README.md

Catalyst

High-level utils for PyTorch DL/RL research. It was developed with a focus on reproducibility, fast experimentation and code/ideas/models reusing. Being able to research/develop something new, rather then write another regular train loop. Best coding practices included.

Features

• Universal train/inference loop.
• Key-values storages.
• Data and model usage standardization.
• Configuration files for model/data hyperparameters.
• Loggers and Tensorboard support.
• Reproducibility – even source code will be saved.
• 1Cycle and LRFinder support.
• FP16 support.
• Corrected weight decay (AdamW).
• N-best-checkpoints saving (SWA).
• Training stages support.
• Logdir autonaming based on hyperparameters.
• Callbacks – reusable train/inference pipeline parts.
• Well structured and production friendly.
• Lots of reusable code for different purposes: losses, optimizers, models, knns, embeddings projector.

Catalyst is compatible with: Python 3.6+. PyTorch 0.4.1+.

Stable branch - master. Development branch - dev.

Usage

git submodule add https://github.com/Scitator/catalyst.git catalyst

Examples

https://github.com/Scitator/catalyst-examples

Dependencies

pip install git+https://github.com/pytorch/tnt.git@master \
    tensorboardX jpeg4py albumentations

Docker

See ./docker for more information and examples.

Contribution guide

Autoformatting code

We use yapf for linting, and the config file is located at .style.yapf. We recommend running yapf.sh prior to pushing to format changed files.

Linter

To run the Python linter on a specific file, run something like flake8 dl/scripts/train.py. You may need to first run pip install flake8.

See codestyle.md for more information.