Hugging Face Accelerate for Multi-GPU Training

Accelerate is a library for running PyTorch training code across different hardware setups. Its goal is to let a script move between CPU, one accelerator, multiple GPUs, or configured multi-node environments without rewriting every device-specific operation. The Accelerator object can prepare models, optimizers, and data loaders, manage backward passes, and coordinate launch behavior depending on the chosen configuration. A common workflow begins by writing and testing an ordinary PyTorch training loop on one device. The script creates its model, optimizer, and data loader, then passes them through the framework's preparation interface. Launch configuration selects the number of processes and hardware strategy. Distributed data parallel training typically uses multiple processes so each device works on different batches and synchronizes gradients. This does not remove distributed-training requirements. Batch sizes may be per process or global depending on configuration. Metrics must be reduced across processes when a global result is intended. Checkpoint writes should avoid races, and only the appropriate process should write shared artifacts. Random seeds and data sampler behavior need deliberate handling. A model that runs without exceptions can still evaluate incorrectly if examples or metrics are duplicated. Mixed precision can reduce memory or improve throughput on compatible hardware, but numerical behavior depends on model and device. Compare validation metrics and watch for overflow, underflow, or unsupported operators. Performance depends on interconnects, data loading, batch size, and synchronization overhead; multiple GPUs do not guarantee linear speedup. Start with a small controlled run and verify device placement, number of processes, effective batch size, metric aggregation, and checkpoint loading. Record launch configuration and software versions. Accelerate reduces infrastructure-specific code, but its configuration and distributed semantics still need to be understood.