How do I set up distributed training in PyTorch?

How do I set up distributed training in PyTorch?

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

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. Make sure to specify the correct rank and world size.

How do I modify my training loop for distributed training?

In your training loop, you should use DistributedDataParallel to wrap your model. This ensures gradients are synchronized across processes.

Can you give me an example of a complete code snippet?

Sure! Here's a simple example:

import torch
import torch.distributed as dist
from torch.nn.parallel import DistributedDataParallel as DDP

# Initialize process group
dist.init_process_group(backend='nccl')

# Create model and wrap it
model = MyModel().to(device)
model = DDP(model)

# Training loop
for data in dataloader:
    outputs = model(data)
    loss = criterion(outputs, targets)
    loss.backward()
    optimizer.step()