f9", - "metadata": {}, - "outputs": [], - "source": [ - "compute_name = \"cpucluster\"" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "9fc65dc7", - "metadata": {}, - "source": [ - "Finally, we need to specify a version for the data and components we will create while running this notebook. This should be unique for the workspace, but the specific value doesn't matter:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "78053935", - "metadata": {}, - "outputs": [], - "source": [ - "rai_example_version_string = \"23\"" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "73be2b63", - "metadata": {}, - "source": [ - "## Accessing the Data\n", - "\n", - "We supply the data as a pair of parquet files and accompanying `MLTable` file. We can download them, preprocess them, and take a brief look:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5f875f18", - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import datasets\n", - "import pandas as pd\n", - "\n", - "from sklearn import preprocessing\n", - "\n", - "NUM_TEST_SAMPLES = 100" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ccbfd923", - "metadata": {}, - "outputs": [], - "source": [ - "def load_dataset(split):\n", - " dataset = datasets.load_dataset(\"DeveloperOats/DBPedia_Classes\", split=split)\n", - " return pd.DataFrame({\"text\": dataset[\"text\"], \"l1\": dataset[\"l1\"]})\n", - "\n", - "\n", - "def transform_dataset(dataset):\n", - " dataset[\"label\"] = dataset[\"l1\"]\n", - " dataset = dataset.drop(columns=\"l1\")\n", - " return dataset\n", - "\n", - "\n", - "pd_test_data = load_dataset(\"test\")\n", - "\n", - "pd_test_data = transform_dataset(pd_test_data)\n", - "\n", - "test_data = pd_test_data[:NUM_TEST_SAMPLES]\n", - "\n", - "# Add some known error instances to make the data more interesting\n", - "error_indices = [101, 319, 391, 414, 455, 894, 1078, 1209]\n", - "error_data = pd_test_data.iloc[error_indices]\n", - "test_data = test_data.append(error_data)\n", - "test_data = test_data.reset_index(drop=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "671d286f", - "metadata": {}, - "outputs": [], - "source": [ - "test_data" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "17d53df4", - "metadata": {}, - "source": [ - "Now create the mltable:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4c7bbe58", - "metadata": {}, - "outputs": [], - "source": [ - "pq_filename = \"hf_data.parquet\"\n", - "\n", - "\n", - "def create_ml_table_file_contents(pq_filename):\n", - " return (\n", - " \"$schema: http://azureml/sdk-2-0/MLTable.json\\n\"\n", - " \"type: mltable\\n\"\n", - " \"paths:\\n\"\n", - " \" - file: ./{0}\\n\"\n", - " \"transformations:\\n\"\n", - " \" - read_parquet\\n\"\n", - " ).format(pq_filename)\n", - "\n", - "\n", - "def write_to_parquet(data, path, pq_filename):\n", - " os.makedirs(path, exist_ok=True)\n", - " data.to_parquet(os.path.join(path, pq_filename), index=False)\n", - "\n", - "\n", - "def create_ml_table_file(path, contents):\n", - " with open(os.path.join(path, \"MLTable\"), \"w\") as f:\n", - " f.write(contents)\n", - "\n", - "\n", - "test_data_path = \"test_data\"\n", - "\n", - "write_to_parquet(test_data, test_data_path, pq_filename)\n", - "\n", - "mltable_file_contents = create_ml_table_file_contents(pq_filename)\n", - "create_ml_table_file(test_data_path, mltable_file_contents)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "a2c4ebb4", - "metadata": {}, - "source": [ - "Load some data for a quick view:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1027fa92", - "metadata": {}, - "outputs": [], - "source": [ - "import mltable\n", - "\n", - "tbl = mltable.load(test_data_path)\n", - "test_df: pd.DataFrame = tbl.to_pandas_dataframe()\n", - "\n", - "display(test_df)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "1115ac59", - "metadata": {}, - "source": [ - "The label column contains the classes:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5b42df3d", - "metadata": {}, - "outputs": [], - "source": [ - "target_column_name = \"label\"" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "52e79b04", - "metadata": {}, - "source": [ - "First, we need to upload the datasets to our workspace. We start by creating an `MLClient` for interactions with AzureML:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4570d606", - "metadata": {}, - "outputs": [], - "source": [ - "# Enter details of your AML workspace\n", - "subscription_id = \"\"\n", - "resource_group = \"\"\n", - "workspace = \"\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "395435fc", - "metadata": {}, - "outputs": [], - "source": [ - "# Handle to the workspace\n", - "from azure.ai.ml import MLClient\n", - "from azure.identity import DefaultAzureCredential\n", - "\n", - "try:\n", - " credential = DefaultAzureCredential()\n", - " ml_client = MLClient(\n", - " credential=credential,\n", - " subscription_id=subscription_id,\n", - " resource_group_name=resource_group,\n", - " workspace_name=workspace,\n", - " )\n", - "except Exception:\n", - " # If in compute instance we can get the config automatically\n", - " from azureml.core import Workspace\n", - "\n", - " workspace = Workspace.from_config()\n", - " workspace.write_config()\n", - " ml_client = MLClient.from_config(\n", - " credential=DefaultAzureCredential(exclude_shared_token_cache_credential=True),\n", - " logging_enable=True,\n", - " )\n", - "\n", - "print(ml_client)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "7b501735", - "metadata": {}, - "source": [ - "We can now upload the data to AzureML:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "62eb02a2", - "metadata": {}, - "outputs": [], - "source": [ - "from azure.ai.ml.entities import Data\n", - "from azure.ai.ml.constants import AssetTypes\n", - "\n", - "input_test_data = \"DBPedia_Test_MLTable\"\n", - "\n", - "try:\n", - " test_data = ml_client.data.get(\n", - " name=input_test_data,\n", - " version=rai_example_version_string,\n", - " )\n", - "except Exception:\n", - " test_data = Data(\n", - " path=test_data_path,\n", - " type=AssetTypes.MLTABLE,\n", - " description=\"RAI DBPedia test data\",\n", - " name=input_test_data,\n", - " version=rai_example_version_string,\n", - " )\n", - " ml_client.data.create_or_update(test_data)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "6815ba75", - "metadata": {}, - "source": [ - "# Creating the Model\n", - "\n", - "To simplify the model creation process, we're going to use a pipeline.\n", - "\n", - "We create a directory for the training script:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e78d869b", - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "\n", - "os.makedirs(\"dbpedia_component_src\", exist_ok=True)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "ea86e55d", - "metadata": {}, - "source": [ - "Next, we write out our script to retrieve the trained model:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a523f144", - "metadata": {}, - "outputs": [], - "source": [ - "%%writefile dbpedia_component_src/training_script.py\n", - "\n", - "import argparse\n", - "import logging\n", - "import json\n", - "import os\n", - "import time\n", - "\n", - "\n", - "import mlflow\n", - "import mlflow.pyfunc\n", - "\n", - "import zipfile\n", - "from azureml.core import Run\n", - "\n", - "from transformers import AutoModelForSequenceClassification, \\\n", - " AutoTokenizer, pipeline\n", - "\n", - "from azureml.rai.utils import PyfuncModel\n", - "from raiutils.common.retries import retry_function\n", - "\n", - "try:\n", - " from urllib import urlretrieve\n", - "except ImportError:\n", - " from urllib.request import urlretrieve\n", - "\n", - "\n", - "_logger = logging.getLogger(__file__)\n", - "logging.basicConfig(level=logging.INFO)\n", - "\n", - "\n", - "DBPEDIA_MODEL_NAME = \"dbpedia_model\"\n", - "NUM_LABELS = 9\n", - "\n", - "\n", - "def parse_args():\n", - " # setup arg parser\n", - " parser = argparse.ArgumentParser()\n", - "\n", - " # add arguments\n", - " parser.add_argument(\n", - " \"--model_output_path\", type=str, help=\"Path to write model info JSON\"\n", - " )\n", - " parser.add_argument(\n", - " \"--model_base_name\", type=str, help=\"Name of the registered model\"\n", - " )\n", - " parser.add_argument(\n", - " \"--model_name_suffix\", type=int, help=\"Set negative to use epoch_secs\"\n", - " )\n", - " parser.add_argument(\n", - " \"--device\", type=int, help=(\n", - " \"Device for CPU/GPU supports. Setting this to -1 will leverage \"\n", - " \"CPU, >=0 will run the model on the associated CUDA device id.\")\n", - " )\n", - "\n", - " # parse args\n", - " args = parser.parse_args()\n", - "\n", - " # return args\n", - " return args\n", - "\n", - "\n", - "class FetchModel(object):\n", - " def __init__(self):\n", - " pass\n", - "\n", - " def fetch(self):\n", - " zipfilename = DBPEDIA_MODEL_NAME + '.zip'\n", - " url = ('https://publictestdatasets.blob.core.windows.net/models/' +\n", - " DBPEDIA_MODEL_NAME + '.zip')\n", - " urlretrieve(url, zipfilename)\n", - " with zipfile.ZipFile(zipfilename, 'r') as unzip:\n", - " unzip.extractall(DBPEDIA_MODEL_NAME)\n", - "\n", - "\n", - "def retrieve_dbpedia_model():\n", - " fetcher = FetchModel()\n", - " action_name = \"Model download\"\n", - " err_msg = \"Failed to download model\"\n", - " max_retries = 4\n", - " retry_delay = 60\n", - " retry_function(fetcher.fetch, action_name, err_msg,\n", - " max_retries=max_retries,\n", - " retry_delay=retry_delay)\n", - " model = AutoModelForSequenceClassification.from_pretrained(\n", - " DBPEDIA_MODEL_NAME, num_labels=NUM_LABELS)\n", - " return model\n", - "\n", - "\n", - "def main(args):\n", - " current_experiment = Run.get_context().experiment\n", - " tracking_uri = current_experiment.workspace.get_mlflow_tracking_uri()\n", - " _logger.info(\"tracking_uri: {0}\".format(tracking_uri))\n", - " mlflow.set_tracking_uri(tracking_uri)\n", - " mlflow.set_experiment(current_experiment.name)\n", - "\n", - " _logger.info(\"Getting device\")\n", - " device = args.device\n", - "\n", - " _logger.info(\"Loading parquet input\")\n", - "\n", - " # load the model and tokenizer\n", - " tokenizer = AutoTokenizer.from_pretrained(\"bert-base-uncased\")\n", - " model = retrieve_dbpedia_model()\n", - "\n", - " if device >= 0:\n", - " model = model.cuda()\n", - "\n", - " # build a pipeline object to do predictions\n", - " _logger.info(\"Buildling model\")\n", - " pred = pipeline(\n", - " \"text-classification\",\n", - " model=model,\n", - " tokenizer=tokenizer,\n", - " device=device,\n", - " return_all_scores=True\n", - " )\n", - "\n", - " if args.model_name_suffix < 0:\n", - " suffix = int(time.time())\n", - " else:\n", - " suffix = args.model_name_suffix\n", - " registered_name = \"{0}_{1}\".format(args.model_base_name, suffix)\n", - " _logger.info(f\"Registering model as {registered_name}\")\n", - "\n", - " my_mlflow = PyfuncModel(pred)\n", - "\n", - " # Saving model with mlflow\n", - " _logger.info(\"Saving with mlflow\")\n", - " mlflow.pyfunc.log_model(\n", - " python_model=my_mlflow,\n", - " registered_model_name=registered_name,\n", - " artifact_path=registered_name,\n", - " )\n", - "\n", - " _logger.info(\"Writing JSON\")\n", - " dict = {\"id\": \"{0}:1\".format(registered_name)}\n", - " output_path = os.path.join(args.model_output_path, \"model_info.json\")\n", - " with open(output_path, \"w\") as of:\n", - " json.dump(dict, fp=of)\n", - "\n", - "\n", - "# run script\n", - "if __name__ == \"__main__\":\n", - " # add space in logs\n", - " print(\"*\" * 60)\n", - " print(\"\\n\\n\")\n", - "\n", - " # parse args\n", - " args = parse_args()\n", - "\n", - " # run main function\n", - " main(args)\n", - "\n", - " # add space in logs\n", - " print(\"*\" * 60)\n", - " print(\"\\n\\n\")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "e115dd6e", - "metadata": {}, - "source": [ - "Now, we can build this into an AzureML component:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3d54e43f", - "metadata": {}, - "outputs": [], - "source": [ - "from azure.ai.ml import load_component\n", - "\n", - "yaml_contents = f\"\"\"\n", - "$schema: http://azureml/sdk-2-0/CommandComponent.json\n", - "name: rai_training_component\n", - "display_name: DBPedia training component for RAI example\n", - "version: {rai_example_version_string}\n", - "type: command\n", - "inputs:\n", - " model_base_name:\n", - " type: string\n", - " model_name_suffix: # Set negative to use epoch_secs\n", - " type: integer\n", - " default: -1\n", - " device: # set to >= 0 to use GPU\n", - " type: integer\n", - " default: 0\n", - "outputs:\n", - " model_output_path:\n", - " type: path\n", - "code: ./dbpedia_component_src/\n", - "environment: azureml://registries/azureml/environments/responsibleai-text/versions/13\n", - "command: >-\n", - " python training_script.py\n", - " --model_base_name ${{{{inputs.model_base_name}}}}\n", - " --model_name_suffix ${{{{inputs.model_name_suffix}}}}\n", - " --device ${{{{inputs.device}}}}\n", - " --model_output_path ${{{{outputs.model_output_path}}}}\n", - "\"\"\"\n", - "\n", - "yaml_filename = \"DBPediaTextTrainingComp.yaml\"\n", - "\n", - "with open(yaml_filename, \"w\") as f:\n", - " f.write(yaml_contents)\n", - "\n", - "train_component_definition = load_component(source=yaml_filename)\n", - "\n", - "ml_client.components.create_or_update(train_component_definition)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "6d165e2b", - "metadata": {}, - "source": [ - "We need a compute target on which to run our jobs. The following checks whether the compute specified above is present; if not, then the compute target is created." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1e40fc38", - "metadata": {}, - "outputs": [], - "source": [ - "from azure.ai.ml.entities import AmlCompute\n", - "\n", - "all_compute_names = [x.name for x in ml_client.compute.list()]\n", - "\n", - "if compute_name in all_compute_names:\n", - " print(f\"Found existing compute: {compute_name}\")\n", - "else:\n", - " my_compute = AmlCompute(\n", - " name=compute_name,\n", - " size=\"STANDARD_DS3_V2\",\n", - " min_instances=0,\n", - " max_instances=4,\n", - " idle_time_before_scale_down=3600,\n", - " )\n", - " ml_client.compute.begin_create_or_update(my_compute)\n", - " print(\"Initiated compute creation\")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "9d8eb868", - "metadata": {}, - "source": [ - "## Running a training pipeline\n", - "\n", - "Now that we have our training component, we can run it. We begin by generating a unique name for the mode;" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ad76242b", - "metadata": {}, - "outputs": [], - "source": [ - "import time\n", - "\n", - "model_base_name = \"hf_model\"\n", - "model_name_suffix = \"12492\"\n", - "device = -1" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "d49615a7", - "metadata": {}, - "source": [ - "Next, we define our training pipeline. This has two components. The first is the training component which we defined above. The second is a component to register the model in AzureML:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "cb6c6cec", - "metadata": {}, - "outputs": [], - "source": [ - "from azure.ai.ml import dsl, Input\n", - "\n", - "train_model_component = ml_client.components.get(\n", - " name=\"rai_training_component\", version=rai_example_version_string\n", - ")\n", - "\n", - "\n", - "@dsl.pipeline(\n", - " compute=compute_name,\n", - " description=\"Register Model for RAI DBPedia example\",\n", - " experiment_name=f\"RAI_DBPedia_Example_Model_Training_{model_name_suffix}\",\n", - ")\n", - "def my_training_pipeline(model_base_name, model_name_suffix, device):\n", - " trained_model = train_component_definition(\n", - " model_base_name=model_base_name,\n", - " model_name_suffix=model_name_suffix,\n", - " device=device,\n", - " )\n", - " trained_model.set_limits(timeout=3600)\n", - "\n", - " return {}\n", - "\n", - "\n", - "model_registration_pipeline_job = my_training_pipeline(\n", - " model_base_name, model_name_suffix, device\n", - ")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "2fa66ea6", - "metadata": {}, - "source": [ - "With the training pipeline defined, we can submit it for execution in AzureML. We define a helper function to wait for the job to complete:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f854eef5", - "metadata": {}, - "outputs": [], - "source": [ - "from azure.ai.ml.entities import PipelineJob\n", - "\n", - "\n", - "def submit_and_wait(ml_client, pipeline_job) -> PipelineJob:\n", - " created_job = ml_client.jobs.create_or_update(pipeline_job)\n", - " assert created_job is not None\n", - "\n", - " while created_job.status not in [\n", - " \"Completed\",\n", - " \"Failed\",\n", - " \"Canceled\",\n", - " \"NotResponding\",\n", - " ]:\n", - " time.sleep(30)\n", - " created_job = ml_client.jobs.get(created_job.name)\n", - " print(\"Latest status : {0}\".format(created_job.status))\n", - " assert created_job.status == \"Completed\"\n", - " return created_job\n", - "\n", - "\n", - "# This is the actual submission\n", - "training_job = submit_and_wait(ml_client, model_registration_pipeline_job)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "0722395e", - "metadata": {}, - "source": [ - "## Creating the RAI Text Insights\n", - "\n", - "Now that we have our model, we can generate RAI Text insights for it. We will need the `id` of the registered model, which will be as follows:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7d3e6e6e", - "metadata": {}, - "outputs": [], - "source": [ - "expected_model_id = f\"{model_base_name}_{model_name_suffix}:1\"\n", - "azureml_model_id = f\"azureml:{expected_model_id}\"" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "310aa659", - "metadata": {}, - "source": [ - "Next, we load the RAI components, so that we can construct a pipeline:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d67b942e", - "metadata": {}, - "outputs": [], - "source": [ - "dbpedia_test_mltable = Input(\n", - " type=\"mltable\",\n", - " path=f\"{input_test_data}:{rai_example_version_string}\",\n", - " mode=\"download\",\n", - ")\n", - "\n", - "registry_name = \"azureml\"\n", - "credential = DefaultAzureCredential()\n", - "\n", - "ml_client_registry = MLClient(\n", - " credential=credential,\n", - " subscription_id=ml_client.subscription_id,\n", - " resource_group_name=ml_client.resource_group_name,\n", - " registry_name=registry_name,\n", - ")\n", - "\n", - "rai_text_insights_component = ml_client_registry.components.get(\n", - " name=\"rai_text_insights\", version=version_string\n", - ")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "c98cd2d9", - "metadata": {}, - "source": [ - "We can now specify our pipeline. Complex objects (such as lists of column names) have to be converted to JSON strings before being passed to the components." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a62105a7", - "metadata": {}, - "outputs": [], - "source": [ - "import json\n", - "from azure.ai.ml import Input\n", - "from azure.ai.ml.constants import AssetTypes\n", - "\n", - "\n", - "@dsl.pipeline(\n", - " compute=compute_name,\n", - " description=\"Example RAI computation on DBPedia data\",\n", - " experiment_name=f\"RAI_DBPedia_Example_RAIInsights_Computation_{model_name_suffix}\",\n", - ")\n", - "def rai_dbpedia_text_classification_pipeline(\n", - " target_column_name,\n", - " test_data,\n", - " classes,\n", - " use_model_dependency,\n", - "):\n", - " # Initiate the RAIInsights\n", - " rai_text_job = rai_text_insights_component(\n", - " task_type=\"text_classification\",\n", - " model_info=expected_model_id,\n", - " model_input=Input(type=AssetTypes.MLFLOW_MODEL, path=azureml_model_id),\n", - " test_dataset=test_data,\n", - " target_column_name=target_column_name,\n", - " classes=classes,\n", - " use_model_dependency=use_model_dependency,\n", - " )\n", - " rai_text_job.set_limits(timeout=10800)\n", - "\n", - " rai_text_job.outputs.dashboard.mode = \"upload\"\n", - " rai_text_job.outputs.ux_json.mode = \"upload\"\n", - "\n", - " return {\n", - " \"dashboard\": rai_text_job.outputs.dashboard,\n", - " \"ux_json\": rai_text_job.outputs.ux_json,\n", - " }" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "6b5b14a9", - "metadata": {}, - "source": [ - "Next, we define the pipeline object itself, and ensure that the outputs will be available for download:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e4d86ec2", - "metadata": {}, - "outputs": [], - "source": [ - "import uuid\n", - "from azure.ai.ml import Output\n", - "\n", - "insights_pipeline_job = rai_dbpedia_text_classification_pipeline(\n", - " target_column_name=target_column_name,\n", - " test_data=dbpedia_test_mltable,\n", - " classes=json.dumps(\n", - " [\n", - " \"Agent\",\n", - " \"Device\",\n", - " \"Event\",\n", - " \"Place\",\n", - " \"Species\",\n", - " \"SportsSeason\",\n", - " \"TopicalConcept\",\n", - " \"UnitOfWork\",\n", - " \"Work\",\n", - " ]\n", - " ),\n", - " use_model_dependency=True,\n", - ")\n", - "\n", - "rand_path = str(uuid.uuid4())\n", - "insights_pipeline_job.outputs.dashboard = Output(\n", - " path=f\"azureml://datastores/workspaceblobstore/paths/{rand_path}/dashboard/\",\n", - " mode=\"upload\",\n", - " type=\"uri_folder\",\n", - ")\n", - "insights_pipeline_job.outputs.ux_json = Output(\n", - " path=f\"azureml://datastores/workspaceblobstore/paths/{rand_path}/ux_json/\",\n", - " mode=\"upload\",\n", - " type=\"uri_folder\",\n", - ")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "25f34573", - "metadata": {}, - "source": [ - "And submit the pipeline to AzureML for execution:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2ca757f7", - "metadata": {}, - "outputs": [], - "source": [ - "insights_job = submit_and_wait(ml_client, insights_pipeline_job)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "1381768a", - "metadata": {}, - "source": [ - "The dashboard should appear in the AzureML portal in the registered model view. The following cell computes the expected URI:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e86ab611", - "metadata": {}, - "outputs": [], - "source": [ - "sub_id = ml_client._operation_scope.subscription_id\n", - "rg_name = ml_client._operation_scope.resource_group_name\n", - "ws_name = ml_client.workspace_name\n", - "\n", - "expected_uri = f\"https://ml.azure.com/model/{expected_model_id}/model_analysis?wsid=/subscriptions/{sub_id}/resourcegroups/{rg_name}/workspaces/{ws_name}\"\n", - "\n", - "print(f\"Please visit {expected_uri} to see your analysis\")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "93a8dff9", - "metadata": {}, - "source": [ - "## Constructing the pipeline in YAML\n", - "\n", - "It is also possible to specify the pipeline as a YAML file, and submit that using the command line. We will now create a YAML specification of the above pipeline and submit that:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "624bb0cd", - "metadata": {}, - "outputs": [], - "source": [ - "yaml_contents = f\"\"\"\n", - "$schema: https://azuremlschemas.azureedge.net/latest/pipelineJob.schema.json\n", - "experiment_name: AML_RAI_Text_Sample_{rai_example_version_string}\n", - "type: pipeline\n", - "\n", - "compute: azureml:cpucluster\n", - "\n", - "inputs:\n", - " registered_model_name: hf_model\n", - " hf_model_info: {expected_model_id}\n", - " my_test_data:\n", - " type: mltable\n", - " path: azureml:{input_test_data}:{rai_example_version_string}\n", - " mode: download\n", - "\n", - "settings:\n", - " default_datastore: azureml:workspaceblobstore\n", - " default_compute: azureml:cpucluster\n", - " continue_on_step_failure: false\n", - "\n", - "jobs:\n", - " analyse_model:\n", - " type: command\n", - " component: azureml://registries/azureml/components/rai_text_insights/versions/{version_string}\n", - " inputs:\n", - " task_type: text_classification\n", - " model_input:\n", - " type: mlflow_model\n", - " path: {azureml_model_id}\n", - " model_info: ${{{{parent.inputs.hf_model_info}}}}\n", - " test_dataset: ${{{{parent.inputs.my_test_data}}}}\n", - " target_column_name: {target_column_name}\n", - " maximum_rows_for_test_dataset: 5000\n", - " classes: '[]'\n", - " enable_explanation: True\n", - " enable_error_analysis: True\n", - "\"\"\"\n", - "\n", - "yaml_pipeline_filename = \"rai_text_example.yaml\"\n", - "\n", - "with open(yaml_pipeline_filename, \"w\") as f:\n", - " f.write(yaml_contents)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "1fd5f2dd", - "metadata": {}, - "source": [ - "The created file can then be submitted using the Azure CLI:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3bf9bb1c", - "metadata": {}, - "outputs": [], - "source": [ - "cmd_line = [\n", - " \"az\",\n", - " \"ml\",\n", - " \"job\",\n", - " \"create\",\n", - " \"--resource-group\",\n", - " rg_name,\n", - " \"--workspace\",\n", - " ws_name,\n", - " \"--file\",\n", - " yaml_pipeline_filename,\n", - "]\n", - "\n", - "import subprocess\n", - "\n", - "try:\n", - " cmd = subprocess.run(cmd_line, check=True, shell=True, capture_output=True)\n", - "except subprocess.CalledProcessError as cpe:\n", - " print(f\"Error invoking: {cpe.args}\")\n", - " print(cpe.stdout)\n", - " print(cpe.stderr)\n", - " raise\n", - "else:\n", - " print(\"Azure CLI submission completed\")" - ] - } - ], - "metadata": { - "celltoolbar": "Raw Cell Format", - "kernelspec": { - "display_name": "Python 3.10 - SDK V2", - "language": "python", - "name": "python310-sdkv2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.15" - }, - "vscode": { - "interpreter": { - "hash": "8fd340b5477ca1a0b454d48a3973beff39fee032ada47a04f6f3725b469a8988" - } - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/sdk/python/responsible-ai/text/responsibleaidashboard-text-classification-blbooksgenre.ipynb b/sdk/python/responsible-ai/text/responsibleaidashboard-text-classification-blbooksgenre.ipynb deleted file mode 100644 index a74e132d58..0000000000 --- a/sdk/python/responsible-ai/text/responsibleaidashboard-text-classification-blbooksgenre.ipynb +++ /dev/null @@ -1,1241 +0,0 @@ -{ - "cells": [ - { - "attachments": {}, - "cell_type": "markdown", - "id": "98605bcd", - "metadata": {}, - "source": [ - "# Binary Text Classification scenario with RAI Dashboard as Deployed Endpoint\n", - "\n", - "The [blbooksgenre dataset](https://huggingface.co/datasets/blbooksgenre) classifies various book titles into fiction and nonfiction titles.\n", - "\n", - "In this notebook we will look into this binary text classification scenario end to end where the huggingface model will be deployed as a blackbox model webservice endpoint in AzureML from MLFlow." - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "bca59e45", - "metadata": {}, - "source": [ - "Install datasets to retrieve this dataset from huggingface:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "888ae09f", - "metadata": {}, - "outputs": [], - "source": [ - "%pip install datasets\n", - "%pip install ml-wrappers\n", - "%pip install \"pandas<2.0.0\"" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "fd9b5e44", - "metadata": {}, - "source": [ - "First, we need to specify the version of the RAI components which are available in the workspace. This was specified when the components were uploaded." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "53b4eeac", - "metadata": {}, - "outputs": [], - "source": [ - "version_string = \"0.0.20\"" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "06008690", - "metadata": {}, - "source": [ - "We also need to give the name of the compute cluster we want to use in AzureML. Later in this notebook, we will create it if it does not already exist:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f1ad79f9", - "metadata": {}, - "outputs": [], - "source": [ - "compute_name = \"cpucluster\"" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "9fc65dc7", - "metadata": {}, - "source": [ - "Finally, we need to specify a version for the data and components we will create while running this notebook. This should be unique for the workspace, but the specific value doesn't matter:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "78053935", - "metadata": {}, - "outputs": [], - "source": [ - "rai_example_version_string = \"41\"" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "73be2b63", - "metadata": {}, - "source": [ - "## Accessing the Data\n", - "\n", - "We supply the data as a pair of parquet files and accompanying `MLTable` file. We can download them, preprocess them, and take a brief look:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5f875f18", - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import datasets\n", - "import pandas as pd\n", - "\n", - "from sklearn import preprocessing\n", - "from sklearn.model_selection import train_test_split\n", - "\n", - "NUM_TEST_SAMPLES = 100" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ccbfd923", - "metadata": {}, - "outputs": [], - "source": [ - "def load_dataset(split):\n", - " config_kwargs = {\"name\": \"title_genre_classifiction\"}\n", - " dataset = datasets.load_dataset(\n", - " \"blbooksgenre\", split=split, trust_remote_code=True, **config_kwargs\n", - " )\n", - " return pd.DataFrame({\"text\": dataset[\"title\"], \"label\": dataset[\"label\"]})\n", - "\n", - "\n", - "pd_data = load_dataset(\"train\")\n", - "\n", - "_, pd_test_data = train_test_split(pd_data, test_size=0.2, random_state=0)\n", - "\n", - "test_data = pd_test_data[:NUM_TEST_SAMPLES]" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "17d53df4", - "metadata": {}, - "source": [ - "Now create the mltable:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4c7bbe58", - "metadata": {}, - "outputs": [], - "source": [ - "pq_filename = \"hf_data.parquet\"\n", - "\n", - "\n", - "def create_ml_table_file_contents(pq_filename):\n", - " return (\n", - " \"$schema: http://azureml/sdk-2-0/MLTable.json\\n\"\n", - " \"type: mltable\\n\"\n", - " \"paths:\\n\"\n", - " \" - file: ./{0}\\n\"\n", - " \"transformations:\\n\"\n", - " \" - read_parquet\\n\"\n", - " ).format(pq_filename)\n", - "\n", - "\n", - "def write_to_parquet(data, path, pq_filename):\n", - " os.makedirs(path, exist_ok=True)\n", - " data.to_parquet(os.path.join(path, pq_filename), index=False)\n", - "\n", - "\n", - "def create_ml_table_file(path, contents):\n", - " with open(os.path.join(path, \"MLTable\"), \"w\") as f:\n", - " f.write(contents)\n", - "\n", - "\n", - "test_data_path = \"test_data\"\n", - "\n", - "write_to_parquet(test_data, test_data_path, pq_filename)\n", - "\n", - "mltable_file_contents = create_ml_table_file_contents(pq_filename)\n", - "create_ml_table_file(test_data_path, mltable_file_contents)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "a2c4ebb4", - "metadata": {}, - "source": [ - "Load some data for a quick view:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1027fa92", - "metadata": {}, - "outputs": [], - "source": [ - "import mltable\n", - "\n", - "tbl = mltable.load(test_data_path)\n", - "test_df: pd.DataFrame = tbl.to_pandas_dataframe()\n", - "\n", - "display(test_df)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "1115ac59", - "metadata": {}, - "source": [ - "The label column contains the classes:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5b42df3d", - "metadata": {}, - "outputs": [], - "source": [ - "target_column_name = \"label\"" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "52e79b04", - "metadata": {}, - "source": [ - "First, we need to upload the datasets to our workspace. We start by creating an `MLClient` for interactions with AzureML:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2ac60b07", - "metadata": {}, - "outputs": [], - "source": [ - "# Enter details of your AML workspace\n", - "subscription_id = \"\"\n", - "resource_group = \"\"\n", - "workspace = \"\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "395435fc", - "metadata": {}, - "outputs": [], - "source": [ - "# Handle to the workspace\n", - "from azure.ai.ml import MLClient\n", - "from azure.identity import DefaultAzureCredential\n", - "\n", - "try:\n", - " credential = DefaultAzureCredential()\n", - " ml_client = MLClient(\n", - " credential=credential,\n", - " subscription_id=subscription_id,\n", - " resource_group_name=resource_group,\n", - " workspace_name=workspace,\n", - " )\n", - "except Exception:\n", - " # If in compute instance we can get the config automatically\n", - " from azureml.core import Workspace\n", - "\n", - " workspace = Workspace.from_config()\n", - " workspace.write_config()\n", - " ml_client = MLClient.from_config(\n", - " credential=DefaultAzureCredential(exclude_shared_token_cache_credential=True),\n", - " logging_enable=True,\n", - " )\n", - "\n", - "print(ml_client)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "7b501735", - "metadata": {}, - "source": [ - "We can now upload the data to AzureML:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "62eb02a2", - "metadata": {}, - "outputs": [], - "source": [ - "from azure.ai.ml.entities import Data\n", - "from azure.ai.ml.constants import AssetTypes\n", - "\n", - "input_test_data = \"blbooksgenre_Test_MLTable\"\n", - "\n", - "try:\n", - " test_data = ml_client.data.get(\n", - " name=input_test_data,\n", - " version=rai_example_version_string,\n", - " )\n", - "except Exception:\n", - " test_data = Data(\n", - " path=test_data_path,\n", - " type=AssetTypes.MLTABLE,\n", - " description=\"RAI blbooksgenre test data\",\n", - " name=input_test_data,\n", - " version=rai_example_version_string,\n", - " )\n", - " ml_client.data.create_or_update(test_data)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "6815ba75", - "metadata": {}, - "source": [ - "# Creating the Model\n", - "\n", - "To simplify the model creation process, we're going to use a pipeline.\n", - "\n", - "We create a directory for the training script:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e78d869b", - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "\n", - "os.makedirs(\"blbooksgenre_component_src\", exist_ok=True)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "ea86e55d", - "metadata": {}, - "source": [ - "Next, we write out our script to retrieve the trained model:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a523f144", - "metadata": {}, - "outputs": [], - "source": [ - "%%writefile blbooksgenre_component_src/training_script.py\n", - "\n", - "import argparse\n", - "import logging\n", - "import json\n", - "import os\n", - "import time\n", - "import numpy as np\n", - "\n", - "import mlflow\n", - "import mlflow.pyfunc\n", - "\n", - "import zipfile\n", - "from azureml.core import Run\n", - "\n", - "from transformers import AutoModelForSequenceClassification, \\\n", - " AutoTokenizer, pipeline\n", - "\n", - "from raiutils.common.retries import retry_function\n", - "\n", - "try:\n", - " from urllib import urlretrieve\n", - "except ImportError:\n", - " from urllib.request import urlretrieve\n", - "\n", - "\n", - "_logger = logging.getLogger(__file__)\n", - "logging.basicConfig(level=logging.INFO)\n", - "\n", - "\n", - "BLBOOKSGENRE_MODEL_NAME = \"blbooksgenre_model\"\n", - "NUM_LABELS = 2\n", - "\n", - "\n", - "def parse_args():\n", - " # setup arg parser\n", - " parser = argparse.ArgumentParser()\n", - "\n", - " # add arguments\n", - " parser.add_argument(\n", - " \"--model_output_path\", type=str, help=\"Path to write model info JSON\"\n", - " )\n", - " parser.add_argument(\n", - " \"--model_base_name\", type=str, help=\"Name of the registered model\"\n", - " )\n", - " parser.add_argument(\n", - " \"--model_name_suffix\", type=str, help=\"Model name suffix\"\n", - " )\n", - " parser.add_argument(\n", - " \"--device\", type=int, help=(\n", - " \"Device for CPU/GPU supports. Setting this to -1 will leverage \"\n", - " \"CPU, >=0 will run the model on the associated CUDA device id.\")\n", - " )\n", - "\n", - " # parse args\n", - " args = parser.parse_args()\n", - "\n", - " # return args\n", - " return args\n", - "\n", - "\n", - "class FetchModel(object):\n", - " def __init__(self):\n", - " pass\n", - "\n", - " def fetch(self):\n", - " zipfilename = BLBOOKSGENRE_MODEL_NAME + '.zip'\n", - " url = ('https://publictestdatasets.blob.core.windows.net/models/' +\n", - " BLBOOKSGENRE_MODEL_NAME + '.zip')\n", - " urlretrieve(url, zipfilename)\n", - " with zipfile.ZipFile(zipfilename, 'r') as unzip:\n", - " unzip.extractall(BLBOOKSGENRE_MODEL_NAME)\n", - "\n", - "\n", - "def retrieve_blbooksgenre_model():\n", - " fetcher = FetchModel()\n", - " action_name = \"Model download\"\n", - " err_msg = \"Failed to download model\"\n", - " max_retries = 4\n", - " retry_delay = 60\n", - " retry_function(fetcher.fetch, action_name, err_msg,\n", - " max_retries=max_retries,\n", - " retry_delay=retry_delay)\n", - " model = AutoModelForSequenceClassification.from_pretrained(\n", - " BLBOOKSGENRE_MODEL_NAME, num_labels=NUM_LABELS)\n", - " return model\n", - "\n", - "\n", - "class HuggingfaceWrapper(mlflow.pyfunc.PythonModel):\n", - " def __init__(self, pipeline):\n", - " self._pipeline = pipeline\n", - "\n", - " def predict(self, context, model_input):\n", - " if isinstance(model_input, np.ndarray):\n", - " model_input = model_input.tolist()\n", - " result = self._pipeline(model_input)\n", - " return result\n", - "\n", - "\n", - "def main(args):\n", - " current_experiment = Run.get_context().experiment\n", - " tracking_uri = current_experiment.workspace.get_mlflow_tracking_uri()\n", - " _logger.info(\"tracking_uri: {0}\".format(tracking_uri))\n", - " mlflow.set_tracking_uri(tracking_uri)\n", - " mlflow.set_experiment(current_experiment.name)\n", - "\n", - " _logger.info(\"Getting device\")\n", - " device = args.device\n", - "\n", - " _logger.info(\"Loading parquet input\")\n", - "\n", - " # load the model and tokenizer\n", - " tokenizer = AutoTokenizer.from_pretrained(\"bert-base-uncased\")\n", - " model = retrieve_blbooksgenre_model()\n", - "\n", - " if device >= 0:\n", - " model = model.cuda()\n", - "\n", - " # build a pipeline object to do predictions\n", - " _logger.info(\"Building model\")\n", - " # set to false to only return predicted score in webservice\n", - " return_all_scores = False\n", - " pred = pipeline(\n", - " \"text-classification\",\n", - " model=model,\n", - " tokenizer=tokenizer,\n", - " device=device,\n", - " return_all_scores=return_all_scores\n", - " )\n", - "\n", - " suffix = args.model_name_suffix\n", - " registered_name = \"{0}_{1}\".format(args.model_base_name, suffix)\n", - " _logger.info(f\"Registering model as {registered_name}\")\n", - "\n", - " # my_mlflow = PyfuncModel(pred)\n", - " my_mlflow = HuggingfaceWrapper(pred)\n", - "\n", - " # Saving model with mlflow\n", - " _logger.info(\"Saving with mlflow\")\n", - " mlflow.pyfunc.log_model(\n", - " python_model=my_mlflow,\n", - " registered_model_name=registered_name,\n", - " artifact_path=registered_name,\n", - " pip_requirements=['mlflow', 'torch>=2.2.2', 'transformers>=4.17.0,<=4.44.0', 'numpy<2.0.0'])\n", - "\n", - " _logger.info(\"Writing JSON\")\n", - " dict = {\"id\": \"{0}:1\".format(registered_name)}\n", - " output_path = os.path.join(args.model_output_path, \"model_info.json\")\n", - " with open(output_path, \"w\") as of:\n", - " json.dump(dict, fp=of)\n", - "\n", - "\n", - "# run script\n", - "if __name__ == \"__main__\":\n", - " # add space in logs\n", - " print(\"*\" * 60)\n", - " print(\"\\n\\n\")\n", - "\n", - " # parse args\n", - " args = parse_args()\n", - "\n", - " # run main function\n", - " main(args)\n", - "\n", - " # add space in logs\n", - " print(\"*\" * 60)\n", - " print(\"\\n\\n\")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "e115dd6e", - "metadata": {}, - "source": [ - "Now, we can build this into an AzureML component:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3d54e43f", - "metadata": {}, - "outputs": [], - "source": [ - "from azure.ai.ml import load_component\n", - "\n", - "yaml_contents = f\"\"\"\n", - "$schema: http://azureml/sdk-2-0/CommandComponent.json\n", - "name: rai_training_component\n", - "display_name: blbooksgenre training component for RAI example\n", - "version: {rai_example_version_string}\n", - "type: command\n", - "inputs:\n", - " model_base_name:\n", - " type: string\n", - " model_name_suffix:\n", - " type: string\n", - " device: # set to >= 0 to use GPU\n", - " type: integer\n", - " default: 0\n", - "outputs:\n", - " model_output_path:\n", - " type: path\n", - "code: ./blbooksgenre_component_src/\n", - "environment: azureml://registries/azureml/environments/responsibleai-text/versions/13\n", - "command: >-\n", - " python training_script.py\n", - " --model_base_name ${{{{inputs.model_base_name}}}}\n", - " --model_name_suffix ${{{{inputs.model_name_suffix}}}}\n", - " --device ${{{{inputs.device}}}}\n", - " --model_output_path ${{{{outputs.model_output_path}}}}\n", - "\"\"\"\n", - "\n", - "yaml_filename = \"blbooksgenreTextTrainingComp.yaml\"\n", - "\n", - "with open(yaml_filename, \"w\") as f:\n", - " f.write(yaml_contents)\n", - "\n", - "train_component_definition = load_component(source=yaml_filename)\n", - "\n", - "ml_client.components.create_or_update(train_component_definition)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "6d165e2b", - "metadata": {}, - "source": [ - "We need a compute target on which to run our jobs. The following checks whether the compute specified above is present; if not, then the compute target is created." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1e40fc38", - "metadata": {}, - "outputs": [], - "source": [ - "from azure.ai.ml.entities import AmlCompute\n", - "\n", - "all_compute_names = [x.name for x in ml_client.compute.list()]\n", - "\n", - "if compute_name in all_compute_names:\n", - " print(f\"Found existing compute: {compute_name}\")\n", - "else:\n", - " my_compute = AmlCompute(\n", - " name=compute_name,\n", - " size=\"STANDARD_DS3_V2\",\n", - " min_instances=0,\n", - " max_instances=4,\n", - " idle_time_before_scale_down=3600,\n", - " )\n", - " ml_client.compute.begin_create_or_update(my_compute)\n", - " print(\"Initiated compute creation\")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "9d8eb868", - "metadata": {}, - "source": [ - "## Running a training pipeline\n", - "\n", - "Now that we have our training component, we can run it. We begin by generating a unique name for the mode;" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a80b5b12", - "metadata": {}, - "outputs": [], - "source": [ - "import random\n", - "import string\n", - "\n", - "# Creating a unique model and endpoint name by including a random suffix\n", - "allowed_chars = string.ascii_lowercase + string.digits\n", - "endpoint_suffix = \"\".join(random.choice(allowed_chars) for x in range(5))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ad76242b", - "metadata": {}, - "outputs": [], - "source": [ - "import time\n", - "\n", - "model_base_name = \"blbooksgenre_model\"\n", - "model_name_suffix = endpoint_suffix\n", - "device = -1" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "d49615a7", - "metadata": {}, - "source": [ - "Next, we define our training pipeline. This has two components. The first is the training component which we defined above. The second is a component to register the model in AzureML:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "cb6c6cec", - "metadata": {}, - "outputs": [], - "source": [ - "from azure.ai.ml import dsl, Input\n", - "\n", - "train_model_component = ml_client.components.get(\n", - " name=\"rai_training_component\", version=rai_example_version_string\n", - ")\n", - "\n", - "\n", - "@dsl.pipeline(\n", - " compute=compute_name,\n", - " description=\"Register Model for RAI blbooksgenre example\",\n", - " experiment_name=f\"RAI_blbooksgenre_Example_Model_Training_{model_name_suffix}\",\n", - ")\n", - "def my_training_pipeline(model_base_name, model_name_suffix, device):\n", - " trained_model = train_component_definition(\n", - " model_base_name=model_base_name,\n", - " model_name_suffix=model_name_suffix,\n", - " device=device,\n", - " )\n", - " trained_model.set_limits(timeout=3600)\n", - "\n", - " return {}\n", - "\n", - "\n", - "model_registration_pipeline_job = my_training_pipeline(\n", - " model_base_name, model_name_suffix, device\n", - ")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "2fa66ea6", - "metadata": {}, - "source": [ - "With the training pipeline defined, we can submit it for execution in AzureML. We define a helper function to wait for the job to complete:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f854eef5", - "metadata": {}, - "outputs": [], - "source": [ - "from azure.ai.ml.entities import PipelineJob\n", - "\n", - "\n", - "def submit_and_wait(ml_client, pipeline_job) -> PipelineJob:\n", - " created_job = ml_client.jobs.create_or_update(pipeline_job)\n", - " assert created_job is not None\n", - "\n", - " while created_job.status not in [\n", - " \"Completed\",\n", - " \"Failed\",\n", - " \"Canceled\",\n", - " \"NotResponding\",\n", - " ]:\n", - " time.sleep(30)\n", - " created_job = ml_client.jobs.get(created_job.name)\n", - " print(\"Latest status : {0}\".format(created_job.status))\n", - " print(\"pipeline job outputs: \", created_job.outputs)\n", - " print(created_job.outputs)\n", - " # get job details\n", - " print(created_job)\n", - " # stream the job logs\n", - " ml_client.jobs.stream(name=created_job.name)\n", - " assert created_job.status == \"Completed\"\n", - " return created_job\n", - "\n", - "\n", - "# This is the actual submission\n", - "training_job = submit_and_wait(ml_client, model_registration_pipeline_job)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "36ee01e0", - "metadata": {}, - "source": [ - "Now we can define the registered model id:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6f9364c7", - "metadata": {}, - "outputs": [], - "source": [ - "model_name = f\"{model_base_name}_{model_name_suffix}\"\n", - "model_version = 1" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "39e98d0e", - "metadata": {}, - "source": [ - "### Create Scoring Endpoint " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3ee0683b", - "metadata": {}, - "outputs": [], - "source": [ - "endpoint_name = \"blbooksgenre-classifier-\" + endpoint_suffix\n", - "\n", - "print(f\"Endpoint name: {endpoint_name}\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "49583af0", - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import mlflow\n", - "\n", - "mlflow_tracking_key = \"MLFLOW_TRACKING_URI\"\n", - "\n", - "if mlflow_tracking_key in os.environ:\n", - " target_uri = os.environ[mlflow_tracking_key]\n", - "else:\n", - " ws = ml_client.workspaces.get(name=workspace)\n", - " target_uri = ws.mlflow_tracking_uri\n", - " mlflow.set_tracking_uri(target_uri)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d0b9713f", - "metadata": {}, - "outputs": [], - "source": [ - "from mlflow.deployments import get_deploy_client\n", - "\n", - "deployment_client = get_deploy_client(target_uri)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "066bff17", - "metadata": {}, - "outputs": [], - "source": [ - "endpoint = deployment_client.create_endpoint(endpoint_name)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d6adcde5", - "metadata": {}, - "outputs": [], - "source": [ - "scoring_uri = deployment_client.get_endpoint(endpoint=endpoint_name)[\"properties\"][\n", - " \"scoringUri\"\n", - "]\n", - "print(scoring_uri)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "8aad33f2", - "metadata": {}, - "source": [ - "### Create a deployment" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "fe1dcb36", - "metadata": {}, - "outputs": [], - "source": [ - "deployment_name = \"default\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "59600239", - "metadata": {}, - "outputs": [], - "source": [ - "deploy_config = {\n", - " \"instance_type\": \"Standard_DS4_v2\",\n", - " \"instance_count\": 1,\n", - " \"readiness_probe\": {\"initial_delay\": 60, \"timeout\": 5},\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8a57c773", - "metadata": {}, - "outputs": [], - "source": [ - "import json\n", - "\n", - "deployment_config_path = \"deployment_config.json\"\n", - "with open(deployment_config_path, \"w\") as outfile:\n", - " outfile.write(json.dumps(deploy_config))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8d154900", - "metadata": {}, - "outputs": [], - "source": [ - "f\"models:/{model_name}/{model_version}\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "314aa10d", - "metadata": {}, - "outputs": [], - "source": [ - "deployment = deployment_client.create_deployment(\n", - " name=deployment_name,\n", - " endpoint=endpoint_name,\n", - " model_uri=f\"models:/{model_name}/{model_version}\",\n", - " config={\"deploy-config-file\": deployment_config_path},\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ce6b6c82", - "metadata": {}, - "outputs": [], - "source": [ - "traffic_config = {\"traffic\": {deployment_name: 100}}" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a13eb07d", - "metadata": {}, - "outputs": [], - "source": [ - "traffic_config_path = \"traffic_config.json\"\n", - "with open(traffic_config_path, \"w\") as outfile:\n", - " outfile.write(json.dumps(traffic_config))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9379c15d", - "metadata": {}, - "outputs": [], - "source": [ - "deployment_client.update_endpoint(\n", - " endpoint=endpoint_name,\n", - " config={\"endpoint-config-file\": traffic_config_path},\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f3224875", - "metadata": {}, - "outputs": [], - "source": [ - "endpoint_secret_key = ml_client.online_endpoints.get_keys(\n", - " name=endpoint_name\n", - ").access_token" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "1dfff912", - "metadata": {}, - "source": [ - "Now let's wrap the registered endpoint and register it as a model in azureml:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "52b43f7b", - "metadata": {}, - "outputs": [], - "source": [ - "from ml_wrappers.model import EndpointWrapperModel\n", - "\n", - "extra_headers = {\"azureml-model-deployment\": \"default\"}\n", - "endpoint_wrapper = EndpointWrapperModel(\n", - " endpoint_secret_key,\n", - " scoring_uri,\n", - " extra_headers,\n", - " transform_output_dict=True,\n", - " class_names=[\"LABEL_0\", \"LABEL_1\"],\n", - " wrap_input_data_dict=True,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e4ed64a7", - "metadata": {}, - "outputs": [], - "source": [ - "endpoint_wrapper.predict(test_df.iloc[0:2][\"text\"].tolist())" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "26e8e45a", - "metadata": {}, - "source": [ - "Now we can register the endpoint wrapper using mlflow as another model:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d28aaa31", - "metadata": {}, - "outputs": [], - "source": [ - "endpoint_model_name = \"wrapped_endpoint_\" + model_name\n", - "expected_model_id = f\"{endpoint_model_name}:{model_version}\"\n", - "azureml_model_id = f\"azureml:{expected_model_id}\"\n", - "mlflow.pyfunc.log_model(\n", - " python_model=endpoint_wrapper,\n", - " registered_model_name=endpoint_model_name,\n", - " artifact_path=endpoint_model_name,\n", - " pip_requirements=[\"ml-wrappers\"],\n", - ")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "0722395e", - "metadata": {}, - "source": [ - "## Creating the RAI Text Insights\n", - "\n", - "Now that we have our model, we can generate RAI Text insights for it.\n", - "Next, we load the RAI components, so that we can construct a pipeline:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d67b942e", - "metadata": {}, - "outputs": [], - "source": [ - "blbooksgenre_test_mltable = Input(\n", - " type=\"mltable\",\n", - " path=f\"{input_test_data}:{rai_example_version_string}\",\n", - " mode=\"download\",\n", - ")\n", - "\n", - "registry_name = \"azureml\"\n", - "credential = DefaultAzureCredential()\n", - "\n", - "ml_client_registry = MLClient(\n", - " credential=credential,\n", - " subscription_id=ml_client.subscription_id,\n", - " resource_group_name=ml_client.resource_group_name,\n", - " registry_name=registry_name,\n", - ")\n", - "\n", - "rai_text_insights_component = ml_client_registry.components.get(\n", - " name=\"rai_text_insights\", version=version_string\n", - ")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "c98cd2d9", - "metadata": {}, - "source": [ - "We can now specify our pipeline. Complex objects (such as lists of column names) have to be converted to JSON strings before being passed to the components." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a62105a7", - "metadata": {}, - "outputs": [], - "source": [ - "import json\n", - "from azure.ai.ml import Input\n", - "from azure.ai.ml.constants import AssetTypes\n", - "\n", - "\n", - "@dsl.pipeline(\n", - " compute=compute_name,\n", - " description=\"Example RAI computation on blbooksgenre data\",\n", - " experiment_name=f\"RAI_blbooksgenre_Example_RAIInsights_Computation_{model_name_suffix}\",\n", - ")\n", - "def rai_blbooksgenre_text_classification_pipeline(\n", - " target_column_name,\n", - " test_data,\n", - " classes,\n", - " use_model_dependency,\n", - "):\n", - " # Initiate the RAIInsights\n", - " rai_text_job = rai_text_insights_component(\n", - " task_type=\"text_classification\",\n", - " model_info=expected_model_id,\n", - " model_input=Input(type=AssetTypes.MLFLOW_MODEL, path=azureml_model_id),\n", - " test_dataset=test_data,\n", - " target_column_name=target_column_name,\n", - " classes=classes,\n", - " use_model_dependency=use_model_dependency,\n", - " )\n", - " rai_text_job.set_limits(timeout=7200)\n", - "\n", - " rai_text_job.outputs.dashboard.mode = \"upload\"\n", - " rai_text_job.outputs.ux_json.mode = \"upload\"\n", - "\n", - " return {\n", - " \"dashboard\": rai_text_job.outputs.dashboard,\n", - " \"ux_json\": rai_text_job.outputs.ux_json,\n", - " }" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "6b5b14a9", - "metadata": {}, - "source": [ - "Next, we define the pipeline object itself, and ensure that the outputs will be available for download:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e4d86ec2", - "metadata": {}, - "outputs": [], - "source": [ - "import uuid\n", - "from azure.ai.ml import Output\n", - "\n", - "insights_pipeline_job = rai_blbooksgenre_text_classification_pipeline(\n", - " target_column_name=target_column_name,\n", - " test_data=blbooksgenre_test_mltable,\n", - " classes=\"[]\",\n", - " use_model_dependency=True,\n", - ")\n", - "\n", - "rand_path = str(uuid.uuid4())\n", - "insights_pipeline_job.outputs.dashboard = Output(\n", - " path=f\"azureml://datastores/workspaceblobstore/paths/{rand_path}/dashboard/\",\n", - " mode=\"upload\",\n", - " type=\"uri_folder\",\n", - ")\n", - "insights_pipeline_job.outputs.ux_json = Output(\n", - " path=f\"azureml://datastores/workspaceblobstore/paths/{rand_path}/ux_json/\",\n", - " mode=\"upload\",\n", - " type=\"uri_folder\",\n", - ")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "25f34573", - "metadata": {}, - "source": [ - "And submit the pipeline to AzureML for execution:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2ca757f7", - "metadata": {}, - "outputs": [], - "source": [ - "insights_job = submit_and_wait(ml_client, insights_pipeline_job)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "1381768a", - "metadata": {}, - "source": [ - "The dashboard should appear in the AzureML portal in the registered model view. The following cell computes the expected URI:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e86ab611", - "metadata": {}, - "outputs": [], - "source": [ - "sub_id = ml_client._operation_scope.subscription_id\n", - "rg_name = ml_client._operation_scope.resource_group_name\n", - "ws_name = ml_client.workspace_name\n", - "\n", - "expected_uri = f\"https://ml.azure.com/model/{expected_model_id}/model_analysis?wsid=/subscriptions/{sub_id}/resourcegroups/{rg_name}/workspaces/{ws_name}\"\n", - "\n", - "print(f\"Please visit {expected_uri} to see your analysis\")" - ] - } - ], - "metadata": { - "celltoolbar": "Raw Cell Format", - "kernelspec": { - "display_name": "Python 3.10 - SDK V2", - "language": "python", - "name": "python310-sdkv2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", 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"source": [ + "# 📝 Agenda\n", + "\n", + "This notebook guides you through the end-to-end process of fine-tuning the **Qwen2.5-7B-Instruct** model into a **reasoning model** using medical data on **Azure ML**. Qwen2.5-7B-Instruct is an instruction-tuned large language model developed by Alibaba Cloud, based on their Qwen2.5-7B foundation model. It is optimized for following human instructions across a wide range of tasks, such as question answering, code generation, and language understanding. In this walkthrough, one will learn how to enhance the model's reasoning capabilities using **Reinforced Fine-Tuning (RFT)** techniques, with a focus on **GRPO (**G**roup **R**elative **P**olicy **O**ptimization)**.\n", + "\n", + "\"image.png\"\n", + "\n", + "-------------------------------------------------------------------------------------------------------" + ] + }, + { + "cell_type": "markdown", + "id": "c3348434", + "metadata": {}, + "source": [ + "# ⚙️ Section 1: Setup - AML Resources\n", + "\n", + "Install the necessary packages and CLI tools to get started with Azure Machine Learning:\n", + "\n", + "- **azure-core**: Provides core utilities and HTTP infrastructure used by all Azure SDKs for Python.\n", + "- **azure-ai-ml**: The Python SDK used to interact with Azure Machine Learning for managing and running ML workflows.\n", + "- **rich**: A library for rendering richly formatted text, tables, and progress bars directly in the terminal.\n", + "- **huggingface_hub**: Lets you download, upload, and manage models and datasets from the Hugging Face Hub.\n", + "- **AzureCLI**: The `az` command-line interface used to manage Azure resources and services from your terminal.\n", + "\n", + "✅ These tools form the foundation for orchestrating scalable and efficient ML workloads on Azure." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "01ab214d", + "metadata": {}, + "outputs": [], + "source": [ + "%%capture\n", + "\n", + "# Installing Azure cli and Azure SDK for Python.\n", + "! pip install azure-core azure-ai-ml rich huggingface_hub\n", + "! curl -sL https://aka.ms/InstallAzureCLIDeb | sudo bash" + ] + }, + { + "cell_type": "markdown", + "id": "0793aea2", + "metadata": {}, + "source": [ + "In this example, a **Hugging Face access token** is used to download the **Qwen2.5-7B-Instruct model**. This token is required only for the initial run to access the gated model; subsequent runs will use the cached copy of the model from the workspace." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "42f85ed9", + "metadata": { + "gather": { + "logged": 1747816782954 + } + }, + "outputs": [], + "source": [ + "# Set your Hugging Face token here (generate one at https://huggingface.co/settings/tokens)\n", + "# This token is required the first time you run the script, to download gated models from Hugging Face.\n", + "! export HF_TOKEN=\"hf_xxxxxxxxxxxxx\"" + ] + }, + { + "cell_type": "markdown", + "id": "d1f43523-ccbf-422a-b694-2ba267f45ef5", + "metadata": { + "nteract": { + "transient": { + "deleting": false + } + } + }, + "source": [ + "The Azure Machine Learning (AML) **setup process is encapsulated** into a script that provisions all required resources in the workspace. \\\n", + "By the end of the setup, the AML workspace will be fully configured with the below resources: \n", + "\n", + "- **Dataset** : [MedMCQA](https://medmcqa.github.io): A Large-scale Multi-Subject Multi-Choice Dataset for Medical domain Question Answering. We use a modified version of the MedMCQA dataset, restricting our experiments to question/answer pairs having only a single correct answer. The modified dataset used in the demo can be found in `datasets/med_mcqa`\n", + "- **Model** : [Qwen2_5-7B-Instruct_base](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct)\n", + "- **Compute Cluster**: STANDARD_ND96ISR_H100_V5 cluster with at least 2 nodes \n", + "- **Environment**: Is designed for GRPO specific large-scale, distributed training and inference of reasoning models using Azure Machine Learning, TRL, DeepSpeed, vLLM, and LoRA." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1b41ca80", + "metadata": { + "gather": { + "logged": 1747816824407 + }, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "from aml_setup import (\n", + " setup,\n", + ") # This script sets up the Azure ML client, model, and environment.\n", + "\n", + "ml_client, med_mcqa_data, model, compute, environment = setup()" + ] + }, + { + "cell_type": "markdown", + "id": "26cd924e", + "metadata": {}, + "source": [ + "The complete dataset can be found in datasets/med_mcqa. Here is a sample of the dataset :\n", + "\n", + "## Sample DataSet\n", + "\n", + "📘 When solving a multiple-choice question, we ask the LLM to follow this structure:\n", + "\n", + "**Think out loud**: Explain your reasoning step by step. Wrap this part in tags. \n", + "**Give your final answer**: Clearly state your chosen option (A, B, C, or D) and explain why it's correct. Wrap this part in tags.
\n", + "**Final Answer line**: On a new line, write Final Answer: followed by just one letter — A, B, C, or D. \n", + "\n", + "✅ **Example Question**: \n", + "\n", + "```text\n", + "CSF Rhinorrhea occurs due to damage of:\n", + "\n", + "Options:\n", + "\n", + "A. Roof of orbit \n", + "B. Cribriform plate of ethmoidal bone \n", + "C. Frontal sinus \n", + "D. Sphenoid bone\n", + "```\n", + "**Ideal reasoning model response:**\n", + "```text\n", + "\n", + "\n", + "Start by identifying the anatomical structure most commonly associated with CSF (cerebrospinal fluid) leakage. CSF rhinorrhea typically results from a breach in the skull base, especially the cribriform plate of the ethmoid bone, which is thin and located near the nasal cavity.\n", + "\n", + "\n", + "\n", + "The cribriform plate of the ethmoid bone is the most common site of CSF leakage into the nasal cavity, leading to CSF rhinorrhea. This makes option B the correct answer. \n", + "\n", + "B\n", + "\n", + "Final Answer: B\n", + "```\n" + ] + }, + { + "cell_type": "markdown", + "id": "cc71ed6c", + "metadata": {}, + "source": [ + "---------------------------------------------------------------------------------------\n" + ] + }, + { + "cell_type": "markdown", + "id": "5497f1aa-513e-48ec-8c46-812f169c2975", + "metadata": { + "nteract": { + "transient": { + "deleting": false + } + } + }, + "source": [ + "# 🧩 Section 2: How to train a Reasoning Model on AML Using GRPO Trainer\n", + "\n", + "

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