pytorch-fsdp

Fully sharded data parallelism partitions model parameters, gradients, and optimizer states across distributed GPU clusters to reduce per-GPU memory. This allows training massive models that exceed single GPU limits while maintaining high throughput.

Parameter sharding for distributed training is configured using the fully_shard API. This interface facilitates setting up device meshes, sharding strategies, and collective communication backends like NCCL or Gloo for high-performance gradient synchronization.

Yes, FSDP supports uneven inputs across data parallel workers by utilizing the Join context manager. This handles variable batch sizes during collective communication operations, ensuring stable distributed training without crashing.

Fully sharded data parallelism manages state and gradients in eager-mode PyTorch workflows by sharding optimizer states and synchronizing gradients across workers. This structured interface optimizes memory usage during large-scale model training.

Yes, you can train transformer models using this Skill, as it lists the transformers library as a dependency. It provides comprehensive guidance on implementing fully sharded data parallelism for large language models exceeding single GPU memory.