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2 dépôts

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

Trouvez les meilleurs dépôts grâce à l'IA.Nous recherchons les dépôts les plus pertinents grâce à l'IA.
  • openmanus/openmanus-rlAvatar de OpenManus

    OpenManus/OpenManus-RL

    3,916Voir sur GitHub↗

    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
    Voir sur GitHub↗3,916
  • allenai/open-instructAvatar de allenai

    allenai/open-instruct

    3,586Voir sur GitHub↗

    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
    Voir sur GitHub↗3,586
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