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Awesome GitHub RepositoriesTrainer Coordination

Management of environment initialization and worker scaling for distributed ML training.

Distinct from Distributed ML Trainers: Focuses on the orchestration of training workers rather than the general distribution of the model.

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

Awesome Trainer Coordination GitHub Repositories

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  • openmanus/openmanus-rlOpenManus 的头像

    OpenManus/OpenManus-RL

    3,916在 GitHub 上查看↗

    OpenManus-RL is a reinforcement learning framework and distributed training pipeline designed to train large language models as agents. It serves as an agentic reasoning optimizer and reward model trainer, providing the infrastructure to improve model decision-making through reward-based policy optimization. The project distinguishes itself through a distributed architecture that supports parameter sharding across multiple compute nodes and a coordinated rollout system for collecting interaction trajectories. It incorporates advanced reasoning strategies, such as Tree-of-Thoughts and Monte Ca

    Coordinates environment initialization, worker group scaling, and policy updates across multiple compute nodes.

    Python
    在 GitHub 上查看↗3,916
  • allenai/open-instructallenai 的头像

    allenai/open-instruct

    3,586在 GitHub 上查看↗

    Open-Instruct is a distributed training and instruction tuning framework for large language models. It functions as a coordinator for supervised fine-tuning, reinforcement learning from human feedback pipelines, and tool-use training, providing specialized roles for dataset curation and model alignment. The project distinguishes itself through a high-performance training architecture that utilizes actor-based distributed coordination and hybrid sharding to manage large GPU clusters. It implements advanced alignment techniques including direct preference optimization, group relative policy opt

    Provides a coordinator for managing environment initialization and worker scaling across large GPU clusters during distributed training.

    Python
    在 GitHub 上查看↗3,586
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