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onnxruntime/docs/get-started/install.md
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Install Get Started 1

Install ONNX Runtime

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Contents

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  • TOC placeholder {:toc}

Use this guide to install ONNX Runtime and its dependencies, for your target operating system, hardware, accelerator, and language.

For an overview, see this installation matrix.

Prerequisites

Linux / CPU

  1. English language package with the en_US.UTF-8 locale

Linux / GPU

  1. English language package with the en_US.UTF-8 locale

  2. CUDA 11.0.3 and cuDNN 8.0.2.4

    • libcudart 11.0.221
    • libcufft 10.2.1.245
    • libcurand 10.2.1.245
    • libcublasLt 11.2.0.252
    • libcublas 11.2.0.252
    • libcudnn 8.0.4

Version dependencies for older ONNX Runtime releases are listed here.

Windows / CPU

  1. English language package with the en_US.UTF-8 locale

  2. Visual C++ 2019 runtime

Windows / GPU

  1. English language package with the en_US.UTF-8 locale

  2. Visual C++ 2019 runtime

  3. CUDA 11.0.3 and cuDNN 8.0.2.39

Version dependencies for older ONNX Runtime releases are listed here.

MacOS / CPU

  1. The system must have libomp.dylib which can be installed using brew install libomp.

Install

  • Default CPU Provider (Eigen + MLAS)
  • GPU Provider - NVIDIA CUDA
  • GPU Provider - DirectML (Windows)

If using pip, run pip install --upgrade pip prior to downloading.

Repository Official build Nightly build
Python CPU: onnxruntime ort-nightly (dev)
GPU: onnxruntime-gpu ort-gpu-nightly (dev)
C#/C/C++ CPU: Microsoft.ML.OnnxRuntime ort-nightly (dev)
GPU: Microsoft.ML.OnnxRuntime.Gpu ort-nightly (dev)
Java CPU: com.microsoft.onnxruntime/onnxruntime
GPU: com.microsoft.onnxruntime/onnxruntime_gpu
nodejs CPU: onnxruntime
Other Contributed non-official packages (including Homebrew, Linuxbrew, and nixpkgs)
These are not maintained by the core ONNX Runtime team and may have limited support; use at your discretion.

Note: Dev builds created from the master branch are available for testing newer changes between official releases. Please use these at your own risk. We strongly advise against deploying these to production workloads as support is limited for dev builds.

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