FSDP Insights | Sumble

What is FSDP?

FSDP, or Fully Sharded Data Parallel, is a data parallelism strategy used in deep learning to train large models that would otherwise not fit in the memory of a single GPU. FSDP shards the model parameters, optimizer states, and gradients across multiple GPUs, allowing for training models with billions or even trillions of parameters. During the forward and backward passes, the necessary shards are gathered to each GPU on demand, and then discarded, thus reducing the memory footprint.

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What other technologies are related to FSDP?

FSDP Competitor Technologies

GSPMD

GSPMD is a compiler and runtime system for distributed training of large models, providing an alternative approach to data and model parallelism, making it a competitor.

mentioned alongside FSDP in 99% (79) of relevant job posts

DeepSpeed

DeepSpeed offers similar capabilities to FSDP for large model training, including data parallelism, model parallelism, and optimization techniques, thus competing with FSDP.

mentioned alongside FSDP in 21% (363) of relevant job posts

Megatron

Megatron is a framework for training large transformer models with model parallelism, representing a competing approach to FSDP.

mentioned alongside FSDP in 25% (116) of relevant job posts

DDP

DDP (DistributedDataParallel) is PyTorch's built-in data parallelism implementation, and FSDP offers a more advanced alternative, especially for large models, making it a competitor.

mentioned alongside FSDP in 37% (70) of relevant job posts

JAX

JAX is a framework with automatic differentiation and XLA compilation that is often used to scale model training across accelerators. Therefore, it is a competitor.

mentioned alongside FSDP in 2% (171) of relevant job posts

FSDP Complementary Technologies

torch.fx

torch.fx is a tracing-based Python-to-Python platform for program transforms and dynamic graph execution. It can be used to further optimize FSDP training by manipulating the computational graph.

mentioned alongside FSDP in 65% (63) of relevant job posts

XLA

XLA (Accelerated Linear Algebra) is a domain-specific compiler for linear algebra that can optimize the performance of PyTorch models when used with FSDP.

mentioned alongside FSDP in 15% (171) of relevant job posts

CUTLASS

CUTLASS is a collection of CUDA C++ template abstractions for implementing high-performance matrix-multiplication, which can be leveraged to improve the efficiency of FSDP.

mentioned alongside FSDP in 21% (64) of relevant job posts