Catalyst is a PyTorch framework designed to accelerate deep learning research and development by providing a robust, reproducible, and reusable codebase. It aims to eliminate boilerplate code for training loops, metrics, and other common DL tasks, allowing researchers and engineers to focus on innovation.

How It Works

Catalyst employs a runner-based architecture that abstracts away the complexities of the training loop. Users define their models, optimizers, and data loaders, then configure the runner with callbacks for metrics, logging, and checkpointing. This modular design promotes code reuse and simplifies experimentation, enabling rapid iteration on model architectures and training strategies.

Quick Start & Requirements

• Install via pip: pip install -U catalyst
• Specialized installs: pip install catalyst[ml], pip install catalyst[cv], pip install catalyst[optuna]
• Requirements: Python 3.7+, PyTorch 1.4+. Compatible with Linux, macOS, and Windows.
• Documentation: https://catalyst-team.github.io/catalyst/

Highlighted Details

• Supports a wide range of ML tasks including classification, segmentation, GANs, and RL.
• Offers a flexible Config API for defining experiments via YAML files.
• Integrates with Optuna for hyperparameter optimization and pruning.
• Provides utilities for model post-processing like ONNX export and quantization.

Maintenance & Community

• Active development with a significant number of contributors.
• Community channels available via Slack and Telegram.
• Regular releases and extensive documentation.
• Twitter
• Slack

Licensing & Compatibility

• MIT License. Permissive for commercial use and integration with closed-source projects.

Limitations & Caveats

• While extensive, the framework has a learning curve due to its comprehensive nature and abstraction layers. Some advanced features require installing optional dependencies (e.g., catalyst[ml]).