PyTorch Distributed Training Best Practices

Distributed training is a crucial aspect of large-scale machine learning models, especially in PyTorch. This guide outlines the best practices for distributed training in PyTorch.

Key Points

• Data Parallelism: Utilize data parallelism to distribute the data across multiple GPUs.
• Model Parallelism: Implement model parallelism when the model is too large to fit on a single GPU.
• Communication Overheads: Minimize communication overheads by using optimized communication libraries.

Step-by-Step Guide

1. Setup Distributed Backend: Choose a distributed backend like NCCL or MPI.
2. Distributed Data Loading: Use torch.utils.data.distributed.DistributedSampler to ensure data is evenly distributed.
3. Model Initialization: Initialize the model on the first GPU to ensure consistency.
4. Backward Propagation: Use torch.nn.parallel.DistributedDataParallel for efficient backward propagation.

Tips

• Mixed Precision Training: Utilize mixed precision training to speed up training and reduce memory usage.
• Checkpointing: Regularly save checkpoints to prevent data loss.

Resources

For more detailed information, refer to the PyTorch Distributed Documentation.

Distributed Training

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