awesome-repositories.com
© 2026 Bringes Technology SRL·VAT RO45896025·hello@bringes.io
MCPSitemapPrivacyTerms
Distributed Learning · Awesome GitHub Repositories

2 repos

Awesome GitHub RepositoriesDistributed Learning

Techniques and frameworks for training machine learning models across multiple computing nodes or parallel processing units.

Explore 2 awesome GitHub repositories matching artificial intelligence & ml · Distributed Learning. Refine with filters or upvote what's useful.

  1. Home
  2. Artificial Intelligence & ML
  3. Machine Learning
  4. Infrastructure
  5. Training & Tuning
  6. Distributed and Scaling Strategies
  7. Distributed Learning

Awesome Distributed Learning GitHub Repositories

Describe the repository you're looking for…
We'll search the best matching repositories with AI.
  • tensorflow/tensorflow

    tensorflow/tensorflow

    193,864GitHubView on GitHub↗

    TensorFlow is a comprehensive machine learning framework designed for the construction, training, and deployment of complex mathematical models. It utilizes a graph-based execution model that represents operations as directed acyclic graphs, enabling automatic differentiation and efficient parallel processing. The syst

    Coordinates decentralized training across network nodes to preserve data privacy while aggregating model updates.

    C++deep-learningdeep-neural-networksdistributed
  • pytorch/pytorch

    pytorch/pytorch

    97,601GitHubView on 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 diffe

    Shards model parameters, gradients, and optimizer states across processes to enable memory-efficient distributed training.

    Pythonautograddeep-learninggpu

Explore sub-tags

  • Federated LearningsComputational tasks executed across decentralized data sources to train models locally while maintaining data privacy.
  • Fully Sharded Data ParallelismMemory-efficient training technique that shards model parameters, gradients, and optimizer states across data-parallel processes.