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:

directory structure

• src/model

• src/config

• data/

• test/

  • pytest: unit testing.
    • good for data shape
    • TODO:

• docs/

  • switching projects is easier with these in place
  • organize them

• **/__init__.py

  • creates modules out of dir.
  • import module works with these.

• README.md

  • root level required.
  • can exist inside any dir.

• environment.yml

• Makefile

  • to install and run stuff.
  • houses common operations and scripts.

• launch.sh

  • script to dispatch training.

testing

• if __name__ == "__main__".
  • good way to test things
• enables lots breakpoints.

config

• Hydra config.
  • quickly experiment with hyperparameters
  • good way to define env. variables
    • lr, workers, batch_size
    • debug

data

• collate functions!

formatting python

• python type hints.
• automatic linting with the black package.

running

• tqdm to track progress.

architecture

• dataloader, optimizer, criterion, device, state are constructed in main, but passed to an object that runs batches.