PyTorch Distributed provides tools and APIs to enable distributed training on multiple GPUs and across multiple machines. This document will guide you through the basics of using distributed training with PyTorch.
Getting Started
To begin using distributed training, you need to set up your environment correctly. This involves installing the necessary packages and configuring your hardware.
Basic Concepts
Understanding the basic concepts of distributed training is crucial before diving into the implementation details. Here are some key concepts:
- Distributed Data Parallel (DDP): A library that simplifies the process of distributed training by handling all the heavy lifting for you.
- Ring All-reduce: A communication primitive used by DDP for efficient data aggregation across multiple GPUs.
- Environment Variables: Used to configure various aspects of the distributed training process.
Quick Start Guide
Here's a quick guide to get you started with distributed training:
- Setup: Install the necessary packages and configure your environment.
- Define your model: Create a PyTorch model.
- Configure DDP: Wrap your model and optimizer with DDP.
- Train your model: Run your training loop with the wrapped model and optimizer.
For a detailed step-by-step guide, check out the Quick Start Guide.
Advanced Topics
Once you're comfortable with the basics, you can explore more advanced topics:
- Multi-node Training: How to extend your training across multiple machines.
- Mixed Precision Training: Using mixed precision to speed up training and reduce memory usage.
- Custom Collective Operations: Implementing custom collective operations for more complex scenarios.
For more information, refer to the Advanced Topics section.
Conclusion
Distributed training with PyTorch can significantly speed up your training process and enable you to scale to larger models and datasets. We hope this documentation has provided you with a solid foundation to get started.
For further reading, check out the PyTorch Distributed GitHub repository.
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