DeepSpeed

Last updated
DeepSpeed
Original author(s) Microsoft Research
Developer(s) Microsoft
Initial releaseMay 18, 2020;4 years ago (2020-05-18)
Stable release
v0.16.2 / December 18, 2024;2 months ago (2024-12-18)
Repository github.com/microsoft/DeepSpeed
Written in Python, CUDA, C++
Type Software library
License Apache License 2.0
Website deepspeed.ai

DeepSpeed is an open source deep learning optimization library for PyTorch. [1]

Contents

Library

The library is designed to reduce computing power and memory use and to train large distributed models with better parallelism on existing computer hardware. [2] [3] DeepSpeed is optimized for low latency, high throughput training. It includes the Zero Redundancy Optimizer (ZeRO) for training models with 1 trillion or more parameters. [4] Features include mixed precision training, single-GPU, multi-GPU, and multi-node training as well as custom model parallelism. The DeepSpeed source code is licensed under MIT License and available on GitHub. [5]

The team claimed to achieve up to a 6.2x throughput improvement, 2.8x faster convergence, and 4.6x less communication. [6]

See also

References

  1. ↑ "Microsoft Updates Windows, Azure Tools with an Eye on The Future". PCMag UK. May 22, 2020.
  2. ↑ Yegulalp, Serdar (February 10, 2020). "Microsoft speeds up PyTorch with DeepSpeed". InfoWorld.
  3. ↑ "Microsoft unveils "fifth most powerful" supercomputer in the world". Neowin. 18 June 2023.
  4. ↑ "Microsoft trains world's largest Transformer language model". February 10, 2020.
  5. ↑ "microsoft/DeepSpeed". July 10, 2020 – via GitHub.
  6. ↑ "DeepSpeed: Accelerating large-scale model inference and training via system optimizations and compression". Microsoft Research. 2021-05-24. Retrieved 2021-06-19.

Further reading

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