Distributed training is a crucial technique in machine learning, especially for large-scale models. It allows you to train models on multiple machines, which can significantly speed up the training process and reduce the computational resources required.

Key Concepts

  • Parameter Server: A central server that holds the model parameters and sends updates to the workers.
  • All-reduce: A method for aggregating gradients across all workers.
  • Parameter Server with All-reduce: A common architecture that combines the parameter server and all-reduce methods.

Step-by-Step Guide

  1. Setup: Install the necessary libraries and configure your environment.
  2. Model Definition: Define your model architecture.
  3. Optimizer: Choose an optimizer that supports distributed training.
  4. Distributed Strategy: Implement a distributed strategy for training your model.
  5. Training: Start the training process and monitor the progress.

Useful Resources

For more detailed information and tutorials, check out the following resources:

Distributed Training Architecture