2 Repos
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.
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Awesome Trainer Coordination GitHub Repositories
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- openmanus/openmanus-rl
OpenManus/OpenManus-RL
3,916Auf GitHub ansehenOpenManus-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 - allenai/open-instruct
allenai/open-instruct
3,586Auf GitHub ansehenOpen-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
