README.md

pmip

Predictive Models in Production

Local dev

Pro tips

If you ever find yourself with a "no space left on device" error when building the Docker image, try

docker rm $(docker ps -q -f 'status=exited')
docker rmi $(docker images -q -f "dangling=true")

Build the base training image

bash scripts/build_training_image.sh

Do interactive model training and data exploration in the Jupyter notebook

ENVIRONMENT=dev bash scripts/run_training_container.sh -c "jupyter notebook notebooks/ --allow-root --ip=0.0.0.0 --port=8888 --no-browser"

Then open http://localhost:8888 to run Jupyter.

If you need to enter into the container's shell, do this.

ENVIRONMENT=dev bash scripts/run_training_container.sh -

Train a model programmatically

ENVIRONMENT=dev bash scripts/run_training_container.sh scripts/train.sh

Pushing the new Docker image to production for the training and API services

If this is your first or only ECR repo, then run

bash scripts/push_training_image.sh $(aws ecr describe-repositories | jq -r '.repositories[0].repositoryUri')

You have have multiple ECR repos you'll have to change the argument so that it points to the one you want to push to.

Build the API image

bash scripts/build_api_image.sh

Run the API locally in debugging mode

ENVIRONMENT=dev bash scripts/run_api_container.sh "python -m pmip.routes"

Run the API locally.

ENVIRONMENT=dev bash scripts/run_api_container.sh

Drop into the container.

ENVIRONMENT=dev bash scripts/run_api_container.sh -

Resources

• https://github.com/pypa/sampleproject/blob/master/setup.py