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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.

Break the cycle - use the Catalyst!

Project manifest. Part of PyTorch Ecosystem. Part of Catalyst Ecosystem:

Catalyst at AI Landscape.

Getting started

import os
import torch
from torch.nn import functional as F
from import DataLoader
from torchvision.datasets import MNIST
from torchvision.transforms import ToTensor
from catalyst import dl
from catalyst.utils import metrics

model = torch.nn.Linear(28 * 28, 10)
optimizer = torch.optim.Adam(model.parameters(), lr=0.02)

loaders = {
    "train": DataLoader(MNIST(os.getcwd(), train=True, download=True, transform=ToTensor()), batch_size=32),
    "valid": DataLoader(MNIST(os.getcwd(), train=False, download=True, transform=ToTensor()), batch_size=32),

class CustomRunner(dl.Runner):

    def predict_batch(self, batch):
        # model inference step
        return self.model(batch[0].to(self.device).view(batch[0].size(0), -1))

    def _handle_batch(self, batch):
        # model train/valid step
        x, y = batch
        y_hat = self.model(x.view(x.size(0), -1))

        loss = F.cross_entropy(y_hat, y)
        accuracy01, accuracy03 = metrics.accuracy(y_hat, y, topk=(1, 3))
            {"loss": loss, "accuracy01": accuracy01, "accuracy03": accuracy03}

        if self.state.is_train_loader:

runner = CustomRunner()
# model training
# model inference
for prediction in runner.predict_loader(loader=loaders["valid"]):
    assert prediction.detach().cpu().numpy().shape[-1] == 10
# model tracing
traced_model = runner.trace(loader=loaders["valid"])


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.


Common installation:

pip install -U catalyst

More specific with additional requirements:

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+ --upgrade

Catalyst is compatible with: Python 3.6+. PyTorch 1.0.0+.


  • 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.


  • 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.


All the Catalyst code is tested rigorously with every new PR.

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.


In the examples of the repository, you can find advanced tutorials and Catalyst best practices.


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 for more information.

By participating in this project, you agree to abide by its Code of Conduct.

User feedback

We have created 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!


Please use this bibtex if you want to cite this repository in your publications:

    author = {Kolesnikov, Sergey},
    title = {Accelerated DL R&D},
    year = {2018},
    publisher = {GitHub},
    journal = {GitHub repository},
    howpublished = {\url{}},
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