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Lightning Lab

Overview

Lightning Lab is a public template for artificial intelligence and machine learning research projects using Lightning AI's PyTorch Lightning.

[!NOTE] Lightning Lab was featured by Weights and Biases in this community spotlight

The Structure

Source Module

lab.components/ contains experiment utilities grouped by purpose for cohesion.

lab.cli contains code for the command line interface built with Typer.

lab.config assists with project, trainer, and sweep configurations.

lab.datamodule contains code for the LightningDataModule.

lab.dataset contains code for the Torch Dataset.

lab.module contains code for the LightningModule.

lab.trainer contains code for the Lightning Trainer.

Project Root

checkpoints directory contains training checkpoints and the pre-trained production model.

data directory should be used to cache the TorchDataset and training splits locally if the size of the dataset allows for local storage. additionally, this directory should be used to cache predictions during HPO sweeps.

docs directory should be used for technical documentation.

logs directory contains logs generated from experiment managers and profilers.

notebooks directory can be used to present exploratory data analysis, explain math concepts, and create a presentation notebook to accompany a conference style paper.

requirements directory should mirror base requirements and extras found in setup.cfg. the requirements directory and requirements.txt at root are required by the basic Coverage GitHub Action.

tests module contains unit and integration tests targeted by pytest.

setup.py setup.cfg pyproject.toml and MANIFEST.ini assist with packaging the Python project.

.pre-commit-config.yaml is required by pre-commit to install its git-hooks.

Installation

Lightning Lab installs minimal requirements out of the box, and provides extras to make creating robust virtual environments easier. To view the requirements, in setup.cfg, see install_requires for the base requirements and options.extras_require for the available extras.

The recommended install is as follows:

python3 -m venv .venv
source .venv/bin/activate
pip install -e ".[all, { domain extra(s) }]"

where { domain extra(s) } is one of, or some combination of (vision, text, audio, rl, forecast) e.g.

python3 -m venv .venv
source .venv/bin/activate
pip install -e ".[all, forecast]"

[!NOTE] Do not install multiple variations of Lightning Lab into a single virtual environment. As this will override the studio CLI for each new variation that is installed.

Refactoring the Template

Lightning Lab is a great template for deep learning projects. Using the template will require some refactoring if you intend to rename src/lab to something like src/textlab. You can refactor in a few simple steps in VS Code:

  1. Start by renaming the src/lab to something like src/textlab or src/imagenetlab. Doing so will allow VS Code to refactor all instance of lab that exists in any .py file.
  2. Open the search pane in VS Code and search for lightniglab in tests/ and replace those occurences with whatever you have renamed the source module to.
  3. Next, search for lab and replace those occurences in all .toml .md cfg files and string occurences in .py files.
  4. Next, search for Lightning Lab and change that to your repo name.
  5. Next, search for my name – Justin Goheen and replace that with either your name or GitHub username.
  6. Next, search once again for my name as jxtngx and do the following:
  7. replace the occurences in mkdocs.yml with your GitHub username.
  8. replace the occurences in authors.yml with your choice of author name for your docs and blog.