Fabric Lab

Overview

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

The recommended way for Fabric Lab users to create new repos is with the use this template button.

The example uses a language transformer borrowed from the PyTorch Lightning demos.

Source Module

fabriclab.core contains code for the Lightning Module and Trainer.

fabriclab.components contains tasks grouped by purpose for cohesion.

fabriclab.pipeline contains code for data acquistion and preprocessing, and building a TorchDataset.

fabriclab.serve contains code for model serving APIs built with FastAPI.

fabriclab.cli contains code for the command line interface built with Typerand Rich.

fabriclab.app contains code for data apps built with Streamlit.

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

Project Root

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 to store technical documentation.

logs directory will store logs generated from experiment managers and profilers.

models directory will store training checkpoints and the pre-trained ONNX model.

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 CircleCI 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.

Base Requirements and Extras

Fabric 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 ".[full, { 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 ".[full, text]"

Using Fabric Lab

After installing fabric-lab, you can run the demo with:

lab run demo