Metadata-Version: 2.1
Name: catalyst
Version: 20.4
Summary: Catalyst. PyTorch framework for DL 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 R&D**
        
        [![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/)
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        [![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)
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        </div>
        
        PyTorch framework for Deep Learning 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!
        
        Project [manifest](https://github.com/catalyst-team/catalyst/blob/master/MANIFEST.md). Part of [PyTorch Ecosystem](https://pytorch.org/ecosystem/). Part of [Catalyst Ecosystem](https://docs.google.com/presentation/d/1D-yhVOg6OXzjo9K_-IS5vSHLPIUxp1PEkFGnpRcNCNU/edit?usp=sharing):
        - [Alchemy](https://github.com/catalyst-team/alchemy) - Experiments logging & visualization
        - [Catalyst](https://github.com/catalyst-team/catalyst) - Accelerated Deep Learning Research and Development
        - [Reaction](https://github.com/catalyst-team/reaction) - Convenient Deep Learning models serving
        
        [Catalyst at AI Landscape](https://landscape.lfai.foundation/selected=catalyst).
        
        ----
        
        ## Getting started
        
        ```python
        import torch
        from torch.utils.data import DataLoader, TensorDataset
        from catalyst.dl import SupervisedRunner
        
        # experiment setup
        logdir = "./logdir"
        num_epochs = 8
        
        # data
        num_samples, num_features = int(1e4), int(1e1)
        X, y = torch.rand(num_samples, num_features), torch.rand(num_samples)
        dataset = TensorDataset(X, y)
        loader = DataLoader(dataset, batch_size=32, num_workers=1)
        loaders = {"train": loader, "valid": loader}
        
        # model, criterion, optimizer, scheduler
        model = torch.nn.Linear(num_features, 1)
        criterion = torch.nn.MSELoss()
        optimizer = torch.optim.Adam(model.parameters())
        scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, [3, 6])
        
        # model training
        runner = SupervisedRunner()
        runner.train(
            model=model,
            criterion=criterion,
            optimizer=optimizer,
            scheduler=scheduler,
            loaders=loaders,
            logdir=logdir,
            num_epochs=num_epochs,
            verbose=True,
        )
        ```
        
        ### Minimal Examples
        
        <details>
        <summary>ML - Linear Regression is my profession</summary>
        <p>
        
        ```python
        import torch
        from torch.utils.data import DataLoader, TensorDataset
        from catalyst.dl import SupervisedRunner
        
        # experiment setup
        logdir = "./logdir"
        num_epochs = 8
        
        # data
        num_samples, num_features = int(1e4), int(1e1)
        X, y = torch.rand(num_samples, num_features), torch.rand(num_samples)
        dataset = TensorDataset(X, y)
        loader = DataLoader(dataset, batch_size=32, num_workers=1)
        loaders = {"train": loader, "valid": loader}
        
        # model, criterion, optimizer, scheduler
        model = torch.nn.Linear(num_features, 1)
        criterion = torch.nn.MSELoss()
        optimizer = torch.optim.Adam(model.parameters())
        scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, [3, 6])
        
        # model training
        runner = SupervisedRunner()
        runner.train(
            model=model,
            criterion=criterion,
            optimizer=optimizer,
            scheduler=scheduler,
            loaders=loaders,
            logdir=logdir,
            num_epochs=num_epochs,
            verbose=True,
        )
        ```
        </p>
        </details>
        
        <details>
        <summary>CV - MNIST one more time</summary>
        <p>
        
        ```python
        import os
        import torch
        from torch.nn import functional as F
        from torch.utils.data import DataLoader
        from torchvision.datasets import MNIST
        from torchvision import transforms
        from catalyst import dl
        
        model = torch.nn.Linear(28 * 28, 10)
        optimizer = torch.optim.Adam(model.parameters(), lr=0.02)
        
        loaders = {
            "train": DataLoader(MNIST(os.getcwd(), train=False, download=True,transform=transforms.ToTensor()), batch_size=32),
            "valid": DataLoader(MNIST(os.getcwd(), train=False, download=True,transform=transforms.ToTensor()), batch_size=32),
        }
        
        class CustomRunner(dl.Runner):
            def _handle_batch(self, batch):
                x, y = batch
                y_hat = self.model(x.view(x.size(0), -1))
                loss = F.cross_entropy(y_hat, y)
                self.state.batch_metrics["loss"] = loss
                
                if self.state.is_train_loader:
                    loss.backward()
                    self.state.optimizer.step()
                    self.state.optimizer.zero_grad()
        
        runner = CustomRunner()
        runner.train(
            model=model, 
            optimizer=optimizer, 
            loaders=loaders, 
            verbose=True,
        )
        ```
        </p>
        </details>
        
        <details>
        <summary>CV - MNIST classification with AutoEncoder</summary>
        <p>
        
        ```python
        import os
        import torch
        from torch import nn
        from torch.nn import functional as F
        from torch.utils.data import DataLoader
        from torchvision.datasets import MNIST
        from torchvision import transforms
        from catalyst import dl
        
        
        class ClassifyAE(nn.Module):
            def __init__(self, in_features, hid_features, out_features):
                super().__init__()
                self.encoder = nn.Sequential(nn.Linear(in_features, hid_features), nn.Tanh())
                self.decoder = nn.Linear(hid_features, in_features)
                self.clf = nn.Linear(hid_features, out_features)
            
            def forward(self, x):
                z = self.encoder(x)
                y_hat = self.clf(z)
                x_ = self.decoder(z)
                return y_hat, x_
        
        
        model = ClassifyAE(28 * 28, 128, 10)
        optimizer = torch.optim.Adam(model.parameters(), lr=0.02)
        
        loaders = {
            "train": DataLoader(MNIST(os.getcwd(), train=False, download=True,transform=transforms.ToTensor()), batch_size=32),
            "valid": DataLoader(MNIST(os.getcwd(), train=False, download=True,transform=transforms.ToTensor()), batch_size=32),
        }
        
        class CustomRunner(dl.Runner):
            def _handle_batch(self, batch):
                x, y = batch
                x = x.view(x.size(0), -1)
                y_hat, x_ = self.model(x)
                loss_clf = F.cross_entropy(y_hat, y)
                loss_ae = F.mse_loss(x_, x)
                loss = loss_clf + loss_ae
                
                self.state.batch_metrics = {
                    "loss_clf": loss_clf,
                    "loss_ae": loss_ae,
                    "loss": loss
                }
                
                if self.state.is_train_loader:
                    loss.backward()
                    self.state.optimizer.step()
                    self.state.optimizer.zero_grad()
        
        runner = CustomRunner()
        runner.train(
            model=model, 
            optimizer=optimizer, 
            loaders=loaders, 
            verbose=True,
        )
        ```
        </p>
        </details>
        
        <details>
        <summary>GAN - MNIST, flatten version</summary>
        <p>
        
        ```python
        import os
        import torch
        from torch import nn
        from torch.nn import functional as F
        from torch.utils.data import DataLoader
        from torchvision.datasets import MNIST
        from torchvision import transforms
        from catalyst import dl
        
        
        generator = nn.Sequential(nn.Linear(128, 28 * 28), nn.Tanh())
        discriminator = nn.Sequential(nn.Linear(28 * 28, 1), nn.Sigmoid())
        model = nn.ModuleDict({"generator": generator, "discriminator": discriminator})
        
        generator_optimizer = torch.optim.Adam(
            generator.parameters(), lr=0.0001, betas=(0.5, 0.999))
        discriminator_optimizer = torch.optim.Adam(
            discriminator.parameters(), lr=0.0001, betas=(0.5, 0.999))
        optimizer = {
            "generator": generator_optimizer,
            "discriminator": discriminator_optimizer,
        }
        
        loaders = {
            "train": DataLoader(MNIST(os.getcwd(), train=False, download=True,transform=transforms.ToTensor()), batch_size=32),
            "valid": DataLoader(MNIST(os.getcwd(), train=False, download=True,transform=transforms.ToTensor()), batch_size=32),
        }
        
        class CustomRunner(dl.Runner):
            
            def _handle_batch(self, batch):
                state = self.state
                
                images, _ = batch
                images = images.view(images.size(0), -1)
                bs = images.shape[0]
                z = torch.randn(bs, 128).to(self.device)
                generated_images = self.model["generator"](z)
                
                # generator step
                ## predictions & labels
                generated_labels = torch.ones(bs, 1).to(self.device)
                generated_pred = self.model["discriminator"](generated_images)
        
                ## loss
                loss_generator = F.binary_cross_entropy(generated_pred, generated_labels)
                state.batch_metrics["loss_generator"] = loss_generator
        
                # discriminator step
                ## real
                images_labels = torch.ones(bs, 1).to(self.device)
                images_pred = self.model["discriminator"](images)
                real_loss = F.binary_cross_entropy(images_pred, images_labels)
        
                ## fake
                generated_labels_ = torch.zeros(bs, 1).to(self.device)
                generated_pred_ = self.model["discriminator"](generated_images.detach())
                fake_loss = F.binary_cross_entropy(generated_pred_, generated_labels_)
        
                ## loss
                loss_discriminator = (real_loss + fake_loss) / 2.0
                state.batch_metrics["loss_discriminator"] = loss_discriminator
        
        runner = CustomRunner()
        runner.train(
            model=model, 
            optimizer=optimizer,
            loaders=loaders,
            callbacks=[
                dl.OptimizerCallback(
                    optimizer_key="generator", 
                    loss_key="loss_generator"
                ),
                dl.OptimizerCallback(
                    optimizer_key="discriminator", 
                    loss_key="loss_discriminator"
                ),
            ],
            main_metric="loss_generator",
            num_epochs=5,
            logdir="./logs/gan",
            verbose=True,
        )
        ```
        </p>
        </details>
        
        [Demo with minimal examples for ML, CV, NLP, GANs and RecSys](https://colab.research.google.com/github/catalyst-team/catalyst/blob/master/examples/notebooks/demo.ipynb)
        
        For Catalyst.RL introduction, please follow [Catalyst.RL repo](https://github.com/catalyst-team/catalyst-rl).
        
        
        ## Table of Contents
        - [Overview](#overview)
          * [Installation](#installation)
          * [Features](#features)
          * [Structure](#structure)
          * [Tests](#tests)
        - [Catalyst](#catalyst)
          * [Tutorials](#tutorials)
          * [Projects](#projects)
          * [Tools and pipelines](#tools-and-pipelines)
          * [Talks and videos](#talks-and-videos)
        - [Community](#community)
          * [Contribution guide](#contribution-guide)
          * [User feedback](#user-feedback)
          * [Trusted by](#trusted-by)
          * [Supported by](#supported-by)
          * [Citation](#citation)
        
        
        ## Overview
        Catalyst helps you write compact
        but full-featured Deep Learning pipelines in a few lines of code.
        You get a training loop with metrics, early-stopping, model checkpointing
        and other features without the boilerplate.
        
        ### 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[cv]         # installs DL+CV based catalyst
        pip install catalyst[nlp]        # installs DL+NLP based catalyst
        pip install catalyst[ecosystem]  # installs Catalyst.Ecosystem
        pip install catalyst[contrib]    # installs DL+contrib based catalyst
        pip install catalyst[all]        # installs everything
        # and master version installation
        pip install git+https://github.com/catalyst-team/catalyst@master --upgrade
        ```
        </p>
        </details>
        
        Catalyst is compatible with: Python 3.6+. PyTorch 1.0.0+.
        
        ### 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 with easy customization.
        - Training stages support.
        - Deep Learning best practices - SWA, AdamW, Ranger optimizer, OneCycle, and more.
        - Developments best practices - fp16 support, distributed training, slurm.
        
        
        ### Structure
        - **contrib** - additional modules contributed by Catalyst users.
        - **core** - framework core with main abstractions - 
            Experiment, Runner, Callback and State.
        - **data** - useful tools and scripts for data processing.
        - **DL** – runner for training and inference,
           all of the classic ML and CV/NLP/RecSys metrics
           and a variety of callbacks for training, validation
           and inference of neural networks.
        - **utils** - typical utils for Deep Learning research.
        
        
        ### Tests
        All the Catalyst code is [tested rigorously with every new PR](./tests).
        
        In fact, we train a number of different models for various of tasks - 
        image classification, image segmentation, text classification, GAN training.
        During the tests, we compare their convergence metrics in order to verify 
        the correctness of the training procedure and its reproducibility.
        
        Overall, Catalyst guarantees fully tested, correct and reproducible 
        best practices for the automated parts.
        
        ## Catalyst
        
        ### Tutorials
        - [Demo with minimal examples](./examples/notebooks/demo.ipynb) for ML, CV, NLP, GANs and RecSys [![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)
        - [Beyond fashion: Deep Learning with Catalyst (Config API)](https://evilmartians.com/chronicles/beyond-fashion-deep-learning-with-catalyst)
        - [Tutorial from Notebook API to Config API (RU)](https://github.com/Bekovmi/Segmentation_tutorial)
        
        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.
        
        
        ### Projects
        - [Kaggle Quick, Draw! Doodle Recognition Challenge](https://github.com/ngxbac/Kaggle-QuickDraw) - 11th place solution
        - [Catalyst.RL - NeurIPS 2018: AI for Prosthetics Challenge](https://github.com/Scitator/neurips-18-prosthetics-challenge) – 3rd place solution
        - [CamVid Segmentation Example](https://github.com/BloodAxe/Catalyst-CamVid-Segmentation-Example) - Example of semantic segmentation for CamVid dataset
        - [Notebook API tutorial for segmentation in Understanding Clouds from Satellite Images Competition](https://www.kaggle.com/artgor/segmentation-in-pytorch-using-convenient-tools/)
        - [Kaggle Google Landmark 2019](https://github.com/ngxbac/Kaggle-Google-Landmark-2019) - 30th place solution
        - [Hierarchical attention for sentiment classification with visualization](https://github.com/neuromation/ml-recipe-hier-attention)
        - [Pediatric bone age assessment](https://github.com/neuromation/ml-recipe-bone-age)
        - [iMet Collection 2019 - FGVC6](https://github.com/ngxbac/Kaggle-iMet) - 24th place solution
        - [ID R&D Anti-spoofing Challenge](https://github.com/bagxi/idrnd-anti-spoofing-challenge-solution) - 14th place solution
        - [Implementation of paper "Tell Me Where to Look: Guided Attention Inference Network"](https://github.com/ngxbac/GAIN)
        - [Catalyst.RL - NeurIPS 2019: Learn to Move - Walk Around](https://github.com/Scitator/learning-to-move-starter-kit) – starter kit
        - [Catalyst.RL - NeurIPS 2019: Animal-AI Olympics](https://github.com/Scitator/animal-olympics-starter-kit) - starter kit
        - [NeurIPS 2019: Recursion Cellular Image Classification](https://github.com/ngxbac/Kaggle-Recursion-Cellular) - 4th place solution
        - [MICCAI 2019: Automatic Structure Segmentation for Radiotherapy Planning Challenge 2019](https://github.com/ngxbac/StructSeg2019) 
          * 3rd place solution for `Task 3: Organ-at-risk segmentation from chest CT scans`
          * and 4th place solution for `Task 4: Gross Target Volume segmentation of lung cancer`
        - [Kaggle Seversteal steel detection](https://github.com/bamps53/kaggle-severstal) - 5th place solution
        - [Implementation of paper "Filter Response Normalization Layer: Eliminating Batch Dependence in the Training of Deep Neural Networks"](https://github.com/yukkyo/PyTorch-FilterResponseNormalizationLayer)
        - [RSNA Intracranial Hemorrhage Detection](https://github.com/ngxbac/Kaggle-RSNA) - 5th place solution
        - [Implementation of paper "Utterance-level Aggregation For Speaker Recognition In The Wild"](https://github.com/ptJexio/Speaker-Recognition)
        - [APTOS 2019 Blindness Detection](https://github.com/BloodAxe/Kaggle-2019-Blindness-Detection) – 7th place solution
        - [Catalyst.RL - NeurIPS 2019: Learn to Move - Walk Around](https://github.com/Scitator/run-skeleton-run-in-3d) – 2nd place solution
        - [xView2 Damage Assessment Challenge](https://github.com/BloodAxe/xView2-Solution) - 3rd place solution
        - [Inria Segmentation Example](https://github.com/BloodAxe/Catalyst-Inria-Segmentation-Example) - An example of training segmentation model for Inria Sattelite Segmentation Challenge
        - [iglovikov_segmentation](https://github.com/ternaus/iglovikov_segmentation) - Semantic segmentation pipeline using Catalyst
        
        
        ### Tools and pipelines
        - [Catalyst.RL](https://github.com/Scitator/catalyst-rl-framework) – A Distributed Framework for Reproducible RL Research by [Scitator](https://github.com/Scitator)
        - [Catalyst.Classification](https://github.com/catalyst-team/classification) - Comprehensive classification pipeline with Pseudo-Labeling by [Bagxi](https://github.com/bagxi) and [Pdanilov](https://github.com/pdanilov)
        - [Catalyst.Segmentation](https://github.com/catalyst-team/segmentation) - Segmentation pipelines - binary, semantic and instance, by [Bagxi](https://github.com/bagxi)
        - [Catalyst.Detection](https://github.com/catalyst-team/detection) - Anchor-free detection pipeline by [Avi2011class](https://github.com/Avi2011class) and [TezRomacH](https://github.com/TezRomacH)
        - [Catalyst.GAN](https://github.com/catalyst-team/gan) - Reproducible GANs pipelines by [Asmekal](https://github.com/asmekal)
        - [Catalyst.Neuro](https://github.com/catalyst-team/neuro) - Brain image analysis project, in collaboration with [TReNDS Center](https://trendscenter.org)
        - [MLComp](https://github.com/catalyst-team/mlcomp) – distributed DAG framework for machine learning with UI by [Lightforever](https://github.com/lightforever)
        - [Pytorch toolbelt](https://github.com/BloodAxe/pytorch-toolbelt) - PyTorch extensions for fast R&D prototyping and Kaggle farming by [BloodAxe](https://github.com/BloodAxe)
        - [Helper functions](https://github.com/ternaus/iglovikov_helper_functions) - An unstructured set of helper functions by [Ternaus](https://github.com/ternaus)
        
        
        ### Talks and videos
        - [Catalyst-team YouTube channel](https://www.youtube.com/channel/UC39Z1Cwr9n8DVpuXcsyi9FQ)
        - [Catalyst.RL – reproducible RL research framework](https://docs.google.com/presentation/d/1U6VWIwQnQDGtu6a1x61tt3AlxCJ1-A1EYKd8lR9tKos/edit?usp=sharing) at [Stachka](https://nastachku.ru/archive/2019_innopolis/index.php?dispatch=products.view&product_id=3650)
        - [Catalyst.DL – reproducible DL research framework (rus)](https://youtu.be/EfG8iwFNdWg) and [slides (eng)](https://docs.google.com/presentation/d/1TL7N_H31zDFShVbKzLfMC3DYw4e1psj6ScDN8spKQlk/edit?usp=sharing) at [RIF](http://rifvrn.ru/program/catalyst-dl-fast-reproducible-dl-4-html)
        - [Catalyst.DL – reproducible DL research framework (rus)](https://youtu.be/7xyMP_5eA8c?t=8964) and [slides (eng)](https://docs.google.com/presentation/d/1XGubfTWvpiJrMyKNx2G6GtAq68y2__sDmx30eSdSRZs/edit?usp=sharing) at [AI-Journey](https://ai-journey.ru/conference-moscow/broadcast?page=2&per-page=12)
        - [Catalyst.DL – fast & reproducible DL](https://docs.google.com/presentation/d/1fbF4PMl092kIdjJTw3olR3wI2cl_P2ttN3c9-WTh1gA/edit?usp=sharing) at [Datastart](https://datastart.ru/msk-autumn-2019/)
        - [Catalyst.RL - NeurIPS 2019: Learn to Move - Walk Around](https://www.youtube.com/watch?v=PprDcJHrFdg&feature=youtu.be&t=4020) and [slides (eng)](https://docs.google.com/presentation/d/1g4g_Rxp9M3xAHwpp_hNzC87L9Gvum3H09g2DIQn1Taw/edit?usp=sharing) at RL reading group Meetup
        - [Catalyst – accelerated DL & RL (rus)](https://youtu.be/Rmo2rx5V3v8?t=77) and [slides (eng)](https://docs.google.com/presentation/d/1xMZMjSwJfM5mZMK7pHp6hVI0FxPyZOpRtBZ0J2l1AaY/edit?fbclid=IwAR1q4XJVqYdD-a5oO2n68Y4xHvChIeOSjCSmlUYqrjIzneYpehzF8PiNdMc#slide=id.g75815b5293_0_202) at [Facebook Developer Circle: Moscow | ML & AI Meetup](https://www.facebook.com/groups/475428499888062/)
        - [Catalyst.RL - Learn to Move - Walk Around 2nd place solution](https://docs.google.com/presentation/d/14UzYAURBulLjuCbQRnNeROhZ74h51-o460DPTkKMrwo/edit?usp=sharing) at NeurIPS competition track
        - [Open Source ML 2019 edition](https://docs.google.com/presentation/d/1A-kwek7USA-j2Nn4n8PmLUQ1PdeUzkkViwXST7RyL-w/edit?usp=sharing) at [Datafest.elka](https://datafest.ru/elka/)
        
        
        ## Community
        
        ### 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).
        
        
        ### User feedback
        
        We have created `catalyst.team.core@gmail.com` for "user feedback".
        - If you like the project and want to say thanks, this the right place.
        - If you would like to start a collaboration between your team and Catalyst team to do better Deep Learning R&D - you are always welcome.
        - If you just don't like Github issues and this ways suits you better - feel free to email us.
        - Finally, if you do not like something, please, share it with us and we can see how to improve it.
        
        We appreciate any type of feedback. Thank you!
        
        
        ### Trusted by
        - [Awecom](https://www.awecom.com)
        - Researchers@[Center for Translational Research in Neuroimaging and Data Science (TReNDS)](https://trendscenter.org)
        - Researchers@[Emory University](https://www.emory.edu)
        - [Evil Martians](https://evilmartians.com)
        - Researchers@[Georgia Institute of Technology](https://www.gatech.edu)
        - Researchers@[Georgia State University](https://www.gsu.edu)
        - [Helios](http://helios.to)
        - [HPCD Lab](https://www.hpcdlab.com)
        - [iFarm](https://ifarmproject.com)
        - [Kinoplan](http://kinoplan.io/)
        - Researchers@[Moscow Institute of Physics and Technology](https://mipt.ru/english/)
        - [Neuromation](https://neuromation.io)
        - [Poteha Labs](https://potehalabs.com/en/)
        - [Provectus](https://provectus.com)
        - Researchers@[Skolkovo Institute of Science and Technology](https://www.skoltech.ru/en)
        - [SoftConstruct](https://www.softconstruct.io/)
        - Researchers@[Tinkoff](https://www.tinkoff.ru/eng/)
        - Researchers@[Yandex.Research](https://research.yandex.com)
        
        
        ### Supported by
        - [HostKey](https://www.hostkey.com)
        - [Moscow Institute of Physics and Technology](https://mipt.ru/english/)
        
        
        ### Citation
        
        Please use this bibtex if you want to cite this repository in your publications:
        
            @misc{catalyst,
                author = {Kolesnikov, Sergey},
                title = {Accelerated DL R&D},
                year = {2018},
                publisher = {GitHub},
                journal = {GitHub repository},
                howpublished = {\url{https://github.com/catalyst-team/catalyst}},
            }
        
Keywords: Machine Learning,Distributed Computing,Deep 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: all
