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

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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