lightningtrain 0.0.4

PyTorch Lightning Project Setup

LightningTrain

A clean and simple PyTorch Lightning + Hydra projects.

📌 Introduction

This repository contains a simple PyTorch Lightning + Hydra project template. It is designed to be a starting point for your own projects. It is based on the PyTorch Lightning.

📦 Main Technologies

PyTorch Lightning - a lightweight PyTorch wrapper for high-performance AI research. Think of it as a framework for organizing your PyTorch code.

Hydra - a framework for elegantly configuring complex applications. The key feature is the ability to dynamically create a hierarchical configuration by composition and override it through config files and the command line.

📁 Project Structure

The directory structure of new project looks like this:

configs
│   ├── data                     <- Data configs
configs
│   ├── hydra                    <- Hydra configs
│   ├── local                    <- Local configs
│   ├── model                    <- Model configs
│   ├── paths                    <- Project paths configs
│   ├── trainer                  <- Trainer configs
│   ├── eval.yaml             <- Main config for evaluation
│   └── train.yaml            <- Main config for training
│
├── data                   <- Project data
│
├── logs                   <- Logs generated by hydra and lightning loggers
│
├── notebooks              <- Jupyter notebooks. Naming convention is a number (for ordering),
│                             the creator's initials, and a short `-` 
│
├── lightningtrain                    <- Source code
│   ├── data                     <- Data scripts
│   ├── models                   <- Model scripts
│   ├── utils                    <- Utility scripts
│   │
│   ├── eval.py                  <- Run evaluation
│   └── train.py                 <- Run training
│
├── tests                  <- Tests of any kind
│
├── .gitignore                <- List of files ignored by git
├── .project-root             <- File for inferring the position of project root directory
├── Makefile                  <- Makefile with commands like `make train` or `make test`
├── pyproject.toml            <- Configuration options for testing and linting
├── requirements.txt          <- File for installing python dependencies
├── setup.py                  <- File for installing project as a package
└── README.md

🚀 Quickstart

# clone project
git clone https://github.com/dlwizard/lightningflow.git
cd lightningflow

# create docker container with .devcontainer.json
# or install dependencies locally

# install project as a package
pip install -e .

# run training
lightningtrain_train data.num_workers=16

# run evaluation
lightningtrain_eval data.num_workers=16

📝 Docker container usage instructions

Prerequisites:

• Docker
• Visual Studio Code
• Remote - Containers extension

Steps:

1. Clone this repository
2. Open the repository in Visual Studio Code
3. press crtl+shift+p and select "Remote-Containers: Reopen in Container"
4. Wait for the container to build
5. Open a terminal in Visual Studio Code and run the following commands:

# install project as a package
pip install -e .

# run training
lightningtrain_train data.num_workers=16

# run evaluation
lightningtrain_eval data.num_workers=16

📝 Instructions for PyPi package usage

Prerequisites:

• Python 3.10
• pip
• virtualenv

Steps:

1. python -m venv venv
2. source venv\Scripts\activate
3. python3 -m pip install lightningtrain
4. lightningtrain_train data.num_workers=16
5. lightningtrain_eval data.num_workers=16

References

• PyTorch Lightning
• Hydra