environments documentation

Environments

Categories

• AutoML Image training

• Common

• Demand Forecasting

• Designer

• Inference

• Labeling

• Model monitoring

• Models

• Olive

• prompt flow

• PyTorch

• Responsible AI

• Retrieval Augmented Generation

• Scikit-learn

• Tensorflow

• Training

All environments

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• acft-hf-nlp-gpu

  Environment used by Hugging Face NLP Finetune components

• acft-mmdetection-image-gpu

  Environment used by MMDetection Image Finetune components

• acft-transformers-image-gpu

  Environment used by HuggingFace Transformers Image Finetune components

• acpt-automl-image-framework-selector-gpu

  Environment used by framework selector component for automl image workloads

• acpt-pytorch-1.13-cuda11.7

  Recommended environment for Deep Learning with PyTorch on Azure containing the Azure ML SDK with the latest compatible versions of Ubuntu, Python, PyTorch, CUDA\RocM, combined with optimizers like ORT Training,+DeepSpeed+MSCCL+ORT MoE and more. The image introduces preview of new fastcheckpointin...

• acpt-pytorch-2.0-cuda11.7

  Recommended environment for Deep Learning in public preview with PyTorch on Azure containing the Azure ML SDK with the latest compatible versions of Ubuntu, Python, PyTorch, CUDA\RocM, combined with optimizers like ORT Training,+DeepSpeed+MSCCL+ORT MoE and more. The image introduces newly release...

• automl-dnn-vision-gpu

  GPU based environment for finetuning AutoML legacy models for image tasks.

• automl-gpu

  An environment for automl inferencing (part of demand forecasting).

• data-labeling

  CPU based environment for AML Data Labeling.

• data-labeling-sam

  GPU based environment for AML Data Labeling SAM Embedding Generation.

• foundation-model-inference

  Environment used for deploying model to use DS-MII for inference

• lightgbm-3.3

  An environment for tasks such as regression, clustering, and classification with LightGBM. Contains the Azure ML SDK and additional python packages.

• llm-auto-prompt

  An environment for auto prompt components.

• llm-dbcopilot-embeddings

  An environment for Large Language Model Retrieval Augmented Generation standard grounding database components.

• llm-dbcopilot-mir

  An environment for Large Language Model MIR endpoint components.

• llm-rag

  An environment for standard Large Language Model Retrieval Augmented Generation components.

• llm-rag-embeddings

  An environment for standard Large Language Model Retrieval Augmented Generation embedding components.

• minimal-ubuntu20.04-py38-cpu-inference

  AzureML minimal/Ubuntu 20.04/Python 3.8 cpu environment.

• minimal-ubuntu20.04-py38-cuda11.6.2-gpu-inference

  AzureML minimal/Ubuntu 20.04/Python 3.8 gpu environment.

• mldesigner

  CPU based environment for pipelines (ML Designer).

• mldesigner-minimal

  CPU based environment for pipelines (ML Designer) minimal version.

• model-evaluation

  Environment for evaluating mlflow models.

• model-management

  Environment used by Model Management components

• model-monitor-spark-standalone

  Spark environment for running model monitor jobs on AzureML compute clusters.

• model-monitor-spark-standalone-feature-attribution

  Spark environment for running model monitor feature attribution jobs on AzureML compute clusters.

• olive-optimizer

  An environment for olive_optimizer cpu components.

• olive-optimizer-gpu

  An environment for olive_optimizer_gpu components.

• promptflow-runtime

  Runtime environment for PromptFlow, refer to our CHANGELOG for more details.

• python-sdk-v2

  Environment containing Azure SDK for Python v2

• responsibleai-text-ubuntu20.04-py38-cpu

  AzureML Responsible AI Text environment.

• responsibleai-ubuntu20.04-py38-cpu

  AzureML Responsible AI environment.

• responsibleai-vision-ubuntu20.04-py38-cpu

  AzureML Responsible AI Vision environment.

• sklearn-1.0

  An environment for tasks such as regression, clustering, and classification with Scikit-learn. Contains the Azure ML SDK and additional python packages.

• sklearn-1.1

  An environment for tasks such as regression, clustering, and classification with Scikit-learn. Contains the Azure ML SDK and additional python packages.

• tensorflow-2.11-cuda11

  An environment for deep learning with Tensorflow containing the Azure ML SDK and additional python packages.

• tensorflow-2.12-cuda11

  An environment for deep learning with Tensorflow containing the Azure ML SDK and additional python packages.