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Machine Learning Training Frameworks · Awesome GitHub Repositories

4 repos

Awesome GitHub RepositoriesMachine Learning Training Frameworks

Infrastructure and methodologies specifically for the training, checkpointing, and distributed execution of machine learning models.

Explore 4 awesome GitHub repositories matching artificial intelligence & ml · Machine Learning Training Frameworks. Refine with filters or upvote what's useful.

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  • openclaw/openclaw

    openclaw/openclaw

    211,971GitHubView on GitHub↗

    Openclaw is a platform for managing agent execution environments, providing the infrastructure to control agent lifecycles, session state, and workspace persistence. It features a centralized gateway that handles model loops, tool invocation, and streaming events, while supporting multi-agent routing and persistent mem

    TypeScriptaiassistantcrustacean
  • 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
  • huggingface/transformers

    huggingface/transformers

    156,730GitHubView on GitHub↗

    Transformers is a comprehensive library for machine learning that provides a unified interface for training, fine-tuning, and deploying transformer-based models. It supports a wide range of tasks, including text classification, language modeling, question answering, and sequence-to-sequence translation, while offering

    Pythonaudiodeep-learningdeepseek
  • 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

Explore sub-tags

  • Distributed Learning2 sub-tagsTechniques and frameworks for training machine learning models across multiple computing nodes or parallel processing units.
  • Training Checkpointing1 sub-tagMechanisms for saving the state of a training process and resuming it after interruptions or failures.
  • Training Evaluation1 sub-tagMethods and tools for assessing the performance and accuracy of machine learning models during the training phase.
Training Frameworks1 sub-tag
Comprehensive software libraries that provide the infrastructure and APIs necessary to train machine learning models.
  • Training Integrations1 sub-tagTools and plugins that connect training workflows to external infrastructure, monitoring systems, or distributed computing clusters.
  • Training Utilities1 sub-tagSupport software that assists in the training process, including tools for tracking metrics, logging progress, and debugging experiments.