jiz148 / pyml

Python Machine Learning Practice

PyML

Python Moonlight (Machine Learning) Practice

Contents

• Design
• Documentation
• Prerequisites Checklist
• Dev Setup
• Testing
• Run

Prerequisites

• Python 3
• Python 3 pip version 19.0.1 and up
• Python 3 built-in virtual env venv
• System tools: find, rm, tee, xargs, zip (for building, e.g. AWS Lambda package)
• Command line JSON processor: jq
• Docker (optional)

Dev Setup

Running a dev-setup script to install the project and libraries.

make clean dev-setup  # this will create a python virtualenv

Testing

After running make dev-setup, the project and libraries are installed (in python virtual environment). Now it is able to run tests.

make test  # also available to run `make unittest` or `make nosetest`

or to start a clean test (highly recommended before committing changes) -

make clean test-all

and open test coverage report

make show  # must be on docker host

Run

API Example

• run Gunicorn/Flask API in docker container

  make run-api
  

• run Gunicorn/Flask example in virtual environment (venv)

  # start/enable venv (e.g. `source .venv/bin/activate`)
  make run-gunicorn
  

• run Gunicorn/FastApi in venv

  # start/enable venv (e.g. `source .venv/bin/activate`)
  make run-fastapi
  

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