What are the best practices for distributed training in machine learning?

What are the best practices for distributed training in machine learning?

Some best practices include data parallelism, efficient communication, and using appropriate hardware.

Can you explain data parallelism?

Data parallelism involves splitting the dataset across multiple devices, allowing each to process a portion simultaneously.

What about model parallelism?

Model parallelism splits the model itself across devices, which is useful for very large models that don't fit in memory.

How can I optimize communication between nodes?

You can optimize communication by using techniques like gradient compression and asynchronous updates.