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Python Machine Learning Practice

License: GNU General Public License v3.0

Dockerfile 0.42% Makefile 4.50% Python 74.00% Shell 20.18% Go 0.90%

pyml's Introduction

PyML

Python Moonlight (Machine Learning) Practice

Code Coverage Build Status


Contents


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
    


Code Coverage Build Status License: GPL v3 Contributors


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