What are the best practices for distributed training in PyTorch?

What are the best practices for distributed training in PyTorch?

Some best practices include using the DistributedDataParallel module, ensuring proper data sharding, and optimizing communication between nodes.

How can I efficiently manage data loading in a distributed setup?

You can use torch.utils.data.DistributedSampler to ensure that each process gets a unique subset of the data.

What should I consider when choosing the number of processes?

Consider the number of GPUs available, the size of your dataset, and the model complexity. A common approach is to use one process per GPU.

Are there any specific libraries that can help with distributed training?

Yes, libraries like Horovod and PyTorch Lightning can simplify the setup and management of distributed training.

Can you explain how to monitor the training process in a distributed environment?

You can use tools like TensorBoard or Weights & Biases to visualize metrics across different processes and nodes.