Awesome

This is a template to initialize PyTorch projects that use as a backbone framework PyTorch Lightning. The project is very simple and minimalistic and serves as a bootstrap in order to avoid rewriting the same boilerplate code every time a new project is created. Finally, in order to organize the configuration files and all the hyperparameters we utilize Hydra, a framework from Facebook Research built for "elegantly configuring complex applications".

Repository Structure

p-lightning-template
| conf                      # contains Hydra config files
  | data
  | model
  | train
  root.yaml                 # hydra root config file
| data                      # datasets should go here
| experiments               # where the models are stored
| src
  | pl_data_modules.py      # base LightinigDataModule
  | pl_modules.py           # base LightningModule
  | train.py                # main script for training the network
| README.md
| requirements.txt
| setup.sh                  # environment setup script 

The structure of the repository is very simplistic and involves mainly four components:

• pl_data_modules.py where you can declare your LightningDataModules.
• pl_modules.py where you can declare your LightningModules.
• train.py the main script to start the training phase.
• conf the directory containing the config files of Hydra.

Initializing the environment

In order to set up the python interpreter we utilize conda , the script setup.sh creates a conda environment and install pytorch and the dependencies in "requirements.txt".

Using the repository

To use this repository as a starting template for your projects, you can just click the green button "Use this template" at the top of this page. More on using GitHub repositories on the following link.

FAQ

Q: When I run any script using a Hydra config I can see that relative paths do not work. Why?

A: Whenever you run a script that uses a Hydra config, Hydra will create a new working directory (specified in the root.yaml file) for you. Every relative path you will use will start from it, and this is why you get the 'FileNotFound' error. However, using a different working directory for each of your experiments has a couple of benefits that you can read in the Hydra documentation for the Working directory. There are several ways that hydra offers as a workaround for this problem here we will report the two that the authors of this repository use the most, but you can find the other on the link we previously mentioned:

1. You can use the 'hydra.utils.to_absolute_path' function that will convert every relative path starting from your working directory (p-lightning-template in this project) to a full path that will be accessible from inside the new working dir.

2. Hydra will provide you with a reference to the original working directory in your config files. You can access it under the name of 'hydra:runtime.cwd'. So, for example, if your training dataset has the relative path 'data/train.tsv' you can convert it to a full path by prepending the hydra variable before

Contributing

Contributions are always more than welcome, the only thing to take into account when submitting a pull request is that we utilize the Black code formatter with a max length for the code of 120. More pragmatically you should ensure to utilize the command "black -l 120" on the whole "src" directory before pushing the code.

Other useful repositories

This repository has been created with the idea of providing a simple skeleton from which you can start a PyTorch Lightning project. Instead of favoring the customizability, we favored the simplicity and we intended this template as a base for building more specific templates based on the user needs (for example by forking this one). However, there are several other repositories with different features that you can check if interested. We will list two of them here:

• lucmos/nn-template: a very nice template with support for DVC.
• hobogalaxy/lightning-hydra-template: another useful and very well documented template repository.

As we were mentioning earlier we created this repository even with the intention of bootstrapping other Lightning templates. We mention here these repositories:

• poccio/grid-seq2seq: This is a NLP oriented repository that serves as a skeleton for Seq2Seq projects, while at the same time offering a way of running Bart based Seq2Seq models without writing a single line of code! it in the yaml files by doing that following: '${hydra:runtime.cwd}/data/train.tsv'.

Final remark, if your project involves Natural Language Processing, you might want to take a look at Classy. Classy is a simple-to-use library (that I co-authored) for building high-performance Machine Learning models in NLP, and it should be a very good start for almost every NLP project.