Metadata-Version: 2.1
Name: catalyst
Version: 19.9
Summary: Catalyst. High-level utils for PyTorch DL & RL research.
Home-page: https://github.com/catalyst-team/catalyst
Author: Sergey Kolesnikov
Author-email: scitator@gmail.com
License: Apache License 2.0
Description: 
        <div align="center">
        
        ![Catalyst logo](https://raw.githubusercontent.com/catalyst-team/catalyst-pics/master/pics/catalyst_logo.png)
        
        **Reproducible and fast DL & RL**
         
        [![Pipi version](https://img.shields.io/pypi/v/catalyst.svg)](https://pypi.org/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 then 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)
        - 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.
        
        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.
        
        ## 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, 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.
        
        [![Donate](https://c5.patreon.com/external/logo/become_a_patron_button.png)](https://www.patreon.com/catalyst_team)
        
        ## 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}},
            }
        
Keywords: Machine Learning,Distributed Computing,Deep Learning,Reinforcement Learning,Computer Vision,PyTorch
Platform: UNKNOWN
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: Implementation :: CPython
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3.6.0
Description-Content-Type: text/markdown
Provides-Extra: dl
Provides-Extra: drl
Provides-Extra: contrib
Provides-Extra: all
Provides-Extra: rl
