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pmip

Predictive Models in Production

Follow the tutorial that goes with this repository on Medium at

https://medium.com/@fwhigh/predictive-models-in-production-c0228767c006.

Requirements

  • AWS CLI (brew install awscli)
  • AWS EB CLI (brew install awsebcli)
  • Docker
  • An AWS account if you're going to deploy things there

Workflow

This is the regular workflow once you've got the whole system set up.

  1. Edit code and commit to Github.
  2. Build the Docker image locally.
  3. Push the Docker image to the AWS ECR repository (command below).
  4. Manually upload the latest model to AWS S3 s3:///models/staging/YYYYMMDD/, where YYYYMMDD is today.
  5. Deploy the AWS ElasticBeanstalk API with eb deploy. This should pick up the S3 model you just uploaded.
  6. Run the AWS Batch training job. This will make a new model (again) and restart the EB application (again).

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")

I recommend setting BUCKET in ~/.profile.

export BUCKET=<your-S3-bucket>

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 BUCKET=$BUCKET bash scripts/run_training_container.sh -

Train a model programmatically

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

Pushing the new Docker image to production for the training and Flask 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')

If 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 Flask API image

bash scripts/build_api_image.sh

Run the Flask API locally

Run the Flask API locally outside of the Docker container.

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

Then open http://localhost:8000 to view the Swagger documentation and issue API calls.

Run the Flask API locally inside your Docker container.

ENVIRONMENT=dev BUCKET=$BUCKET bash scripts/run_api_container.sh

Then open http://localhost:8000 to view the Swagger documentation and issue API calls.

Drop into the Flask API container.

ENVIRONMENT=dev BUCKET=$BUCKET bash scripts/run_api_container.sh -

Deploy the ElasticBeanstalk

If this is the first time, run

eb init

To deploy run

eb deploy

Push the Lambda API to Lambda

BUCKET=$BUCKET serverless deploy --region $([ -z "$AWS_DEFAULT_REGION" ] && aws configure get region || echo "$AWS_DEFAULT_REGION")

Issue API calls.

curl -X GET https://lrgbpftjy3.execute-api.us-west-1.amazonaws.com/dev/healthcheck
curl -X GET https://lrgbpftjy3.execute-api.us-west-1.amazonaws.com/dev/model-info
curl -X POST -d '{"comments":["Check out this free stuff","I take issue with your characterization"]}' \
    https://lrgbpftjy3.execute-api.us-west-1.amazonaws.com/dev/predict?flavor=class

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