README.md

Workshop: "ML in Production" on practice

Workshop and discord

• For a video lecture explaining this code, watch this webinar
• For support and questions, join this Discord server

Tools to install

• docker
• kind
• kubectl
• k9s

Setup end2end

bash ./script/build_docker.sh
bash ./script/start_all.sh

Clean up

./script/start_all.sh

Setup development

python -m venv ~/env/
source ~/env/bin/activate
pip install -r requirements.txt

Observe

k9s -A

kubectl port-forward --address 0.0.0.0 svc/data-labeling 6900:6900
kubectl port-forward --address 0.0.0.0 svc/monitoring-custom 8081:8080
kubectl port-forward --address 0.0.0.0 svc/monitoring-open 8082:8080
kubectl port-forward --address 0.0.0.0 svc/serving-custom-model 8001:80
kubectl port-forward --address 0.0.0.0 svc/serving-open-model 8002:80

Setup creds

export ARGILLA_URI=http://0.0.0.0:6900
export ARGILLA_KEY=adminadmin
export ARGILLA_NAMESPACE=admin
expoer HF_TOKEN=hf_your_token

Data

python end2end/data.py load-text-to-sql-dataset
python end2end/data.py load-data-for-labeling --dataset-name text2sql --sample --num-sample 10000

Reference

• https://docs.argilla.io/en/latest/getting_started/quickstart_workflow_feedback.html
• https://github.com/argilla-io/argilla

Experiments

python end2end/experiments.py --model_name google/flan-t5-small --dataset_name text2sql-workshop --api_url ${ARGILLA_URI} --api_key ${ARGILLA_KEY} --workspace ${ARGILLA_NAMESPACE} --output_dir result-flan-t5-small --overwrite_output_dir --do_train --do_eval --evaluation_strategy steps --per_device_train_batch_size 16 --per_device_eval_batch_size 16 --learning_rate 1e-3 --num_train_epochs 1000 --hub_model_id kyryl-opens-ml/flan-t5-small-sql --hub_token ${HF_TOKEN}

accelerate launch end2end/experiments.py --model_name google/flan-t5-small --dataset_name text2sql-workshop --api_url ${ARGILLA_URI} --api_key ${ARGILLA_KEY} --workspace ${ARGILLA_NAMESPACE} --output_dir result-flan-t5-small --overwrite_output_dir --do_train --do_eval --evaluation_strategy steps --per_device_train_batch_size 16 --per_device_eval_batch_size 16 --learning_rate 1e-3 --num_train_epochs 1000 --hub_model_id kyryl-opens-ml/flan-t5-small-sql --hub_token ${HF_TOKEN}

Reference

• https://arxiv.org/abs/2210.11416
• https://github.com/huggingface/peft
• https://huggingface.co/codellama/CodeLlama-7b-hf
• https://huggingface.co/google/gemma-7b
• https://www.anyscale.com/blog/fine-tuning-llms-lora-or-full-parameter-an-in-depth-analysis-with-llama-2

Pipeline

dagster dev -f end2end/pipeline.py -p 3000 -h 0.0.0.0

Reference

• https://dagster.io/blog/finetuning-llms

Serving

docker run --shm-size 1g -p 8080:80 ghcr.io/huggingface/text-generation-inference:1.4 --model-id kyryl-opens-ml/flan-t5-small-sql

Reference

• https://huggingface.co/docs/text-generation-inference/en/index
• https://github.com/predibase/lorax

Monitoring

streamlit run --server.port 8080 --server.address 0.0.0.0 end2end/monitoring_ui.py

Reference

• https://docs.argilla.io/en/latest/getting_started/quickstart_workflow_feedback.html