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Python MLOps Cookbook

This is an example of a Containerized Flask Application the can be the core ingredient in many "recipes", i.e. deploy targets..

Github Container Registery

Feel free to test my ML project: docker pull ghcr.io/noahgift/python-mlops-cookbook:latest

Assets in repo

Makefile: View Makefile
requirements.txt: View requirements.txt
cli.py: View cli.py
utilscli.py: View utilscli.py
app.py: View app.py
mlib.py: View mlib.pyModel Handling Library
htwtmlb.csv: View CSV Useful for input scaling
model.joblib: View model.joblib
Dockerfile: View Dockerfile
Baseball_Predictions_Export_Model.ipynb: Baseball_Predictions_Export_Model.ipynb

CLI Tools

There are two cli tools. First, the main cli.py is the endpoint that serves out predictions. To predict the height of an MLB player you use the following: ./cli.py --weight 180

The second cli tool is utilscli.py and this perform model retraining, and could serve as the entry point to do more things. For example, this version doesn't change the default model_name, but you could add that as an option by forking this repo.

./utilscli.py retrain --tsize 0.4

Here is an example retraining the model.

Additionally the you can query the API via the CLI allowing you to change both the host and the value passed into the API. This is accomplished through the requests library.

./utilscli.py predict --weight 400

Flask Microservice

The Flask ML Microservice can be run many ways.

Containerized Flask Microservice Locally

You can run the Flask Microservice as follows with the commmand: python app.py.

(.venv) ec2-user:~/environment/Python-MLOps-Cookbook (main) $ python app.py 
 * Serving Flask app "app" (lazy loading)
 * Environment: production
   WARNING: This is a development server. Do not use it in a production deployment.
   Use a production WSGI server instead.
 * Debug mode: on
INFO:werkzeug: * Running on http://127.0.0.1:8080/ (Press CTRL+C to quit)
INFO:werkzeug: * Restarting with stat
WARNING:werkzeug: * Debugger is active!
INFO:werkzeug: * Debugger PIN: 251-481-511

To serve a prediction against the application, run the predict.sh.

(.venv) ec2-user:~/environment/Python-MLOps-Cookbook (main) $ ./predict.sh                             
Port: 8080
{
  "prediction": {
    "height_human_readable": "6 foot, 2 inches", 
    "height_inches": 73.61
  }
}

Containerized Flask Microservice

Here is an example of how to build the container and run it locally, this is the contents of predict.sh

#!/usr/bin/env bash

# Build image
#change tag for new container registery, gcr.io/bob
docker build --tag=noahgift/mlops-cookbook . 

# List docker images
docker image ls

# Run flask app
docker run -p 127.0.0.1:8080:8080 noahgift/mlops-cookbook

Automatically Build Container via Github Actions and Push to Github Container Registery

To setup the container build process do the following. This is also covered by Alfredo Deza in Practical MLOps book in greater detail.

  build-container:
    runs-on: ubuntu-latest
    steps:
    - uses: actions/checkout@v2
    - name: Loging to Github registry
      uses: docker/login-action@v1
      with:
        registry: ghcr.io
        username: ${{ github.repository_owner }}
        password: ${{ secrets.BUILDCONTAINERS }}
    - name: build flask app
      uses: docker/build-push-action@v2
      with:
        context: ./
        #tags: alfredodeza/flask-roberta:latest
        tags: ghcr.io/noahgift/python-mlops-cookbook:latest
        push: true

Automatically Build Container via Github Actions and Push to Dockerhub Container Registery

Build Targets

With the project using DevOps/MLOps best practices including linting, testing, and deployment, this project can be the base to deploy to many deployment targets.

[In progress....]