Distributed PyTorch is a powerful tool for training large models across multiple GPUs or across multiple machines. It allows for efficient parallelization of computations and can significantly speed up the training process.

Understanding Distributed Training

  • What is Distributed Training? Distributed training is the process of training a model on multiple machines or multiple GPUs to speed up the training process and handle larger datasets.

  • Why Use Distributed Training?

    • Scalability: Handle larger datasets and models that cannot fit on a single GPU.
    • Speed: Parallelize computations across multiple GPUs or machines to speed up training.
    • Resource Utilization: Make efficient use of available hardware resources.

Getting Started

Before diving into the tutorials, make sure you have the following prerequisites:

  • PyTorch installed.
  • Basic understanding of PyTorch and deep learning.

Tutorials

1. Basic Concepts

  • Distributed Data Parallel (DDP)

    • DDP is a simple way to parallelize your model training across multiple GPUs.
    • Learn how to set up and use DDP in your PyTorch code.
    • Learn more about DDP

  • Parameter Server (PS)

    • PS is another method for distributed training, where parameters are updated by a central server.
    • Explore the setup and implementation of PS in PyTorch.
    • Read about PS

2. Advanced Topics

  • Fault Tolerance

  • Mixed Precision Training

    • Mixed precision training uses both 32-bit and 16-bit floating-point types to speed up training and reduce memory usage.
    • Explore mixed precision

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By following these tutorials, you will gain a solid understanding of distributed training with PyTorch and be able to apply it to your own projects. Happy training!