DeepSpeed
Deep learning optimization library for efficient distributed training and inference
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Overview
DeepSpeed is a Python-based deep learning library aimed at facilitating efficient distributed training and inference, supporting PyTorch with optimizations like compression, data parallelism, model parallelism, and pipeline parallelism.
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- python
Source: github.language · Jul 11, 2026
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README
Installation
The quickest way to get started with DeepSpeed is via pip, this will install the latest release of DeepSpeed which is not tied to specific PyTorch or CUDA versions. DeepSpeed includes several C++/CUDA extensions that we commonly refer to as our 'ops'. By default, all of these extensions/ops will be built just-in-time (JIT) using torch's JIT C++ extension loader that relies on ninja to build and dynamically link them at runtime.
Requirements
- PyTorch must be installed before installing DeepSpeed.
- For full feature support we recommend a version of PyTorch that is >= 2.0 and ideally the latest PyTorch stable release.
- A CUDA or ROCm compiler such as nvcc or hipcc used to compile C++/CUDA/HIP extensions.
- Specific GPUs we develop and test against are listed below, this doesn't mean your GPU will not work if it doesn't fall into this category it's just DeepSpeed is most well tested on the following:
- NVIDIA: Pascal, Volta, Ampere, and Hopper architectures
- AMD: MI100 and MI200