Metadata-Version: 2.1
Name: catalyst
Version: 19.5
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: MIT
Description: 
        # Catalyst
        [![Build Status](https://travis-ci.com/catalyst-team/catalyst.svg?branch=master)](https://travis-ci.com/catalyst-team/catalyst) 
        [![License](https://img.shields.io/github/license/catalyst-team/catalyst.svg)](LICENSE)
        [![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)
        
        ![Catalyst logo](https://raw.githubusercontent.com/catalyst-team/catalyst-pics/master/pics/catalyst_logo.png)
        
        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!
        
        ---
        
        Catalyst is compatible with: Python 3.6+. PyTorch 0.4.1+.
        
        API documentation and an overview of the library can be found 
        [here](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.
        
        
        ## Installation
        
        ```bash
        pip install 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 – even source code 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,
        actor-critic off-policy continuous actions space 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.experiments 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())
        
        # model runner
        runner = SupervisedRunner()
        
        # model training
        runner.train(
            model=model,
            criterion=criterion,
            optimizer=optimizer,
            loaders=loaders,
            logdir=logdir,
            num_epochs=num_epochs,
            verbose=True
        )
        ```
        
        
        ## 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.
        
        
        ## 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}},
            }
        
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT 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
