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Nvidia TensorRT开源软件

2020-12-02 07:33:45 作者：互联网

TensorRT开源软件

此存储库包含NVIDIA TensorRT的开源软件（OSS）组件。其中包括TensorRT插件和解析器（Caffe和ONNX）的源代码，以及演示TensorRT平台使用和功能的示例应用程序。这些开源软件组件是TensorRT General Availability（GA）发行版的一个子集，其中包含一些扩展和错误修复。

对于TensorRT OSS的代码贡献，请参阅我们的贡献指南和编码指南。

有关TensorRT OSS发行版附带的新添加和更新的摘要，请参阅变更日志。

Build

Prerequistites

要构建TensorRT OSS组件，首先需要以下软件包。

参考链接：https://github.com/NVIDIA/TensorRT

TensorRT GA build

TensorRT v7.2.1

See Downloading TensorRT Builds for details

System Packages

CUDA

Recommended versions:
cuda-11.1 + cuDNN-8.0
cuda-11.0 + cuDNN-8.0
cuda-10.2 + cuDNN-8.0

GNU make >= v4.1
cmake >= v3.13
python >= v3.6.5
pip >= v19.0
Essential utilities

git, pkg-config, wget, zlib

Optional Packages

Containerized build

Docker >= 19.03
NVIDIA Container Toolkit

Toolchains and SDKs

(Cross compilation for Jetson platform) NVIDIA JetPack >= 4.4
(For Windows builds) Visual Studio 2017 Community or Enterprise edition
(Cross compilation for QNX platform) QNX Toolchain

PyPI packages (for demo applications/tests)

numpy
onnx 1.6.0
onnxruntime >= 1.3.0
pytest
tensorflow-gpu 1.15.4

Code formatting tools (for contributors)

NOTE: onnx-tensorrt, cub, and protobuf packages are downloaded along with TensorRT OSS, and not required to be installed.

Downloading TensorRT Build

1. Download TensorRT OSS

On Linux: Bash

git clone -b master https://github.com/nvidia/TensorRT TensorRT

cd TensorRT

git submodule update --init --recursive

export TRT_SOURCE=`pwd`

On Windows: Powershell

git clone -b master https://github.com/nvidia/TensorRT TensorRT

cd TensorRT

git submodule update --init --recursive

$Env:TRT_SOURCE = $(Get-Location)

2. Download TensorRT GA

To build TensorRT OSS, obtain the corresponding TensorRT GA build from NVIDIA Developer Zone.

Example: Ubuntu 18.04 on x86-64 with cuda-11.1

Download and extract the latest TensorRT 7.2.1 GA package for Ubuntu 18.04 and CUDA 11.1

cd ~/Downloads

tar -xvzf TensorRT-7.2.1.6.Ubuntu-18.04.x86_64-gnu.cuda-11.1.cudnn8.0.tar.gz

export TRT_RELEASE=`pwd`/TensorRT-7.2.1.6

Example: Ubuntu 18.04 on PowerPC with cuda-11.0

Download and extract the latest TensorRT 7.2.1 GA package for Ubuntu 18.04 and CUDA 11.0

cd ~/Downloads

tar -xvzf TensorRT-7.2.1.6.Ubuntu-18.04.powerpc64le-gnu.cuda-11.0.cudnn8.0.tar.gz

export TRT_RELEASE=`pwd`/TensorRT-7.2.1.6

Example: CentOS/RedHat 7 on x86-64 with cuda-11.0

Download and extract the TensorRT 7.2.1 GA for CentOS/RedHat 7 and CUDA 11.0 tar package

cd ~/Downloads

tar -xvzf TensorRT-7.2.1.6.CentOS-7.6.x86_64-gnu.cuda-11.0.cudnn8.0.tar.gz

export TRT_RELEASE=`pwd`/TensorRT-7.2.1.6

Example: Ubuntu18.04 Cross-Compile for QNX with cuda-10.2

Download and extract the TensorRT 7.2.1 GA for QNX and CUDA 10.2 tar package

cd ~/Downloads

tar -xvzf TensorRT-7.2.1.6.Ubuntu-18.04.aarch64-qnx.cuda-10.2.cudnn7.6.tar.gz

export TRT_RELEASE=`pwd`/TensorRT-7.2.1.6

export QNX_HOST=/<path-to-qnx-toolchain>/host/linux/x86_64

export QNX_TARGET=/<path-to-qnx-toolchain>/target/qnx7

Example: Windows on x86-64 with cuda-11.0

Download and extract the TensorRT 7.2.1 GA for Windows and CUDA 11.0 zip package and add msbuild to PATH

cd ~\Downloads

Expand-Archive .\TensorRT-7.2.1.6.Windows10.x86_64.cuda-11.0.cudnn8.0.zip

$Env:TRT_RELEASE = '$(Get-Location)\TensorRT-7.2.1.6'

$Env:PATH += 'C:\Program Files (x86)\Microsoft Visual Studio\2017\Professional\MSBuild\15.0\Bin\'

3. (Optional) JetPack SDK for Jetson builds

Using the JetPack SDK manager, download the host components. Steps:

i. Download and launch the SDK manager. Login with your developer account.

ii. Select the platform and target OS (example: Jetson AGX Xavier, Linux Jetpack 4.4), and click Continue.

iii. Under Download & Install Options change the download folder and select Download now, Install later. Agree to the license terms and click Continue.

iv. Move the extracted files into the $TRT_SOURCE/docker/jetpack_files folder.

Setting Up The Build Environment

For native builds, install the prerequisite System Packages. Alternatively (recommended for non-Windows builds), install Docker and generate a build container as described below:

1. Generate the TensorRT-OSS build container.

The TensorRT-OSS build container can be generated using the Dockerfiles and build script included with TensorRT-OSS. The build container is bundled with packages and environment required for building TensorRT OSS.

Example: Ubuntu 18.04 on x86-64 with cuda-11.1

./docker/build.sh --file docker/ubuntu.Dockerfile --tag tensorrt-ubuntu --os 18.04 --cuda 11.1

Example: Ubuntu 18.04 on PowerPC with cuda-11.0

./docker/build.sh --file docker/ubuntu-cross-ppc64le.Dockerfile --tag tensorrt-ubuntu-ppc --os 18.04 --cuda 11.0

Example: CentOS/RedHat 7 on x86-64 with cuda-11.0

./docker/build.sh --file docker/centos.Dockerfile --tag tensorrt-centos --os 7 --cuda 11.0

Example: Ubuntu 18.04 Cross-Compile for Jetson (arm64) with cuda-10.2 (JetPack)

./docker/build.sh --file docker/ubuntu-cross-aarch64.Dockerfile --tag tensorrt-cross-jetpack --os 18.04 --cuda 10.2

2. Launch the TensorRT-OSS build container.

Example: Ubuntu 18.04 build container

./docker/launch.sh --tag tensorrt-ubuntu --gpus all --release $TRT_RELEASE --source $TRT_SOURCE

NOTE:

i. Use the tag corresponding to the build container you generated in
ii. To run TensorRT/CUDA programs in the build container, install NVIDIA Container Toolkit. Docker versions < 19.03 require nvidia-docker2 and --runtime=nvidia flag for docker run commands. On versions >= 19.03, you need the nvidia-container-toolkit package and --gpus all flag.

Building TensorRT-OSS

Generate Makefiles or VS project (Windows) and build.

Example: Linux (x86-64) build with default cuda-11.1

 cd $TRT_SOURCE

 mkdir -p build && cd build

 cmake .. -DTRT_LIB_DIR=$TRT_RELEASE/lib -DTRT_OUT_DIR=`pwd`/out

 make -j$(nproc)

Example: Native build on Jetson (arm64) with cuda-10.2

cd $TRT_SOURCE

mkdir -p build && cd build

cmake .. -DTRT_LIB_DIR=$TRT_RELEASE/lib -DTRT_OUT_DIR=`pwd`/out -DTRT_PLATFORM_ID=aarch64 -DCUDA_VERSION=10.2

make -j$(nproc)

Example: Ubuntu 18.04 Cross-Compile for Jetson (arm64) with cuda-10.2 (JetPack)

 cd $TRT_SOURCE

 mkdir -p build && cd build

 cmake .. -DTRT_LIB_DIR=$TRT_RELEASE/lib -DTRT_OUT_DIR=`pwd`/out -DCMAKE_TOOLCHAIN_FILE=$TRT_SOURCE/cmake/toolchains/cmake_aarch64.toolchain -DCUDA_VERSION=10.2

 make -j$(nproc)

Example: Cross-Compile for QNX with cuda-10.2

 cd $TRT_SOURCE

 mkdir -p build && cd build

 cmake .. -DTRT_LIB_DIR=$TRT_RELEASE/lib -DTRT_OUT_DIR=`pwd`/out -DCMAKE_TOOLCHAIN_FILE=$TRT_SOURCE/cmake/toolchains/cmake_qnx.toolchain -DCUDA_VERSION=10.2

 make -j$(nproc)

Example: Windows (x86-64) build in Powershell

 cd $Env:TRT_SOURCE

 mkdir -p build ; cd build

 cmake .. -DTRT_LIB_DIR=$Env:TRT_RELEASE\lib -DTRT_OUT_DIR='$(Get-Location)\out' -DCMAKE_TOOLCHAIN_FILE=..\cmake\toolchains\cmake_x64_win.toolchain

 msbuild ALL_BUILD.vcxproj

NOTE:

The default CUDA version used by CMake is 11.1. To override this, for example to 10.2, append -DCUDA_VERSION=10.2 to the cmake command.
If samples fail to link on CentOS7, create this symbolic link: ln -s $TRT_OUT_DIR/libnvinfer_plugin.so $TRT_OUT_DIR/libnvinfer_plugin.so.7

Required CMake build arguments are:
Optional CMake build arguments:
The TensorRT python API bindings must be installed for running TensorRT python applications

TRT_LIB_DIR: Path to the TensorRT installation directory containing libraries.
TRT_OUT_DIR: Output directory where generated build artifacts will be copied.

CMAKE_BUILD_TYPE: Specify if binaries generated are for release or debug (contain debug symbols). Values consists of [Release] | Debug
CUDA_VERISON: The version of CUDA to target, for example [11.1].
CUDNN_VERSION: The version of cuDNN to target, for example [8.0].
NVCR_SUFFIX: Optional nvcr/cuda image suffix. Set to "-rc" for CUDA11 RC builds until general availability. Blank by default.
PROTOBUF_VERSION: The version of Protobuf to use, for example [3.0.0]. Note: Changing this will not configure CMake to use a system version of Protobuf, it will configure CMake to download and try building that version.
CMAKE_TOOLCHAIN_FILE: The path to a toolchain file for cross compilation.
BUILD_PARSERS: Specify if the parsers should be built, for example [ON] | OFF. If turned OFF, CMake will try to find precompiled versions of the parser libraries to use in compiling samples. First in ${TRT_LIB_DIR}, then on the system. If the build type is Debug, then it will prefer debug builds of the libraries before release versions if available.
BUILD_PLUGINS: Specify if the plugins should be built, for example [ON] | OFF. If turned OFF, CMake will try to find a precompiled version of the plugin library to use in compiling samples. First in ${TRT_LIB_DIR}, then on the system. If the build type is Debug, then it will prefer debug builds of the libraries before release versions if available.
BUILD_SAMPLES: Specify if the samples should be built, for example [ON] | OFF.
CUB_VERSION: The version of CUB to use, for example [1.8.0].
GPU_ARCHS: GPU (SM) architectures to target. By default we generate CUDA code for all major SMs. Specific SM versions can be specified here as a quoted space-separated list to reduce compilation time and binary size. Table of compute capabilities of NVIDIA GPUs can be found here. Examples:

NVidia A100: -DGPU_ARCHS="80"
Tesla T4, GeForce RTX 2080: -DGPU_ARCHS="75"
Titan V, Tesla V100: -DGPU_ARCHS="70"
Multiple SMs: -DGPU_ARCHS="80 75"

TRT_PLATFORM_ID: Bare-metal build (unlike containerized cross-compilation) on non Linux/x86 platforms must explicitly specify the target platform. Currently supported options: x86_64 (default), aarch64

(Optional) Install TensorRT python bindings

Example: install TensorRT wheel for python 3.6

pip3 install $TRT_RELEASE/python/tensorrt-7.2.1.6-cp36-none-linux_x86_64.whl

References

TensorRT Resources

Known Issues

TensorRT 7.2.1

None

标签：--,TensorRT,7.2,开源,build,Nvidia,TRT,cuda
来源： https://www.cnblogs.com/wujianming-110117/p/14071754.html