distributed-training-accelerate

Topics in the source

• HuggingFace Accelerate - Unified Distributed Training
• Quick start
• Common workflows
• Workflow 1: From single GPU to multi-GPU
• Workflow 2: Mixed precision training
• Workflow 3: DeepSpeed ZeRO integration
• Workflow 4: FSDP (Fully Sharded Data Parallel)
• Workflow 5: Gradient accumulation

Accelerate simplifies distributed training to 4 lines of code. Installation: Convert PyTorch script (4 lines): Run (single command): Original script: With Accelerate (4 lines added): Configure (interactive): Questions: Which machine? (single/multi GPU/TPU/CPU) How many machines? (1) Mixed precision? (no/fp16/bf16/fp8) DeepSpeed? (no/yes) Launch (works on any setup): Enable FP16/BF16: Enable DeepSpeed ZeRO-2: Or via config: deepspeedconfig.json: Launch: Enable FSDP: Or via config: Accumulate gradients: Effective batch size: batchsize numgpus gradientaccumulationsteps Use Accelerate when: Want simplest distributed training Need single script for any hardware Use HuggingFace ecosystem Want flexibility (DDP/DeepSpeed/FSDP/Megatron) Need quick prototyping Key advantages: 4 lines: Minimal code changes Unified API: Same code for DDP, DeepSpeed, FSDP, Megatron Automatic: Device placement, mixed precision, sharding Interactive config: No manual launcher setup Single launch: Works everywhere Use alternatives instead: PyTorch Lightning: Need callbacks, high-level abstractions Ray Train: Multi-node orchestration, hyperparameter tuning DeepSpeed: Direct API control, advanced features Raw DDP: M

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