Overview
Catalyst is a PyTorch framework designed to accelerate Deep Learning R&D. It emphasizes reproducibility, rapid experimentation, and codebase reuse, enabling researchers and developers to create innovative models without rewriting training loops. The framework offers features like training loop management, metrics tracking, early stopping, and model checkpointing. Catalyst supports various deep learning tasks, including image classification, segmentation, text classification, and GANs. It facilitates model tracing, quantization, pruning, and ONNX export. Catalyst integrates with popular logging tools like TensorBoard, MLflow, Neptune, and Wandb, providing comprehensive experiment tracking and management capabilities. It supports distributed training via DataParallel and DistributedDataParallel engines.