Deep Waste REST API

January 6, 2024 · View on GitHub

A RESTful API to return predictions from a trained ML model, built with Python 3 and Flask-RESTX. The neural network used to trained the model can be found here.

Preview

https://deep-waste-rest-api.fly.dev/api/

Development set-up instructions

First, open a command line interface and clone the GitHub repo in your workspace

PS > cd $WORKSPACE_PATH$
PS > git clone https://github.com/sumn2u/ml_rest_api
PS > cd ml_rest_api

Create and activate a Python virtual environment, then install the required Python packages using pip

PS > virtualenv venv
PS > venv/scripts/activate.ps1
(venv) PS > pip install -r requirements.txt

Once dependencies are installed, set up the project for development

(venv) PS > pip install -e .

Finally, run the project:

(venv) PS > python ml_rest_api/app.py

Open the URL http://localhost:8888/api/ with your browser and see the sample Swagger documentation

Interfaces exposed

Swagger JSON available from URL http://localhost:8888/api/swagger.json

Health

These two methods are meant to be used as the liveness and readiness probes in a Kubernetes deployment:

GET http://localhost:8888/api/liveness returns 200/"Alive" if the service is up and running
GET http://localhost:8888/api/readiness returns 200/"Ready" or 503/"Not Ready" depending on whether the ML model has been correctly initialised or not

Model

POST http://localhost:8888/api/model/predict will return a prediction using the ML model. The data_point structure shows the JSON argument that must be supplied, and example values for each of the fields. The service will validate that all the mandatory values are passed. Return values are:
- 200/Predicted value based on JSON input
- 400/Validation error if any mandatory parameter is missing or if any wrong data type (e.g. str, int, bool, datetime...) is supplied
- 500/"Internal Server Error" as catch-all exception handler
- 503/"Not Ready" if model is not initialised

Config settings

Configuration parameters are contained in the file ml_rest_api/settings.py, but they can also be overriden by setting env vars:

settings: Dict = {
    # Flask settings
    "FLASK_SERVER_NAME": "localhost:8888",
    "FLASK_HOST": "0.0.0.0",
    "FLASK_PORT": 8888,
    "FLASK_DEBUG": True,  # Do not use debug mode in production
    # Flask-RESTX settings
    "SWAGGER_UI_DOC_EXPANSION": "list",
    "RESTX_VALIDATE": True,
    "RESTX_MASK_SWAGGER": False,
    "ERROR_404_HELP": False,
    "SWAGGER_UI_JSONEDITOR": True,
    # Flask-WTF settings
    "WTF_CSRF_ENABLED": True,
    # Trained ML/AI model settings
    "TRAINED_MODEL_MODULE_NAME": "ml_trained_model",
    # Module settings
    "MULTITHREADED_INIT": True,
}

Parameter	Values	Details
FLASK_SERVER_NAME	e.g.: localhost:8888	Flask server name
FLASK_HOST	e.g.: 0.0.0.0	Leave as 0.0.0.0 to avoid virtual host filtering
FLASK_PORT	e.g.: 8888	Choose whatever suits you, go crazy
FLASK_DEBUG	False/True	Do not use debug mode in production
SWAGGER_UI_DOC_EXPANSION	'none', 'list' or 'full'	Explained here: https://flask-restx.readthedocs.io/en/stable/swagger.html#customization
RESTX_VALIDATE	False/True	Explained here: https://flask-restx.readthedocs.io/en/stable/swagger.html#the-api-expect-decorator
RESTX_MASK_SWAGGER	False/True	Explained here: https://flask-restx.readthedocs.io/en/stable/mask.html#usage
ERROR_404_HELP	False/True	Explained here: https://flask-restx.readthedocs.io/en/stable/quickstart.html#endpoints
WTF_CSRF_ENABLED	False/True	Enable CSRF protection using Flask-WTF pip module
SWAGGER_UI_JSONEDITOR	False/True	Enable a JSON editor in the Swagger interface
TRAINED_MODEL_MODULE_NAME	e.g.: ml_trained_model	Name of the Python module that initialises the ML model and returns predictions (see section below)
MULTITHREADED_INIT	False/True	Multi-threaded initialisation of the trained model

The trained ML model is meant to be initialised and invoked to make predictions in the context of a Python unit saved inside the directory ml_rest_api/ml_trained_model. The structure of this Python module is explained in this document

Build automation

This project is built into a container image using GitHub Actions and pushed to the Docker Hub at https://hub.docker.com/r/iamsuman/ml-rest-api/

Running the Docker container

> docker run -d -p 8888:8888 ml-rest-api:latest

Open the URL http://localhost:8888/api/ with your browser and see the sample Swagger documentation

Acknowledgements

This projects borrows heavily from the work done by Michał Karzyński: