onnxruntime/docs/install/index.md

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Install ONNX Runtime	Instructions to install ONNX Runtime on your target platform in your environment	false	1	/docs/how-to/install

Install ONNX Runtime (ORT)

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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Requirements

• 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

• Windows builds require Visual C++ 2019 runtime. The latest version is recommended.

Python Installs

Install ONNX Runtime (ORT)

Install ONNX Runtime CPU

pip install onnxruntime

Install ONNX Runtime GPU (CUDA 11.x)

The default CUDA version for ORT is 11.8

pip install onnxruntime-gpu

Install ONNX Runtime GPU (CUDA 12.x)

For Cuda 12.x please using the following instructions to download the instruction from ORT Azure Devops Feed

1. Install keyring for Azure Artifacts

python -m pip install --upgrade pip
pip install keyring artifacts-keyring

If you're using Linux, ensure you've installed the prerequisites, which are required for artifacts-keyring.

2. Project setup

Ensure you have installed the latest version of the Azure Artifacts keyring from the "Get the tools" menu. If you don't already have one, create a virtualenv using these instructions from the official Python documentation. Per the instructions, "it is always recommended to use a virtualenv while developing Python applications."

Add a pip.ini (Windows) or pip.conf (Linux) file to your virtualenv

pip install onnxruntime-gpu --index-url https://aiinfra.pkgs.visualstudio.com/PublicPackages/_packaging/onnxruntime-cuda-12/pypi/simple/

3. Install onnxruntime-gpu

pip install onnxruntime-gpu

Install ONNX to export the model

## ONNX is built into PyTorch
pip install torch

## tensorflow
pip install tf2onnx

## sklearn
pip install skl2onnx

C#/C/C++/WinML Installs

Install ONNX Runtime (ORT)

Install ONNX Runtime CPU

# CPU
dotnet add package Microsoft.ML.OnnxRuntime

Install ONNX Runtime GPU (CUDA 11.x)

The default CUDA version for ORT is 11.8

# GPU
dotnet add package Microsoft.ML.OnnxRuntime.Gpu

Install ONNX Runtime GPU (CUDA 12.x)

1. Project Setup

Ensure you have installed the latest version of the Azure Artifacts keyring from the its Github Repo.
Add a nuget.config file to your project in the same directory as your .csproj file.

<?xml version="1.0" encoding="utf-8"?>
<configuration>
    <packageSources>
        <clear/>
        <add key="onnxruntime-cuda-12"
             value="https://aiinfra.pkgs.visualstudio.com/PublicPackages/_packaging/onnxruntime-cuda-12/nuget/v3/index.json"/>
    </packageSources>
</configuration>

2. Restore packages

Restore packages (using the interactive flag, which allows dotnet to prompt you for credentials)

dotnet add package Microsoft.ML.OnnxRuntime.Gpu

Note: You don't need --interactive every time. dotnet will prompt you to add --interactive if it needs updated credentials.

DirectML

dotnet add package Microsoft.ML.OnnxRuntime.DirectML

WinML

dotnet add package Microsoft.AI.MachineLearning

Install on web and mobile

Unless stated otherwise, the installation instructions in this section refer to pre-built packages that include support for selected operators and ONNX opset versions based on the requirements of popular models. These packages may be referred to as "mobile packages". If you use mobile packages, your model must only use the supported opsets and operators.

Another type of pre-built package has full support for all ONNX opsets and operators, at the cost of larger binary size. These packages are referred to as "full packages".

If the pre-built mobile package supports your model/s but is too large, you can create a custom build. A custom build can include just the opsets and operators in your model/s to reduce the size.

If the pre-built mobile package does not include the opsets or operators in your model/s, you can either use the full package if available, or create a custom build.

JavaScript Installs

Install ONNX Runtime Web (browsers)

# install latest release version
npm install onnxruntime-web

# install nightly build dev version
npm install onnxruntime-web@dev

Install ONNX Runtime Node.js binding (Node.js)

# install latest release version
npm install onnxruntime-node

Install ONNX Runtime for React Native

# install latest release version
npm install onnxruntime-react-native

Install on iOS

In your CocoaPods Podfile, add the onnxruntime-c, onnxruntime-mobile-c, onnxruntime-objc, or onnxruntime-mobile-objc pod, depending on whether you want to use a full or mobile package and which API you want to use.

C/C++

use_frameworks!

# choose one of the two below:
pod 'onnxruntime-c'  # full package
#pod 'onnxruntime-mobile-c'  # mobile package

Objective-C

use_frameworks!

# choose one of the two below:
pod 'onnxruntime-objc'  # full package
#pod 'onnxruntime-mobile-objc'  # mobile package

Run pod install.

Custom build

Refer to the instructions for creating a custom iOS package.

Install on Android

Java/Kotlin

In your Android Studio Project, make the following changes to:

1. build.gradle (Project):

    repositories {
        mavenCentral()
    }
   

2. build.gradle (Module):

   dependencies {
       // choose one of the two below:
       implementation 'com.microsoft.onnxruntime:onnxruntime-android:latest.release'  // full package
       //implementation 'com.microsoft.onnxruntime:onnxruntime-mobile:latest.release'  // mobile package
   }
   

C/C++

Download the onnxruntime-android ( full package) or onnxruntime-mobile ( mobile package) AAR hosted at MavenCentral, change the file extension from .aar to .zip, and unzip it. Include the header files from the headers folder, and the relevant libonnxruntime.so dynamic library from the jni folder in your NDK project.

Custom build

Refer to the instructions for creating a custom Android package.

Install for On-Device Training

Unless stated otherwise, the installation instructions in this section refer to pre-built packages designed to perform on-device training.

If the pre-built training package supports your model but is too large, you can create a custom training build.

Offline Phase - Prepare for Training

python -m pip install cerberus flatbuffers h5py numpy>=1.16.6 onnx packaging protobuf sympy setuptools>=41.4.0
pip install -i https://aiinfra.pkgs.visualstudio.com/PublicPackages/_packaging/ORT/pypi/simple/ onnxruntime-training-cpu

Training Phase - On-Device Training

Device	Language	PackageName	Installation Instructions
Windows	C, C++, C#	Microsoft.ML.OnnxRuntime.Training	dotnet add package Microsoft.ML.OnnxRuntime.Training
Linux	C, C++	onnxruntime-training-linux*.tgz	Download the *.tgz file from here. Extract it. Move and include the header files in the include directory. Move the libonnxruntime.so dynamic library to a desired path and include it.
	Python	onnxruntime-training	pip install onnxruntime-training
Android	C, C++	onnxruntime-training-android	Download the onnxruntime-training-android (full package) AAR hosted at Maven Central. Change the file extension from .aar to .zip, and unzip it. Include the header files from the headers folder. Include the relevant libonnxruntime.so dynamic library from the jni folder in your NDK project.
	Java/Kotlin	onnxruntime-training-android	In your Android Studio Project, make the following changes to: build.gradle (Project): repositories { mavenCentral() } build.gradle (Module): dependencies { implementation 'com.microsoft.onnxruntime:onnxruntime-training-android:latest.release' }
iOS	C, C++	CocoaPods: onnxruntime-training-c	In your CocoaPods Podfile, add the onnxruntime-training-c pod: use_frameworks! pod 'onnxruntime-training-c' Run pod install.
	Objective-C	CocoaPods: onnxruntime-training-objc	In your CocoaPods Podfile, add the onnxruntime-training-objc pod: use_frameworks! pod 'onnxruntime-training-objc' Run pod install.
Web	JavaScript, TypeScript	onnxruntime-web	npm install onnxruntime-web Use either import * as ort from 'onnxruntime-web/training'; or const ort = require('onnxruntime-web/training');

Large Model Training

pip install torch-ort
python -m torch_ort.configure

Note: This installs the default version of the torch-ort and onnxruntime-training packages that are mapped to specific versions of the CUDA libraries. Refer to the install options in onnxruntime.ai.

Inference install table for all languages

The table below lists the build variants available as officially supported packages. Others can be built from source from each release branch.

In addition to general requirements, please note additional requirements and dependencies in the table below:

	Official build	Nightly build	Reqs
Python	If using pip, run pip install --upgrade pip prior to downloading.
	CPU: onnxruntime	ort-nightly (dev)
	GPU (CUDA/TensorRT): onnxruntime-gpu	ort-nightly-gpu (dev)	View
	GPU (DirectML): onnxruntime-directml	ort-nightly-directml (dev)	View
	OpenVINO: intel/onnxruntime - Intel managed		View
	TensorRT (Jetson): Jetson Zoo - NVIDIA managed
	Azure (Cloud): onnxruntime-azure
C#/C/C++	CPU: Microsoft.ML.OnnxRuntime	ort-nightly (dev)
	GPU (CUDA/TensorRT): Microsoft.ML.OnnxRuntime.Gpu	ort-nightly (dev)	View
	GPU (DirectML): Microsoft.ML.OnnxRuntime.DirectML	ort-nightly (dev)	View
WinML	Microsoft.AI.MachineLearning	ort-nightly (dev)	View
Java	CPU: com.microsoft.onnxruntime:onnxruntime		View
	GPU (CUDA/TensorRT): com.microsoft.onnxruntime:onnxruntime_gpu		View
Android	com.microsoft.onnxruntime:onnxruntime-mobile		View
iOS (C/C++)	CocoaPods: onnxruntime-mobile-c		View
Objective-C	CocoaPods: onnxruntime-mobile-objc		View
React Native	onnxruntime-react-native (latest)	onnxruntime-react-native (dev)	View
Node.js	onnxruntime-node (latest)	onnxruntime-node (dev)	View
Web	onnxruntime-web (latest)	onnxruntime-web (dev)	View

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.

Training install table for all languages

Refer to the getting started with Optimized Training page for more fine-grained installation instructions.