pytorch-fsdp2

To train large PyTorch models exceeding single-GPU memory, you can use FSDP2 to shard parameters, gradients, and optimizer states across multiple GPUs. This integration enables efficient distributed training for massive models.

DTensor-based sharding in FSDP2 works by distributing tensor computations across a DeviceMesh, enabling multi-dimensional parallelism. It configures initialization and sharding to efficiently manage memory and scale training.

Yes, you can configure mixed precision and offload settings in PyTorch distributed training using FSDP2. This Skill manages these configurations to optimize GPU memory usage during large model training.

The best way to integrate fully_shard is by following provided procedures to add FSDP2 to your training scripts. This involves configuring initialization, sharding, and distributed checkpointing for large models.

Yes, FSDP2 supports distributed checkpointing for large model training. This Skill configures checkpointing alongside sharding and mixed precision to ensure training states are saved across distributed GPUs.

You need DeviceMesh composition for multi-dimensional parallelism in PyTorch when scaling FSDP2 across multiple GPUs. It enables DTensor-based sharding to efficiently manage memory for massive models.