PyTorch provides powerful tools for distributed training, enabling you to scale models across multiple devices and systems. Below are key components and resources to get started:

📚 Core Concepts

  • Process Group: Manages communication between processes. Learn more
  • Backend: Choose from gloo, nccl, or mpi for different hardware setups.
  • Communicator: Handles collective operations like broadcast and reduce.

🧰 Practical Tools

  • torch.distributed.launch: Simplifies starting distributed training scripts.
    Distributed Training Overview
  • torch.nn.parallel.DistributedDataParallel: Wrap models for distributed training.
    Distributed Data Parallel
  • torch.utils.data.DistributedSampler: Ensures data is evenly distributed across GPUs.
    Distributed Sampler

🌐 Extend Your Knowledge

For in-depth guides and code examples, visit our PyTorch Distributed Documentation.

PyTorch Distributed Training