How to properly ship and deploy your machine learning model

A practical guide with FastAPI, Docker and GitHub Actions

Project setup

1. Create the virtual environment.

virtualenv /path/to/venv --python=/path/to/python3

You can find out the path to your python3 interpreter with the command which python3.

2. Activate the environment and install dependencies.

source /path/to/venv/bin/activate
pip install -r requirements.txt

3. Launch the service

uvicorn api.main:app

Posting requests locally

When the service is running, try

127.0.0.1/docs

or

curl

Deployment with Docker

1. Build the Docker image

docker build --file Dockerfile --tag fastapi-ml-quickstart .

2. Running the Docker image

docker run -p 8000:8000 fastapi-ml-quickstart

3. Entering into the Docker image

docker run -it --entrypoint /bin/bash fastapi-ml-quickstart

docker-compose

1. Launching the service

docker-compose up

This command looks for the docker-compose.yaml configuration file. If you want to use another configuration file, it can be specified with the -f switch. For example

2. Testing

docker-compose -f docker-compose.test.yaml up --abort-on-container-exit --exit-code-from fastapi-ml-quickstart

Reference: https://towardsdatascience.com/how-to-properly-ship-and-deploy-your-machine-learning-model-8a8664b763c4