# catalyst

**Repository Path**: githubGAN/catalyst

## Basic Information

- **Project Name**: catalyst
- **Description**: Reproducible and fast DL & RL.
- **Primary Language**: Unknown
- **License**: Apache-2.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2019-10-29
- **Last Updated**: 2021-11-03

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

<div align="center">

[![Catalyst logo](https://raw.githubusercontent.com/catalyst-team/catalyst-pics/master/pics/catalyst_logo.png)](https://github.com/catalyst-team/catalyst)

**Reproducible and fast DL & RL**
 
[![Pipi version](https://img.shields.io/pypi/v/catalyst.svg)](https://pypi.org/project/catalyst/)
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[![PyPI Status](https://pepy.tech/badge/catalyst)](https://pepy.tech/project/catalyst)
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</div>

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

Break the cycle - use the Catalyst!

---

#### Installation

Common installation:
```bash
pip install -U catalyst
```

More specific with additional requirements:
```bash
pip install catalyst[dl] # installs DL based catalyst with Weights & Biases support
pip install catalyst[rl] # installs DL+RL based catalyst
pip install catalyst[drl] # installs DL+RL based catalyst with Weights & Biases support
pip install catalyst[contrib] # installs DL+contrib based catalyst
pip install catalyst[all] # installs everything. Very convenient to deploy on a new server
```

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

#### Docs and examples
- Detailed [classification tutorial](./examples/notebooks/classification-tutorial.ipynb) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/catalyst-team/catalyst/blob/master/examples/notebooks/classification-tutorial.ipynb)
- Advanced [segmentation tutorial](./examples/notebooks/segmentation-tutorial.ipynb) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/catalyst-team/catalyst/blob/master/examples/notebooks/segmentation-tutorial.ipynb)
- Comprehensive [classification pipeline](https://github.com/catalyst-team/classification).

API documentation and an overview of the library can be found here
[![Docs](https://img.shields.io/badge/dynamic/json.svg?label=docs&url=https%3A%2F%2Fpypi.org%2Fpypi%2Fcatalyst%2Fjson&query=%24.info.version&colorB=brightgreen&prefix=v)](https://catalyst-team.github.io/catalyst/index.html).

In the **[examples folder](examples)** 
of the repository, you can find advanced tutorials and Catalyst best practices.

##### Blog
To learn more about Catalyst internals and to be aware of the most important features, you can read **[Catalyst-info](https://github.com/catalyst-team/catalyst-info)**, our blog where we regularly write facts about the framework.

##### Awesome list of Catalyst-powered repositories

We supervise the **[Awesome Catalyst list](https://github.com/catalyst-team/awesome-catalyst-list)**. You can make a PR with your project to the list.

##### Releases
We release a major release once a month with a name like `YY.MM`.
And micro-releases with hotfixes and framework improvements in the format `YY.MM.#`.

You can view the changelog on the **[GitHub Releases](https://github.com/catalyst-team/catalyst/releases)** page.

Current version: [![Pipi version](https://img.shields.io/pypi/v/catalyst.svg)](https://pypi.org/project/catalyst/)

## Overview

Catalyst helps you write compact
but full-featured DL & RL pipelines in a few lines of code.
You get a training loop with metrics, early-stopping, model checkpointing
and other features without the boilerplate.

#### Features

- Universal train/inference loop.
- Configuration files for model/data hyperparameters.
- Reproducibility – all source code and environment variables will be saved.
- Callbacks – reusable train/inference pipeline parts.
- Training stages support.
- Easy customization.
- PyTorch best practices (SWA, AdamW, 1Cycle, Ranger optimizer, FP16 and more).


#### Structure

-  **DL** – runner for training and inference,
   all of the classic machine learning and computer vision metrics
   and a variety of callbacks for training, validation
   and inference of neural networks.
-  **RL** – scalable Reinforcement Learning,
   on-policy & off-policy algorithms and their improvements
   with distributed training support.
-  **contrib** - additional modules contributed by Catalyst users.
-  **data** - useful tools and scripts for data processing.


## Getting started: 30 seconds with Catalyst

```python
import torch
from catalyst.dl import SupervisedRunner

# experiment setup
logdir = "./logdir"
num_epochs = 42

# data
loaders = {"train": ..., "valid": ...}

# model, criterion, optimizer
model = Net()
criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters())
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer)

# model runner
runner = SupervisedRunner()

# model training
runner.train(
    model=model,
    criterion=criterion,
    optimizer=optimizer,
    scheduler=scheduler,
    loaders=loaders,
    logdir=logdir,
    num_epochs=num_epochs,
    verbose=True
)
```

For Catalyst.RL introduction, please follow [OpenAI Gym example](https://github.com/catalyst-team/catalyst/tree/master/examples/rl_gym).


## Docker

Please see the [docker folder](docker) 
for more information and examples.


## Contribution guide

We appreciate all contributions. 
If you are planning to contribute back bug-fixes, 
please do so without any further discussion. 
If you plan to contribute new features, utility functions or extensions, 
please first open an issue and discuss the feature with us.

- Please see the [contribution guide](CONTRIBUTING.md) for more information.
- By participating in this project, you agree to abide by its [Code of Conduct](CODE_OF_CONDUCT.md).

[![Donate](https://c5.patreon.com/external/logo/become_a_patron_button.png)](https://www.patreon.com/catalyst_team)

## License

This project is licensed under the Apache License, Version 2.0 see the [LICENSE](LICENSE) file for details
[![License](https://img.shields.io/github/license/catalyst-team/catalyst.svg)](LICENSE)

## Citation

Please use this bibtex if you want to cite this repository in your publications:

    @misc{catalyst,
        author = {Kolesnikov, Sergey},
        title = {Reproducible and fast DL & RL.},
        year = {2018},
        publisher = {GitHub},
        journal = {GitHub repository},
        howpublished = {\url{https://github.com/catalyst-team/catalyst}},
    }