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Build ONNX Runtime

Supported architectures

x86_32 x86_64 ARM32 ARM64
Windows YES YES YES YES
Linux YES YES YES YES
Mac OS X NO YES NO NO

Supported dev environments

OS Supports CPU Supports GPU Notes
Windows 10 YES YES Must use VS 2017 or the latest VS2015
Windows 10
Subsystem for Linux		 YES NO
Ubuntu 16.x YES YES Also supported on ARM32v7 (experimental)
Ubuntu 17.x YES YES
Ubuntu 18.x YES YES
Fedora 23 YES YES
Fedora 24 YES YES
Fedora 25 YES YES
Fedora 26 YES YES
Fedora 27 YES YES
Fedora 28 YES NO Cannot build GPU kernels but can run them
  • Red Hat Enterprise Linux and CentOS are not supported.
  • GCC 4.x and below are not supported. If you are using GCC 7.0+, you'll need to upgrade eigen to a newer version before compiling ONNX Runtime.

OS/Compiler Matrix:

OS/Compiler Supports VC Supports GCC Supports Clang
Windows 10 YES Not tested planning
Linux NO YES(gcc>=5.0) YES

ONNX Runtime python binding only supports Python 3.x. Please use python 3.5+.

Build

  1. Checkout the source tree: 
    git clone --recursive https://github.com/Microsoft/onnxruntime
cd onnxruntime
  2. Install cmake-3.11 or better from https://cmake.org/download/.
  3. (optional) Install protobuf 3.6.1 from source code (cmake/external/protobuf). CMake flag protobuf_BUILD_SHARED_LIBS must be turned off. After the installation, you should have the 'protoc' executable in your PATH.
  4. (optional) Install onnx from source code (cmake/external/onnx) 
    export ONNX_ML=1
python3 setup.py bdist_wheel
pip3 install --upgrade dist/*.whl
  5. Run ./build.sh --config RelWithDebInfo --build\_wheel for Linux (or ./build.bat --config RelWithDebInfo --build\_wheel for Windows)

The build script runs all unit tests by default.

The complete list of build options can be found by running ./build.sh (or ./build.bat) --help

Build/Test Flavors for CI

CI Build Environments

Build Job Name Environment Dependency Test Coverage Scripts
Linux_CI_Dev Ubuntu 16.04 python=3.5 Unit tests; ONNXModelZoo script
Linux_CI_GPU_Dev Ubuntu 16.04 python=3.5; nvidia-docker Unit tests; ONNXModelZoo script
Windows_CI_Dev Windows Server 2016 python=3.5 Unit tests; ONNXModelZoo script
Windows_CI_GPU_Dev Windows Server 2016 cuda=9.1; cudnn=7.1; python=3.5 Unit tests; ONNXModelZoo script

Additional Build Flavors

The complete list of build flavors can be seen by running ./build.sh --help or ./build.bat --help. Here are some common flavors.

Windows CUDA Build

ONNX Runtime supports CUDA builds. You will need to download and install CUDA and CUDNN.

ONNX Runtime is built and tested with CUDA 9.1 and CUDNN 7.1 using the Visual Studio 2017 14.11 toolset (i.e. Visual Studio 2017 v15.3). CUDA versions from 9.1 up to 10.0, and CUDNN versions from 7.1 up to 7.4 should also work with Visual Studio 2017.

  • The path to the CUDA installation must be provided via the CUDA_PATH environment variable, or the --cuda_home parameter.
  • The path to the CUDNN installation (include the cuda folder in the path) must be provided via the CUDNN_PATH environment variable, or --cudnn_home parameter. The CUDNN path should contain bin, include and lib directories.
  • The path to the CUDNN bin directory must be added to the PATH environment variable so that cudnn64_7.dll is found.

You can build with:

./build.sh --use_cuda --cudnn_home /usr --cuda_home /usr/local/cuda (Linux)
./build.bat --use_cuda --cudnn_home <cudnn home path> --cuda_home <cuda home path> (Windows)

Depending on compatibility between the CUDA, CUDNN, and Visual Studio 2017 versions you are using, you may need to explicitly install an earlier version of the MSVC toolset. CUDA 9.2 is known to work with the 14.11 MSVC toolset (Visual Studio 15.3 and 15.4) CUDA 10.0 is known to work with toolsets from 14.11 up to 14.16 (Visual Studio 2017 15.9)

To install the 14.11 MSVC toolset, see https://blogs.msdn.microsoft.com/vcblog/2017/11/15/side-by-side-minor-version-msvc-toolsets-in-visual-studio-2017/

To use the 14.11 toolset with a later version of Visual Studio 2017 you have two options:

  1. Setup the Visual Studio environment variables to point to the 14.11 toolset by running vcvarsall.bat, prior to running the build script

    • e.g. if you have VS2017 Enterprise, an x64 build would use the following command "C:\Program Files (x86)\Microsoft Visual Studio\2017\Enterprise\VC\Auxiliary\Build\vcvarsall.bat" amd64 -vcvars_ver=14.11
    • For convenience, build.amd64.1411.bat will do this and can be used in the same way as build.bat.
      • e.g. .\build.amd64.1411.bat --use_cuda

  2. Alternatively if you have CMake 3.12 or later you can specify the toolset version via the --msvc_toolset build script parameter.

    • e.g. .\build.bat --msvc_toolset 14.11

Side note: If you have multiple versions of CUDA installed on a Windows machine and are building with Visual Studio, CMake will use the build files for the highest version of CUDA it finds in the BuildCustomization folder.
e.g. C:\Program Files (x86)\Microsoft Visual Studio\2017\Enterprise\Common7\IDE\VC\VCTargets\BuildCustomizations. If you want to build with an earlier version, you must temporarily remove the 'CUDA x.y.*' files for later versions from this directory.

MKL-DNN

To build ONNX Runtime with MKL-DNN support, build it with ./build.sh --use_mkldnn --use_mklml

OpenBLAS

Windows

Instructions how to build OpenBLAS for windows can be found here https://github.com/xianyi/OpenBLAS/wiki/How-to-use-OpenBLAS-in-Microsoft-Visual-Studio#build-openblas-for-universal-windows-platform.

Once you have the OpenBLAS binaries, build ONNX Runtime with ./build.bat --use_openblas

Linux

For Linux (e.g. Ubuntu 16.04), install libopenblas-dev package sudo apt-get install libopenblas-dev and build with ./build.sh --use_openblas

OpenMP

./build.sh --use_openmp (for Linux)
./build.bat --use_openmp (for Windows)

Build with Docker on Linux

Install Docker: https://docs.docker.com/install/

CPU

cd tools/ci_build/github/linux/docker
docker build -t onnxruntime_dev --build-arg OS_VERSION=16.04 -f Dockerfile.ubuntu .
docker run --rm -it onnxruntime_dev /bin/bash

GPU

If you need GPU support, please also install:

  1. nvidia driver. Before doing this please add nomodeset rd.driver.blacklist=nouveau to your linux kernel boot parameters.
  2. nvidia-docker2: Install doc

To test if your nvidia-docker works:

docker run --runtime=nvidia --rm nvidia/cuda nvidia-smi

Then build a docker image. We provided a sample for use:

cd tools/ci_build/github/linux/docker
docker build -t cuda_dev -f Dockerfile.ubuntu_gpu .

Then run it

./tools/ci_build/github/linux/run_dockerbuild.sh

ARM Builds

We've experimental support for Linux ARM builds. Windows on ARM is well tested.

Cross compiling on Linux(FASTER)

  1. Get the corresponding toolchain. For example, if your device is Raspberry Pi and the device os is Ubuntu 16.04, you may use gcc-linaro-6.3.1 from https://releases.linaro.org/components/toolchain/binaries
  2. Setup env vars 
       export PATH=/opt/gcc-linaro-6.3.1-2017.05-x86_64_arm-linux-gnueabihf/bin:$PATH
   export CC=arm-linux-gnueabihf-gcc
   export CXX=arm-linux-gnueabihf-g++
  3. Get a pre-compiled protoc: You may get it from https://github.com/protocolbuffers/protobuf/releases/download/v3.6.1/protoc-3.6.1-linux-x86_64.zip . Please unzip it after downloading.
  4. (optional) Setup sysroot for enabling python extension. (TODO: will add details later)
  5. Save the following content as tool.cmake 
    set(CMAKE_SYSTEM_NAME Linux)
set(CMAKE_SYSTEM_PROCESSOR arm)
set(CMAKE_CXX_COMPILER arm-linux-gnueabihf-c++)
set(CMAKE_C_COMPILER arm-linux-gnueabihf-gcc)
set(CMAKE_FIND_ROOT_PATH_MODE_PROGRAM NEVER)
set(CMAKE_FIND_ROOT_PATH_MODE_LIBRARY ONLY)
set(CMAKE_FIND_ROOT_PATH_MODE_INCLUDE ONLY)
set(CMAKE_FIND_ROOT_PATH_MODE_PACKAGE ONLY)
  6. Append -DONNX_CUSTOM_PROTOC_EXECUTABLE=/path/to/protoc -DCMAKE_TOOLCHAIN_FILE=path/to/tool.cmake to your cmake args, run cmake and make to build it.

Native compiling on Linux (SLOWER)

Please see ARM docker file. Docker build runs on a Raspberry Pi 3B with Raspbian Stretch Lite OS (Desktop version will run out memory when linking the .so file) will take 8-9 hours in total. If you want to use Azure Container Registry Tasks to build the Docker image in cloud, you may want to split this Dockerfile to two steps:

  1. Build environment image creation: steps before onnxruntime repo clone
  2. ONNX Runtime and Python binding creation: the rest of steps in the original Dockerfile with step 1 output as base image.

By doing this, you could avoid hit the ACR-Tasks build timeout (8 hours)

Cross compiling on Windows

(TODO)