How do I set up distributed training with PyTorch?

How do I set up distributed training with PyTorch?

To set up distributed training in PyTorch, you need to use the torch.distributed package. First, ensure you have multiple GPUs or machines.

What are the steps to initialize the process group?

You can initialize the process group using torch.distributed.init_process_group(backend='nccl') for GPUs or backend='gloo' for CPUs.

How do I modify my training loop for distributed training?

You need to wrap your model with torch.nn.parallel.DistributedDataParallel and ensure that your data loader uses DistributedSampler.

Can I use PyTorch's built-in functions for logging during training?

Yes, you can use libraries like TensorBoard or Weights & Biases for logging metrics during distributed training.