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Accelerated deep learning R&D
The main features of catalyst-team/catalyst are: Deep Learning, Deep Learning Ecosystems, Deep Learning Frameworks, General Machine Learning, Machine Learning Frameworks, Perception and Machine Learning, Developer Tools, Training and Experimentation.
Projects with overlapping indexed features include: pytorch/ignite — Ignite is a high-level training framework for PyTorch neural networks that serves as a training engine and deep… pytorch/pytorch — PyTorch is a machine learning framework centered on a GPU-ready tensor library that supports multi-dimensional array… fastai/fastai — Fastai is a high-level deep learning library built on PyTorch that provides a unified interface for managing the… graal-research/poutyne — A simplified framework and utilities for PyTorch. maxpumperla/hyperas — Keras + Hyperopt: A very simple wrapper for convenient hyperparameter optimization. pytorch/audio — Data manipulation and transformation for audio signal processing, powered by PyTorch.
Ignite is a high-level training framework for PyTorch neural networks that serves as a training engine and deep learning lifecycle manager. It provides a structured system for organizing and automating training and evaluation loops, managing data iterators and triggering event handlers at specific milestones during the model training process. The project distinguishes itself through a comprehensive suite of tools for distributed training and model evaluation. It includes utilities for synchronizing gradients and coordinating collective communication across multiple GPUs or nodes, as well as a
Fastai is a high-level deep learning library built on PyTorch that provides a unified interface for managing the entire machine learning lifecycle. It functions as a comprehensive training toolkit, abstracting hardware management and automating complex training loops to simplify the construction and execution of neural network models. The framework is distinguished by its notebook-centric development environment and a type-dispatching data pipeline that automatically applies transformations based on input data formats. It emphasizes transfer learning through discriminative layer-wise optimiza
PyTorch is a machine learning framework centered on a GPU-ready tensor library that supports multi-dimensional array operations across both CPU and accelerator hardware. It provides a foundational infrastructure for mathematical computation and dynamic neural network construction, utilizing a tape-based automatic differentiation system that allows for flexible, non-static graph execution. The framework is designed for deep integration with Python, enabling natural usage alongside standard scientific computing ecosystems. It distinguishes itself through a comprehensive distributed training sui
