Modern deep learning models often exceed the memory capacity of a single GPU, and training on massive datasets can take an impractical amount of time. This chapter addresses these challenges by focusing on distributed training and parallelism techniques within PyTorch.

We will examine methods for scaling training across multiple GPUs and nodes. Key topics include:

• Fundamental concepts of distributed computing relevant to training.
• Data Parallelism with DistributedDataParallel (DDP).
• Approaches for handling extremely large models such as Tensor Model Parallelism and Pipeline Parallelism.
• The memory-efficient Fully Sharded Data Parallelism (FSDP).
• Configuration of distributed environments using different backends.
• Direct usage of PyTorch's underlying communication primitives (torch.distributed).

By the end of this chapter, you will understand how to apply various parallel processing strategies to train larger models more efficiently.