awesome-repositories.com
Blog
awesome-repositories.com

Découvrez les meilleurs dépôts open-source grâce à notre recherche par IA.

ExplorerRecherches sélectionnéesAlternatives open sourceLogiciels auto-hébergésBlogPlan du site
ProjetÀ proposNotre méthodologiePresseServeur MCP
Mentions légalesConfidentialitéConditions d'utilisation
© 2026 Bringes Technology SRL·VAT RO45896025·hello@awesome-repositories.com
·
catalyst-team avatar

catalyst-team/catalyst

0
View on GitHub↗
3,376 stars·400 forks·Python·Apache-2.0·2 vuescatalyst-team.com↗

Catalyst

Accelerated deep learning R&D

Features

  • Deep Learning - High-level utilities for deep learning and reinforcement research.
  • Deep Learning Ecosystems - High-level training and development for PyTorch.
  • Deep Learning Frameworks - High-level framework for PyTorch research and development.
  • General Machine Learning - High-level utilities for PyTorch research.
  • Frameworks d'apprentissage automatique - Framework for reproducible deep learning experimentation.
  • Perception and Machine Learning - Framework for reproducible deep learning experimentation.
  • Outils de développement - High-level utils for research.
  • Training and Experimentation - High-level utilities for research and experimentation.
  • Training and Utilities - High-level utilities for research and reproducibility.
  • PyTorch Utilities - Listed in the “PyTorch Utilities” section of the The Incredible Pytorch awesome list.

Historique des stars

Graphique de l'historique des stars pour catalyst-team/catalystGraphique de l'historique des stars pour catalyst-team/catalyst

Recherche par IA

Explorez plus de dépôts awesome

Décrivez vos besoins en langage naturel — l'IA classe des milliers de projets open source sélectionnés par pertinence.

Start searching with AI

Alternatives open source à Catalyst

Projets open source similaires, classés selon le nombre de fonctionnalités partagées avec Catalyst.
  • pytorch/igniteAvatar de pytorch

    pytorch/ignite

    4,770Voir sur GitHub↗

    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

    Python
    Voir sur GitHub↗4,770
  • graal-research/poutyneAvatar de GRAAL-Research

    GRAAL-Research/poutyne

    578Voir sur GitHub↗

    A simplified framework and utilities for PyTorch

    Python
    Voir sur GitHub↗578
  • fastai/fastaiAvatar de fastai

    fastai/fastai

    27,862Voir sur GitHub↗

    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

    Jupyter Notebookcolabdeep-learningfastai
    Voir sur GitHub↗27,862
  • pytorch/pytorchAvatar de pytorch

    pytorch/pytorch

    100,814Voir sur GitHub↗

    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

    Pythonautograddeep-learninggpu
    Voir sur GitHub↗100,814
Voir les 30 alternatives à Catalyst→

Questions fréquentes

Que fait catalyst-team/catalyst ?

Accelerated deep learning R&D

Quelles sont les fonctionnalités principales de catalyst-team/catalyst ?

Les fonctionnalités principales de catalyst-team/catalyst sont : Deep Learning, Deep Learning Ecosystems, Deep Learning Frameworks, General Machine Learning, Frameworks d'apprentissage automatique, Perception and Machine Learning, Outils de développement, Training and Experimentation.

Quelles sont les alternatives open-source à catalyst-team/catalyst ?

Les alternatives open-source à catalyst-team/catalyst incluent : 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.