DeepSpeed

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

DeepSpeed
Original author(s)Microsoft Research
Developer(s)Microsoft
Initial releaseMay 18, 2020 (2020-05-18)
Stable release
v0.9.2 / May 3, 2023 (2023-05-03)
Repositorygithub.com/microsoft/DeepSpeed
Written inPython, CUDA, C++
TypeSoftware library
LicenseApache License 2.0
Websitedeepspeed.ai

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
  • Rajbhandari, Samyam; Rasley, Jeff; Ruwase, Olatunji; He, Yuxiong (2019). "ZeRO: Memory Optimization Towards Training A Trillion Parameter Models". arXiv:1910.02054 [cs.LG].


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