Machine learning serving

A book about how to create a production level machine learning serving API with Flask, Gunicorn and Docker for people who have more of a mathematical background than computer science.

The framework to create the book is jupyter books: https://jupyterbook.org/intro.html

The full rendered book can be viewed via: https://eligijus112.github.io/api-book/

Contributions

If you enjoyed the book and feel like donating, feel free to do so. The link to do a one time donation is via Stripe.

Additionaly, if you want me to add another chapter or to expand an existing one, please create an issue on Github.

Creating the virtual environment

virtualenv api_book
source api_book/bin/activate
pip install -r requirements.txt

To add the virtual environment to Jupyter notebooks use the command:

python -m ipykernel install --user --name=api_book

Creating and editing notebooks

One of the main types of pages in this book are jupyter notebooks. To start a jupyter notebook session use the command:

jupyter notebook

Necesary command to run before building the book

There are some APIs that need to be running localy in order for the book to render. The commands to run the APIs:

Root calculation API example

cd api-book/chapter-5-API/api_full_example
uvicorn app:app --port 7999

JWT token example

cd api-book/chapter-6-production-tools/jwt_token_example
uvicorn app:app --port 8000

Local ML API

cd api-book/chapter-7-final-serving/ML_API
uvicorn app:app --port 8001

Rendering the book localy

Before rendering the book be sure to activate the virtual environment!

source api_book/bin/activate

Building of the book:

jupyter-book build api-book

Publishing the book to github

To publish the rendered book to github use the command:

cd api-book
ghp-import -n -p -f _build/html

Serving the book via docker and Nginx

To serve the book across multiple computers, servers or whatever place that Docker is installed in, we can build an image and create a container with which we can access the book.

The internal container port that serves the book content is 80 (the default Nginx port).

Building an image localy

The created html of the book can be accesed via api-book/_build/html. To create a docker container that serves the book using nginx, first build the image:

docker build -t eligijusbujokas/ml-serving-book[:<tag>] . 

Running the container on port 4000 on the local machine:

docker run -p 4000:80 eligijusbujokas/ml-serving-book[:<tag>]

The [:] part is optional and should be used only for the collaborators of this project.

To access the book, in your web browser go to:

http://localhost:4000

And the book should be rendered.

Using DockerHub

After the building of the image, you can push it to DockerHub. The command to push the image to DockerHub is:

docker push eligijusbujokas/ml-serving-book[:<tag>]

The book is publicly available as an image in DockerHub. The address of the repository is:

https://hub.docker.com/repository/docker/eligijusbujokas/ml-serving-book/general

To pull the latest image use:

docker pull eligijusbujokas/ml-serving-book:latest

To create the container with the access to the book use:

docker run -d -p 4000:80 eligijusbujokas/ml-serving-book