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        "author": "Sergey Kolesnikov",
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            "License :: OSI Approved :: Apache Software License",
            "Programming Language :: Python",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7",
            "Programming Language :: Python :: Implementation :: CPython",
            "Topic :: Scientific/Engineering :: Artificial Intelligence"
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        "description": "\n<div align=\"center\">\n\n![Catalyst logo](https://raw.githubusercontent.com/catalyst-team/catalyst-pics/master/pics/catalyst_logo.png)\n\n**Reproducible and fast DL & RL**\n\n[![Pipi version](https://img.shields.io/pypi/v/catalyst.svg)](https://pypi.org/project/catalyst/)\n[![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)\n[![PyPI Status](https://pepy.tech/badge/catalyst)](https://pepy.tech/project/catalyst)\n[![Github contributors](https://img.shields.io/github/contributors/catalyst-team/catalyst.svg?logo=github&logoColor=white)](https://github.com/catalyst-team/catalyst/graphs/contributors)\n[![License](https://img.shields.io/github/license/catalyst-team/catalyst.svg)](LICENSE)\n\n[![Build Status](https://travis-ci.com/catalyst-team/catalyst.svg?branch=master)](https://travis-ci.com/catalyst-team/catalyst)\n[![Telegram](./pics/telegram.svg)](https://t.me/catalyst_team)\n[![Gitter](https://badges.gitter.im/catalyst-team/community.svg)](https://gitter.im/catalyst-team/community?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge)\n[![Slack](./pics/slack.svg)](https://opendatascience.slack.com/messages/CGK4KQBHD)\n[![Donate](https://raw.githubusercontent.com/catalyst-team/catalyst-pics/master/third_party_pics/patreon.png)](https://www.patreon.com/catalyst_team)\n\n\n</div>\n\nHigh-level utils for PyTorch DL & RL research.\nIt was developed with a focus on reproducibility, \nfast experimentation and code/ideas reusing.\nBeing able to research/develop something new, \nrather than write another regular train loop.\n\nBreak the cycle - use the Catalyst!\n\n---\n\n#### Installation\n\nCommon installation:\n```bash\npip install -U catalyst\n```\n\nMore specific with additional requirements:\n```bash\npip install catalyst[dl] # installs DL based catalyst with Weights & Biases support\npip install catalyst[rl] # installs DL+RL based catalyst\npip install catalyst[drl] # installs DL+RL based catalyst with Weights & Biases support\npip install catalyst[contrib] # installs DL+contrib based catalyst\npip install catalyst[all] # installs everything. Very convenient to deploy on a new server\n```\n\nCatalyst is compatible with: Python 3.6+. PyTorch 0.4.1+.\n\n#### Docs and examples\n- 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)\n- 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)\n- Comprehensive [classification pipeline](https://github.com/catalyst-team/classification).\n\nAPI documentation and an overview of the library can be found here\n[![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).\n\nIn the **[examples folder](examples)** \nof the repository, you can find advanced tutorials and Catalyst best practices.\n\n##### Blog\nTo 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.\n\n##### Releases\nWe release a major release once a month with a name like `YY.MM`.\nAnd micro-releases with hotfixes and framework improvements in the format `YY.MM.#`.\n\nYou can view the changelog on the [GitHub Releases](https://github.com/catalyst-team/catalyst/releases) page.\n\nCurrent version: [![Pipi version](https://img.shields.io/pypi/v/catalyst.svg)](https://pypi.org/project/catalyst/)\n\n## Overview\n\nCatalyst helps you write compact\nbut full-featured DL & RL pipelines in a few lines of code.\nYou get a training loop with metrics, early-stopping, model checkpointing\nand other features without the boilerplate.\n\n#### Features\n\n- Universal train/inference loop.\n- Configuration files for model/data hyperparameters.\n- Reproducibility \u2013 all source code and environment variables will be saved.\n- Callbacks \u2013 reusable train/inference pipeline parts.\n- Training stages support.\n- Easy customization.\n- PyTorch best practices (SWA, AdamW, 1Cycle, Ranger optimizer, FP16 and more).\n\n\n#### Structure\n\n-  **DL** \u2013 runner for training and inference,\n   all of the classic machine learning and computer vision metrics\n   and a variety of callbacks for training, validation\n   and inference of neural networks.\n-  **RL** \u2013 scalable Reinforcement Learning,\n   on-policy & off-policy algorithms and their improvements\n   with distributed training support.\n-  **contrib** - additional modules contributed by Catalyst users.\n-  **data** - useful tools and scripts for data processing.\n\n\n## Getting started: 30 seconds with Catalyst\n\n```python\nimport torch\nfrom catalyst.dl import SupervisedRunner\n\n# experiment setup\nlogdir = \"./logdir\"\nnum_epochs = 42\n\n# data\nloaders = {\"train\": ..., \"valid\": ...}\n\n# model, criterion, optimizer\nmodel = Net()\ncriterion = torch.nn.CrossEntropyLoss()\noptimizer = torch.optim.Adam(model.parameters())\nscheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer)\n\n# model runner\nrunner = SupervisedRunner()\n\n# model training\nrunner.train(\n    model=model,\n    criterion=criterion,\n    optimizer=optimizer,\n    scheduler=scheduler,\n    loaders=loaders,\n    logdir=logdir,\n    num_epochs=num_epochs,\n    verbose=True\n)\n```\n\nFor Catalyst.RL introduction, please follow [OpenAI Gym example](https://github.com/catalyst-team/catalyst/tree/master/examples/rl_gym).\n\n\n## Docker\n\nPlease see the [docker folder](docker) \nfor more information and examples.\n\n\n## Contribution guide\n\nWe appreciate all contributions. \nIf you are planning to contribute back bug-fixes, \nplease do so without any further discussion. \nIf you plan to contribute new features, utility functions or extensions, \nplease first open an issue and discuss the feature with us.\n\n- Please see the [contribution guide](CONTRIBUTING.md) for more information.\n- By participating in this project, you agree to abide by its [Code of Conduct](CODE_OF_CONDUCT.md).\n\n[![Donate](https://c5.patreon.com/external/logo/become_a_patron_button.png)](https://www.patreon.com/catalyst_team)\n\n## License\n\nThis project is licensed under the Apache License, Version 2.0 see the [LICENSE](LICENSE) file for details\n[![License](https://img.shields.io/github/license/catalyst-team/catalyst.svg)](LICENSE)\n\n## Citation\n\nPlease use this bibtex if you want to cite this repository in your publications:\n\n    @misc{catalyst,\n        author = {Kolesnikov, Sergey},\n        title = {Reproducible and fast DL & RL.},\n        year = {2018},\n        publisher = {GitHub},\n        journal = {GitHub repository},\n        howpublished = {\\url{https://github.com/catalyst-team/catalyst}},\n    }\n\n\n",
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