Distributed Training Basics

Distributed training is a technique used to train machine learning models on large datasets that may not fit into a single machine's memory. This approach allows for parallel processing and can significantly speed up the training process. Here are some key points about distributed training:

Key Concepts

Parameter Server: A central server that holds the model parameters and sends updates to the workers.
All-reduce: A method to aggregate gradients from all workers in a synchronous training setting.
Parameter Server All-reduce: Combines the parameter server and all-reduce methods.
Asynchronous Training: Workers train independently and periodically update the global model.

Advantages

Scalability: Can handle large datasets and complex models.
Speed: Parallel processing can speed up the training process.
Robustness: Reduces the risk of overfitting due to more data being used.

Challenges

Communication Overhead: Increased communication between workers and the parameter server can slow down the training process.
Synchronization: Synchronous training requires all workers to be synchronized, which can be challenging.
Fault Tolerance: Ensuring that the system remains robust in the face of failures is a challenge.

How to Get Started

If you're interested in learning more about distributed training, we recommend checking out our Distributed Training Tutorial. This tutorial provides a step-by-step guide to setting up a distributed training environment.