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Distributed Training Frameworks · Awesome GitHub Repositories

5 repos

Awesome GitHub RepositoriesDistributed Training Frameworks

Systems and primitives specifically designed for scaling machine learning model training across multi-node or multi-accelerator clusters.

Explore 5 awesome GitHub repositories matching networking & communication · Distributed Training Frameworks. Refine with filters or upvote what's useful.

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  3. Distributed Systems and Peer-to-Peer
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  5. Distributed Training Frameworks

Awesome Distributed Training Frameworks GitHub Repositories

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  • 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

    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

    Pythonautograddeep-learninggpu
  • d2l-ai/d2l-zh

    d2l-ai/d2l-zh

    75,708GitHubView on GitHub↗

    This project is an open-source, interactive educational platform designed to teach deep learning through a comprehensive, code-first curriculum. It provides a structured learning path that covers foundational mathematics, modern neural network architectures, and practical optimization techniques, enabling practitioners

    Pythonbookchinesecomputer-vision
  • keras-team/keras

    keras-team/keras

    63,858GitHubView on GitHub↗

    Keras is a high-level deep learning framework designed for constructing and training neural networks through the composition of modular, functional layers. It serves as a comprehensive modeling toolkit that provides standardized procedures for defining, evaluating, and deploying complex architectures. By utilizing a di

    Pythondata-sciencedeep-learningjax
  • karpathy/nanoGPT

    karpathy/nanoGPT

    53,461GitHubView on GitHub↗

    nanoGPT is a lightweight engine for training and fine-tuning transformer-based language models from scratch. It provides a minimalist codebase designed for educational exploration and rapid experimentation with neural network architectures, utilizing self-attention and feed-forward layers to process sequences and predi

    Python

Explore sub-tags

  • Data-Parallel TrainingFrameworks that distribute model training workloads across multiple hardware units by synchronizing gradients and parameters.
  • Distributed TrainingTools for configuring data and model parallelism to train large neural networks across multiple devices.
  • Distributed Training ConfigurationsSettings and frameworks that enable scaling model training workloads across multiple hardware and compute nodes.
Distributed Training Orchestrators
Systems that distribute large-scale model training by separating model definitions from execution across multiple devices.
  • Distributed Training PrimitivesSoftware suites providing fundamental building blocks for implementing data-parallel and model-parallel training.