PyTorch provides powerful tools for distributed training, enabling you to scale machine learning models across multiple GPUs or machines. This tutorial covers the essentials of setting up distributed training using PyTorch's torch.distributed package.

Key Concepts 🔍

  • Distributed Data Parallelism (DDP): A method where data is split across devices, and each device computes gradients independently.
  • Multi-GPU Setup: Utilize torch.nn.parallel.DistributedDataParallel for parallel processing.
  • Communication Backend: PyTorch supports backends like NCCL (for NVIDIA GPUs) and Gloo (for CPU-only setups).
  • Process Groups: Define groups of processes to coordinate training steps.

Getting Started 🧰

  1. Initialize Process Group
    Use torch.distributed.init_process_group to set up the backend and world size.
    Example:

    import torch.distributed as dist
dist.init_process_group(backend='nccl', init_method='env://', world_size=2, rank=0)

  2. Wrap Model with DDP
    Distribute your model across devices:

    model = torch.nn.parallel.DistributedDataParallel(model)

  3. Data Loading
    Ensure each process loads only its subset of data using DistributedSampler.

Best Practices ✅

  • Use torch.cuda.set_device to assign GPUs to each process.
  • Implement proper synchronization with torch.distributed.barrier.
  • Monitor training with tools like TensorBoard for multi-device setups.
PyTorch Distributed Training

For deeper insights into advanced features, check out our PyTorch Advanced Features Guide. 📚