Ray Train
技能 已验证 活跃Distributed training orchestration across clusters. Scales PyTorch/TensorFlow/HuggingFace from laptop to 1000s of nodes. Built-in hyperparameter tuning with Ray Tune, fault tolerance, elastic scaling. Use when training massive models across multiple machines or running distributed hyperparameter sweeps.
To enable users to efficiently scale their machine learning training workloads from single machines to thousands of nodes, facilitating large-scale model training and hyperparameter sweeps.
功能
- Distributed training orchestration
- Scales PyTorch, TensorFlow, HuggingFace
- Hyperparameter tuning with Ray Tune
- Fault tolerance and elastic scaling
- Multi-node cluster setup and management
使用场景
- Training massive machine learning models across multiple machines.
- Running distributed hyperparameter optimization sweeps.
- Scaling existing single-node training code to multi-GPU or multi-node environments with minimal changes.
- Setting up and managing Ray clusters for distributed training on local, cloud, or Kubernetes environments.
非目标
- Providing a full ML framework (relies on PyTorch, TensorFlow, etc.)
- Managing individual node hardware or low-level OS configuration
- Replacing simpler single-GPU training solutions unless scaling is required
安装
请先添加 Marketplace
/plugin marketplace add Orchestra-Research/AI-Research-SKILLs/plugin install AI-Research-SKILLs@ai-research-skills质量评分
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