onnxruntime/docs/how-to/install.md

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Install ORT	How to	1

Install ONNX Runtime

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See the installation matrix for recommended instructions for desired combinations of target operating system, hardware, accelerator, and language.

Details on OS versions, compilers, language versions, dependent libraries , etc can be found under Compatibility.

Contents

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Inference

The following build variants are available as officially supported packages. Others can be built from source from each release branch.

1. Default CPU Provider
2. GPU Provider - NVIDIA CUDA
3. GPU Provider - DirectML (Windows) - recommended for optimized performance and compatibility with a broad set of GPUs on Windows devices

Repository	Official build	Nightly build
Python	If using pip, run pip install --upgrade pip prior to downloading.
	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

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.

Requirements

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• All builds require the English language package with en_US.UTF-8 locale. On Linux, install language-pack-en package by running locale-gen en_US.UTF-8 and update-locale LANG=en_US.UTF-8

• The GPU CUDA build requires installation of compatible CUDA and cuDNN libraries: see CUDA Execution Provider requirements.

• Windows builds require Visual C++ 2019 runtime.

Training

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