Mimimal Viable Deep Learning Infrastructure

Deep learning pipelines are hard to reason about and difficult to code consistently.

Instead of remembering where to put everything and making a different choice for each project, this repository is an attempt to standardize on good defaults.

Think of it like a mini-pytorch lightening, with all the fory internals exposed for extension and modification.

Usage

Install:

Install the conda requirements:

make install

Which is a proxy for calling:

conda env updates -n ml_pipeline --file environment.yml

Run:

Run the code on MNIST with the following command:

make run

Tutorial

The motivation for building a template for deep learning pipelines is this: deep learning is hard enough without every code baase being a little different.

Especially in a research lab, standardizing on a few components makes switching between projects easier.

In this template, you'll see the following:

• src/model, src/config, storage, test dirs.
• if __name__ == "__main__" tests.
• Hydra config.
• dataloader, optimizer, criterion, device, state are constructed in main, but passed to an object that runs batches.
• tqdm to track progress.
• debug config flag enables lots breakpoints.
• python type hints.
• a launch.sh script to dispatch training.
• a Makefile to install and run stuff.
• automatic linting with the black package.
• collate functions!