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 100 billion parameters or more.[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]
| Original author(s) | Microsoft Research |
|---|---|
| Developer(s) | Microsoft |
| Initial release | May 18, 2020 |
| Stable release | v0.2.0
/ June 12, 2020 |
| Repository | github |
| Written in | Python, CUDA, C++ |
| Type | Software library |
| License | MIT License |
| Website | deepspeed |
See also
- Deep learning
- Machine learning
- Comparison of deep learning software
References
- "Microsoft Updates Windows, Azure Tools with an Eye on The Future". PCMag UK. May 22, 2020.
- Yegulalp, Serdar (February 10, 2020). "Microsoft speeds up PyTorch with DeepSpeed". InfoWorld.
- Microsoft unveils "fifth most powerful" supercomputer in the world - Neowin
- "Microsoft trains world's largest Transformer language model". February 10, 2020.
- "microsoft/DeepSpeed". July 10, 2020 – via GitHub.
Further reading
- Rajbhandari, Samyam; Rasley, Jeff; Ruwase, Olatunji; He, Yuxiong (2019). "ZeRO: Memory Optimization Towards Training A Trillion Parameter Models" (PDF). Cite journal requires
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External links
- AI at Scale - Microsoft Research
- GitHub - microsoft/DeepSpeed
- ZeRO & DeepSpeed: New system optimizations enable training models with over 100 billion parameters - Microsoft Research
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