How do I set up distributed training in PyTorch?
An example of an AI chatbot conversation about setting up PyTorch for distributed training.
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 key steps involved in the setup?
The key steps include initializing the process group, defining your model, and using DistributedDataParallel to wrap your model.
Can you explain how to initialize the process group?
Sure! You can initialize the process group using torch.distributed.init_process_group(backend='nccl') for GPUs or backend='gloo' for CPUs.
What about data loading for distributed training?
For data loading, use torch.utils.data.distributed.DistributedSampler to ensure each process gets a unique subset of the data.
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