pytorch-fsdp

PyTorch FSDP (Fully Sharded Data Parallel) is a distributed training technique that shards parameters across GPUs to reduce memory requirements. You need FSDP when training large-scale models that exceed single-GPU memory capacity, enabling efficient distributed computing.

Parameter sharding with PyTorch FSDP is implemented by wrapping your model with FSDP classes, which automatically shard parameters, gradients, and optimizer states across data parallel workers. This Skill provides expert guidance on configuring sharding for optimal distributed training performance.

Yes, PyTorch FSDP supports mixed precision training and CPU offloading. Mixed precision improves training efficiency by using lower precision formats, while CPU offloading moves computations to CPU to optimize GPU memory usage during large-scale model training.

Yes, PyTorch FSDP works with transformers for large-scale model training. This Skill requires PyTorch with FSDP support and the transformers library, providing expert guidance for AI engineers implementing distributed training in large-scale computing environments.

FSDP2 is an updated version of PyTorch's Fully Sharded Data Parallel with enhanced features for distributed training. This Skill covers FSDP2 features alongside standard FSDP, providing guidance on parameter sharding, mixed precision, and CPU offloading for both implementations.

CPU offloading in PyTorch FSDP optimizes GPU memory by moving computations to CPU, but may introduce latency trade-offs. This Skill provides expert guidance on when to use CPU offloading and how to balance performance with memory optimization in distributed training environments.