deepspeedai/DeepSpeed

DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

What it solves

DeepSpeed is designed to overcome the memory and compute limitations of GPU hardware when training extremely large deep learning models. It enables the training of models with hundreds of billions or even trillions of parameters, making large-scale AI training more efficient, effective, and accessible.

How it works

DeepSpeed employs a variety of system-level innovations to optimize memory usage and throughput. Key technologies include:

  • ZeRO (Zero Redundancy Optimizer): Reduces memory redundancy across GPUs by partitioning optimizer states, gradients, and parameters.
  • 3D-Parallelism: Combines different types of parallelism to scale training across many devices.
  • Offloading: Technologies like ZeRO-Infinity, ZenFlow, and SuperOffload move data between GPU memory and CPU/NVMe storage to break the "memory wall."
  • Sequence Parallelism: Specifically designed for long-context LLM training (e.g., Ulysses Sequence Parallelism).

Who it’s for

It is intended for AI researchers and engineers who are training massive language models or other large-scale deep learning models and need to scale their training across multiple GPUs or different hardware accelerators (NVIDIA, AMD, Intel, Huawei).

Highlights

  • Proven Scale: Used to train world-leading models like MT-530B and BLOOM.
  • Hugging Face Integration: Deeply integrated with the Transformers and Accelerate libraries.
  • Broad Hardware Support: Compatible with NVIDIA, AMD, Intel Gaudi/XPU, and Huawei Ascend NPUs.
  • Advanced Memory Management: Features like ZeRO and ZeRO-Infinity allow for training models that would otherwise exceed GPU memory limits.