distributed-training-ray-train

Topics in the source

• Ray Train - Distributed Training Orchestration
• Quick start
• Common workflows
• Workflow 1: Scale existing PyTorch code
• Workflow 2: HuggingFace Transformers integration
• Workflow 3: Hyperparameter tuning with Ray Tune
• Workflow 4: Checkpointing and fault tolerance
• Workflow 5: Multi-node training

Ray Train scales machine learning training from single GPU to multi-node clusters with minimal code changes. Installation: Basic PyTorch training (single node): That's it! Ray handles: Distributed coordination GPU allocation Fault tolerance Checkpointing Metric aggregation Original single-GPU code: Ray Train version (scales to multi-GPU/multi-node): Benefits: Same code runs on 1 GPU or 1000 GPUs Result: Distributed hyperparameter search across cluster Launch Ray cluster: Use Ray Train when: Training across multiple machines (multi-node) Need hyperparameter tuning at scale Want fault tolerance (auto-restart failed workers) Elastic scaling (add/remove nodes during training) Unified framework (same code for PyTorch/TF/HF) Key advantages: Multi-node orchestration: Easiest multi-node setup Ray Tune integration: Best-in-class hyperparameter tuning Fault tolerance: Automatic recovery from failures Elastic: Add/remove nodes without restarting Framework agnostic: PyTorch, TensorFlow, HuggingFace, XGBoost Use alternatives instead: Accelerate: Single-node multi-GPU, simpler PyTorch Lightning: High-level abstractions, callbacks DeepSpeed: Maximum performance, complex setup Raw DDP: Maximum con

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