Metadata-Version: 2.1
Name: catalyst
Version: 20.3.1
Summary: Catalyst. PyTorch framework for DL & RL research and development.
Home-page: https://github.com/catalyst-team/catalyst
Author: Sergey Kolesnikov
Author-email: scitator@gmail.com
License: Apache License 2.0
Download-URL: https://github.com/catalyst-team/catalyst
Project-URL: Bug Tracker, https://github.com/catalyst-team/catalyst/issues
Project-URL: Documentation, https://catalyst-team.github.io/catalyst
Project-URL: Source Code, https://github.com/catalyst-team/catalyst
Description: 
        <div align="center">
        
        [![Catalyst logo](https://raw.githubusercontent.com/catalyst-team/catalyst-pics/master/pics/catalyst_logo.png)](https://github.com/catalyst-team/catalyst)
        
        **Accelerated DL & RL**
        
        [![Build Status](http://66.248.205.49:8111/app/rest/builds/buildType:id:Catalyst_Deploy/statusIcon.svg)](http://66.248.205.49:8111/project.html?projectId=Catalyst&tab=projectOverview&guest=1)
        [![CodeFactor](https://www.codefactor.io/repository/github/catalyst-team/catalyst/badge)](https://www.codefactor.io/repository/github/catalyst-team/catalyst)
        [![Pipi version](https://img.shields.io/pypi/v/catalyst.svg)](https://pypi.org/project/catalyst/)
        [![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)
        [![PyPI Status](https://pepy.tech/badge/catalyst)](https://pepy.tech/project/catalyst)
        
        [![Twitter](https://img.shields.io/badge/news-on%20twitter-499feb)](https://twitter.com/catalyst_core)
        [![Telegram](https://img.shields.io/badge/channel-on%20telegram-blue)](https://t.me/catalyst_team)
        [![Slack](https://img.shields.io/badge/ODS-slack-red)](https://opendatascience.slack.com/messages/CGK4KQBHD)
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        </div>
        
        PyTorch framework for DL & RL research and development.
        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. <br/>
        Break the cycle - use the Catalyst!
        
        Part of [PyTorch Ecosystem](https://pytorch.org/ecosystem/). Part of [Catalyst Ecosystem](https://docs.google.com/presentation/d/1D-yhVOg6OXzjo9K_-IS5vSHLPIUxp1PEkFGnpRcNCNU/edit?usp=sharing). Project [manifest](https://github.com/catalyst-team/catalyst/blob/master/MANIFEST.md).
        
        ---
        
        ## Installation
        
        Common installation:
        ```bash
        pip install -U catalyst
        ```
        
        <details>
        <summary>Specific versions with additional requirements</summary>
        <p>
        
        ```bash
        pip install catalyst[ml]         # installs DL+ML based catalyst
        pip install catalyst[rl]         # installs DL+RL based catalyst
        pip install catalyst[cv]         # installs DL+CV based catalyst
        pip install catalyst[nlp]        # installs DL+NLP based catalyst
        pip install catalyst[ecosystem]  # installs Catalyst.Ecosystem for DL/RL R&D
        pip install catalyst[contrib]    # installs DL+contrib based catalyst
        pip install catalyst[all]        # installs everything. Very convenient to deploy on a new server
        ```
        </p>
        </details>
        
        Catalyst is compatible with: Python 3.6+. PyTorch 1.0.0+.
        
        
        ## Getting started
        
        ```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).
        
        
        #### Docs and examples
        - [Demo with minimal examples](./examples/notebooks/demo.ipynb) for CV, NLP, RecSys and GANs [![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/demo.ipynb)
        - 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)
        - Binary and semantic [segmentation pipeline](https://github.com/catalyst-team/segmentation)
        
        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). <br/>
        In the **[examples folder](examples)**
        of the repository, you can find advanced tutorials and Catalyst best practices.
        
        ##### Infos
        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.
        
        We also supervise **[Awesome Catalyst list](https://github.com/catalyst-team/awesome-catalyst-list)** – Catalyst-powered projects, tutorials and talks. <br/>
        Feel free to make a PR with your project to the list. And don't forget to check out current list, there are many interesting projects.
        
        ##### Releases
        We deploy a major release once a month with a name like `YY.MM`. <br/>
        And micro-releases with framework improvements during a month in the format `YY.MM.#`.
        
        You can view the changelog on the **[GitHub Releases](https://github.com/catalyst-team/catalyst/releases)** page. <br/>
        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, Ranger optimizer, OneCycle, FP16 and more).
        
        
        #### Structure
        
        - **DL** – runner for training and inference,
           all of the classic ML and CV/NLP metrics
           and a variety of callbacks for training, validation
           and inference of neural networks.
        - **RL** – scalable Reinforcement Learning,
           all popular model-free algorithms implementations and their improvements
           with distributed training support.
        - **contrib** - additional modules contributed by Catalyst users.
        - **data** - useful tools and scripts for data processing.
        
        
        ## Docker [![Docker Pulls](https://img.shields.io/docker/pulls/catalystteam/catalyst)](https://hub.docker.com/r/catalystteam/catalyst/tags)
        Catalyst has its own [DockerHub page](https://hub.docker.com/r/catalystteam/catalyst/tags):
        - `catalystteam/catalyst:{CATALYST_VERSION}` – simple image with Catalyst
        - `catalystteam/catalyst:{CATALYST_VERSION}-fp16` – Catalyst with FP16
        - `catalystteam/catalyst:{CATALYST_VERSION}-dev` – Catalyst for development with all the requirements
        - `catalystteam/catalyst:{CATALYST_VERSION}-dev-fp16` – Catalyst for development with FP16
        
        To build a docker from the sources and get more information and examples,
        please visit [docker folder](docker).
        
        
        ## 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).
        
        ## 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 = {Accelerated DL & RL.},
                year = {2018},
                publisher = {GitHub},
                journal = {GitHub repository},
                howpublished = {\url{https://github.com/catalyst-team/catalyst}},
            }
        
Keywords: Machine Learning,Distributed Computing,Deep Learning,Reinforcement Learning,Computer Vision,Natural Language Processing,Recommendation Systems,Information Retrieval,PyTorch
Platform: UNKNOWN
Classifier: Environment :: Console
Classifier: Natural Language :: English
Classifier: Development Status :: 4 - Beta
Classifier: Operating System :: OS Independent
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Image Recognition
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: Implementation :: CPython
Requires-Python: >=3.6.0
Description-Content-Type: text/markdown
Provides-Extra: contrib
Provides-Extra: cv
Provides-Extra: ecosystem
Provides-Extra: ml
Provides-Extra: nlp
Provides-Extra: rl
Provides-Extra: all
