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Documentation update: build instructions (#2636)
* Spacing fix for code block * Update instructions Include java, acl, and nn api instructions on build page * Update build instructions to link to build.md * typo * Update build instructions to link to build.md * Include other minor build.md page updates * Update CUDA version * Fix dockerfile links
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Faith Xu
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75
BUILD.md
75
BUILD.md
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@ -1,5 +1,5 @@
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# Building ONNX Runtime
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*Dockerfiles are available [here](https://github.com/microsoft/onnxruntime/tree/master/tools/ci_build/github/linux/docker) to help you get started.*
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*Dockerfiles are available [here](./dockerfiles) to help you get started.*
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*Pre-built packages are available at the locations indicated [here](https://github.com/microsoft/onnxruntime#official-builds).*
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@ -61,8 +61,6 @@ The default Windows CMake Generator is Visual Studio 2017, but you can also use
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|Windows 10 <br/> Subsystem for Linux | YES | NO | |
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|Ubuntu 16.x | YES | YES | Also supported on ARM32v7 (experimental) |
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* Red Hat Enterprise Linux and CentOS are not supported.
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* Other version of Ubuntu might work but we don't support them officially.
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* GCC 4.x and below are not supported.
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### OS/Compiler Matrix:
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@ -70,7 +68,7 @@ The default Windows CMake Generator is Visual Studio 2017, but you can also use
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| OS/Compiler | Supports VC | Supports GCC |
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|-------------|:------------:|:----------------:|
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|Windows 10 | YES | Not tested |
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|Linux | NO | YES(gcc>=5.0) |
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|Linux | NO | YES(gcc>=4.8) |
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## System Requirements
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For other system requirements and other dependencies, please see [this section](./README.md#system-requirements-pre-requisite-dependencies).
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@ -86,6 +84,7 @@ For other system requirements and other dependencies, please see [this section](
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|**Build Shared Library**|--build_shared_lib||
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|**Build Python wheel**|--build_wheel||
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|**Build C# and C packages**|--build_csharp||
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|**Build Java package**|--build_java|Creates an onnxruntime4j.jar in the build directory, implies `--build_shared_lib`|
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# Additional Build Instructions
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@ -102,6 +101,7 @@ The complete list of build options can be found by running `./build.sh (or .\bui
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* [Android NNAPI](#Android-NNAPI)
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* [Nuphar Model Compiler](#Nuphar)
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* [DirectML](#DirectML)
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* [ARM Compute Library](#ARM-Compute-Library)
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**Options**
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* [OpenMP](#OpenMP)
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@ -116,7 +116,7 @@ The complete list of build options can be found by running `./build.sh (or .\bui
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---
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## Build ONNX Runtime Server on Linux
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Read more about ONNX Runtime Server [here](https://github.com/microsoft/onnxruntime/blob/master/docs/ONNX_Runtime_Server_Usage.md)
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Read more about ONNX Runtime Server [here](./docs/ONNX_Runtime_Server_Usage.md).
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### Pre-Requisites
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* ONNX Runtime server (and only the server) requires you to have Go installed to build, due to building BoringSSL.
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@ -159,7 +159,7 @@ Build command:
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./build.sh --use_cuda --cudnn_home <cudnn home path> --cuda_home <cuda home path>
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```
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A Dockerfile is available [here](./tools/ci_build/github/linux/docker/Dockerfile.ubuntu_gpu).
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A Dockerfile is available [here](./dockerfiles#cuda).
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#### Notes
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@ -209,7 +209,9 @@ See more information on DNNL and MKL-ML [here](./docs/execution_providers/DNNL-E
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#### Build Instructions
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##### Linux
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DNNL: `./build.sh --use_dnnl`
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```
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./build.sh --use_dnnl
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```
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---
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@ -279,7 +281,27 @@ For more information on OpenVINO Execution Provider's ONNX Layer support, To
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### Android NNAPI
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See information on the NNAPI Execution Provider [here](./docs/execution_providers/NNAPI-ExecutionProvider.md).
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See more information on the NNAPI Execution Provider [here](./docs/execution_providers/NNAPI-ExecutionProvider.md).
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#### Pre-Requisites
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To build ONNX Runtime with the NN API EP, first install Android NDK (see [Android Build instructions](#android))
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#### Build Instructions
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The basic build commands are below. There are also some other parameters for building the Android version under See [Android Build instructions](#android) for more details.
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##### Cross compiling on Windows
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```bash
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./build.bat --android --android_ndk_path <android ndk path> --dnnlibrary
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```
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##### Cross compiling on Linux
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```bash
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./build.sh --android --android_ndk_path <android ndk path> --dnnlibrary
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```
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---
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@ -354,7 +376,7 @@ index 7dfa97c..6d99e71 100644
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./build.sh --use_tvm --use_llvm --llvm_path=/llvm/install/path/lib/cmake/llvm --use_mklml --use_nuphar --build_shared_lib --build_csharp --enable_pybind --config=Release
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```
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Dockerfile instructions are available [here](https://github.com/microsoft/onnxruntime/tree/master/dockerfiles#nuphar-public-preview)
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Dockerfile instructions are available [here](./dockerfiles#nuphar-public-preview)
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---
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@ -369,6 +391,41 @@ The DirectML execution provider supports building for both x64 and x86 architect
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---
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### ARM Compute Library
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See more information on the ACL Execution Provider [here](./docs/execution_providers/ACL-ExecutionProvider.md).
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#### Prerequisites
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* Supported backend: i.MX8QM Armv8 CPUs
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* Supported BSP: i.MX8QM BSP
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* Install i.MX8QM BSP: `source fsl-imx-xwayland-glibc-x86_64-fsl-image-qt5-aarch64-toolchain-4*.sh`
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* Set up the build environment
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```
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source /opt/fsl-imx-xwayland/4.*/environment-setup-aarch64-poky-linux
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alias cmake="/usr/bin/cmake -DCMAKE_TOOLCHAIN_FILE=$OECORE_NATIVE_SYSROOT/usr/share/cmake/OEToolchainConfig.cmake"
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```
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* See [Build ARM](#ARM) below for information on building for ARM devices
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#### Build Instructions
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1. Configure ONNX Runtime with ACL support:
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```
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cmake ../onnxruntime-arm-upstream/cmake -DONNX_CUSTOM_PROTOC_EXECUTABLE=/usr/bin/protoc -Donnxruntime_RUN_ONNX_TESTS=OFF -Donnxruntime_GENERATE_TEST_REPORTS=ON -Donnxruntime_DEV_MODE=ON -DPYTHON_EXECUTABLE=/usr/bin/python3 -Donnxruntime_USE_CUDA=OFF -Donnxruntime_USE_NSYNC=OFF -Donnxruntime_CUDNN_HOME= -Donnxruntime_USE_JEMALLOC=OFF -Donnxruntime_ENABLE_PYTHON=OFF -Donnxruntime_BUILD_CSHARP=OFF -Donnxruntime_BUILD_SHARED_LIB=ON -Donnxruntime_USE_EIGEN_FOR_BLAS=ON -Donnxruntime_USE_OPENBLAS=OFF -Donnxruntime_USE_ACL=ON -Donnxruntime_USE_DNNL=OFF -Donnxruntime_USE_MKLML=OFF -Donnxruntime_USE_OPENMP=ON -Donnxruntime_USE_TVM=OFF -Donnxruntime_USE_LLVM=OFF -Donnxruntime_ENABLE_MICROSOFT_INTERNAL=OFF -Donnxruntime_USE_BRAINSLICE=OFF -Donnxruntime_USE_NUPHAR=OFF -Donnxruntime_USE_EIGEN_THREADPOOL=OFF -Donnxruntime_BUILD_UNIT_TESTS=ON -DCMAKE_BUILD_TYPE=RelWithDebInfo
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```
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2. Build ONNX Runtime library, test and performance application:
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```
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make -j 6
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```
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3. Deploy ONNX runtime on the i.MX 8QM board
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```
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libonnxruntime.so.0.5.0
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onnxruntime_perf_test
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onnxruntime_test_all
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```
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---
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## Options
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### OpenMP
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#### Build Instructions
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@ -105,7 +105,7 @@ system.
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[vc_redist.x64.exe](https://aka.ms/vs/16/release/vc_redist.x64.exe) and [vc_redist.x86.exe](https://aka.ms/vs/16/release/vc_redist.x86.exe)
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* For Linux, the system must have **libgomp.so.1** which can be installed using `apt-get install libgomp1`.
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* GPU builds require CUDA runtime libraries being installed on the system:
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* Version: **CUDA 10.0** and **cuDNN 7.6**
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* Version: **CUDA 10.0**, **cuDNN 7.6 on Linux and cuDNN 7.3 on Windows**
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* Older ONNX Runtime releases: used **CUDA 9.1** and **cuDNN 7.1** - please refer to [prior release notes](https://github.com/microsoft/onnxruntime/releases) for more details.
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* Python binaries are compatible with **Python 3.5-3.7**. See [Python Dev Notes](./docs/Python_Dev_Notes.md). If using `pip` to be download the Python binaries, run `pip install --upgrade pip` prior to downloading.
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* The Java API is compatible with **Java 8-13**.
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@ -43,10 +43,10 @@ Here is a [complete sample program](../java/sample/ScoreMNIST.java) that runs in
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## Running on a GPU or with another provider (Optional)
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To enable other execution providers like GPUs simply turn on the appropriate flag on SessionOptions when creating an OrtSession.
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int gpuDeviceId = 0; // The GPU device ID to execute on
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var sessionOptions = new OrtSession.SessionOptions();
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sessionOptions.addCUDA(gpuDeviceId);
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var session = environment.createSession("model.onnx", sessionOptions);
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int gpuDeviceId = 0; // The GPU device ID to execute on
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var sessionOptions = new OrtSession.SessionOptions();
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sessionOptions.addCUDA(gpuDeviceId);
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var session = environment.createSession("model.onnx", sessionOptions);
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The execution providers are preferred in the order they were enabled.
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[Arm Compute Library](https://github.com/ARM-software/ComputeLibrary) is an open source inference engine maintained by Arm and Linaro companies. The integration of ACL as an execution provider (EP) into ONNX Runtime accelerates performance of ONNX model workloads across Armv8 cores.
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### Build ACL execution provider
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Developers can use ACL library through ONNX Runtime to accelerate inference performance of ONNX models. Instructions for building the ACL execution provider from the source is available below.
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### Supported BSP
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* i.MX8QM BSP
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Install i.MX8QM BSP:
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```
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source fsl-imx-xwayland-glibc-x86_64-fsl-image-qt5-aarch64-toolchain-4*.sh
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```
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Setup build environment:
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```
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source /opt/fsl-imx-xwayland/4.*/environment-setup-aarch64-poky-linux
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alias cmake="/usr/bin/cmake -DCMAKE_TOOLCHAIN_FILE=$OECORE_NATIVE_SYSROOT/usr/share/cmake/OEToolchainConfig.cmake"
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```
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Confiure ONNX Runtime with ACL support:
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```
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cmake ../onnxruntime-arm-upstream/cmake -DONNX_CUSTOM_PROTOC_EXECUTABLE=/usr/bin/protoc -Donnxruntime_RUN_ONNX_TESTS=OFF -Donnxruntime_GENERATE_TEST_REPORTS=ON -Donnxruntime_DEV_MODE=ON -DPYTHON_EXECUTABLE=/usr/bin/python3 -Donnxruntime_USE_CUDA=OFF -Donnxruntime_USE_NSYNC=OFF -Donnxruntime_CUDNN_HOME= -Donnxruntime_USE_JEMALLOC=OFF -Donnxruntime_ENABLE_PYTHON=OFF -Donnxruntime_BUILD_CSHARP=OFF -Donnxruntime_BUILD_SHARED_LIB=ON -Donnxruntime_USE_EIGEN_FOR_BLAS=ON -Donnxruntime_USE_OPENBLAS=OFF -Donnxruntime_USE_ACL=ON -Donnxruntime_USE_DNNL=OFF -Donnxruntime_USE_MKLML=OFF -Donnxruntime_USE_OPENMP=ON -Donnxruntime_USE_TVM=OFF -Donnxruntime_USE_LLVM=OFF -Donnxruntime_ENABLE_MICROSOFT_INTERNAL=OFF -Donnxruntime_USE_BRAINSLICE=OFF -Donnxruntime_USE_NUPHAR=OFF -Donnxruntime_USE_EIGEN_THREADPOOL=OFF -Donnxruntime_BUILD_UNIT_TESTS=ON -DCMAKE_BUILD_TYPE=RelWithDebInfo
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```
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Build ONNX Runtime library, test and performance application:
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```
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make -j 6
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```
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Deploy ONNX runtime on the i.MX 8QM board
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```
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libonnxruntime.so.0.5.0
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onnxruntime_perf_test
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onnxruntime_test_all
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```
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### Supported backend
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* i.MX8QM Armv8 CPUs
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For build instructions, please see the [BUILD page](../../BUILD.md#ARM-Compute-Library).
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### Using the ACL execution provider
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#### C/C++
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@ -8,25 +8,7 @@ The NNAPI EP requires Android devices with Android 8.1 or higher.
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## Build NNAPI EP
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### Pre-Requisites
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To build onnxruntime with NNAPI EP, install Android NDK first (see [BUILD.md](/BUILD.md#android))
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### Build Instructions
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The basic commands are following. There are also some other parameters for building the Android version. See [BUILD.md](/BUILD.md#android) for the details.
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#### Cross compiling on Windows
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```bash
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./build.bat --android --android_ndk_path <android ndk path> --dnnlibrary
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```
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#### Cross compiling on Linux
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```bash
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./build.sh --android --android_ndk_path <android ndk path> --dnnlibrary
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```
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For build instructions, please see the [BUILD page](../../BUILD.md#Android-NNAPI).
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## Using NNAPI EP in C/C++
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