Torch Lab is a hackable template for artificial intelligence and machine learning projects using the Meta and GCP ecosystems.
torchlab.data/ contains the CLI application implemented with Typer.
torchlab.data/ contains code for preprocessing pipelines and PyTorch dataset utilities.
torchlab.models/ contains code for model architectures implemented in PyTorch.
torchlab.observe/ contains code for model observability.
torchlab.serve/ contains code to serve a selected model.
torchlab.train/ contains code for several varieties of Trainers.
torchlab.tune/ contains code for HPO runs and sweeps.
torchlab.utils/ contains utility functions.
checkpoints directory contains training checkpoints and the pre-trained production model.
data directory for local data caches.
docs directory for technical documentation.
logs directory contains logs generated from experiment managers and profilers.
notebooks directory can be used to present EDA and demo notebooks.
requirements directory of requirement files titled by purpose.
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.
The recommended installation is as follows:
python3 -m venv .venv
source .venv/bin/activate
pip install -e ".[dev]"