What are the best optimizers in PyTorch for distributed training?

What are the best optimizers in PyTorch for distributed training?

Some of the best optimizers for distributed training in PyTorch include Adam, SGD, and RMSprop. Each has its own advantages depending on the specific use case.

Can you explain why Adam is preferred in many cases?

Adam is often preferred because it combines the benefits of two other extensions of stochastic gradient descent. It adapts the learning rate for each parameter, which can lead to faster convergence.

How does distributed training affect the choice of optimizer?

Distributed training can introduce challenges like synchronization and communication overhead, which can influence the choice of optimizer. For instance, optimizers that require less communication, like SGD, might be more efficient in a distributed setting.

Are there any specific settings I should consider for these optimizers?

Yes, when using optimizers in distributed training, consider settings like learning rate, batch size, and momentum. Tuning these parameters can significantly impact performance.