ML

This repo is intended for accelerating ML education and application development. Refer to the project wiki for setup, resources and hands-on tutorials.

Introduction

This repository contains foundation classes and utilities for ML applications. It is under development and subject to change and thus recommended to use this library as a git submodule.

Installation

1. Install the latest Miniconda on the official website

2. Create a conda environment with python=3.7+

   conda create -n ml37 python=3.7
   

3. Restart the terminal to activate the conda env

   conda activate ml37
   

4. Clone the repo and enter the directory

   git clone --recursive https://gitlab.com/necla-ml/ml.git ML
   cd ML
   

5. Add the dependency channels to ~/.condarc

   cat recipe/.condarc >> ~/.condarc
   

6. Install the dependencies

   conda install ml
   

Local Development

To utilize GPUs and compile CUDA modules, additional GPU packages are necessary:

• cudatoolkit as a dependency of pytorch should have been installed
• cudatoolkit-dev requires extra space >=16GB for installation

To contribute to this project, follow the development flow:

1. Fork this repo in the beginning

2. Uninstall ML through conda remove --force ml

3. Switch to the dev branch for development and testing followed by merge back to main

   make pull      # Pull submodules recursively
   make dev-setup # Switch to dev branch and build the package for local installation
   git commit ... # Check in modified files
   git push       # Push to the dev branch on the repo
   make merge     # Merge back to the main branch and make a pull request afterwards
   

Conda Distribution

After the merge, one may tag a version and build a conda package for distribution as follows:

make tag version=x.y.z
make conda-build