This repository contains the Open Source Software (OSS) components of NVIDIA TensorRT. Included are the sources for TensorRT plugins and parsers (Caffe and ONNX), as well as sample applications demonstrating usage and capabilities of the TensorRT platform.
To build the TensorRT OSS components, ensure you meet the following package requirements:
System Packages
-
- Recommended versions:
- 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
- PyPI packages
- numpy
- onnx 1.6.0
- onnxruntime >= 1.3.0
- pytest
-
Essential libraries and utilities
- Git, pkg-config, Wget, Zlib
-
Cross compilation for Jetson platforms requires JetPack's host component installation
- JetPack >= 4.4
-
Windows requires Visual Studio 2017 either Community or Enterprise Version
-
Cross compilation for QNX requires the qnx developer toolchain
Optional Packages
-
Containerized builds
- Docker >= 19.03
- NVIDIA Docker >= 2.0 or
nvidia-container-toolkit
-
Code formatting tools
-
Required PyPI packages for Demos
- Tensorflow-gpu == 1.15.0
TensorRT Release
- TensorRT v7.1
NOTE: Along with the TensorRT OSS components, the following source packages will also be downloaded, and they are not required to be installed on the system.
- ONNX-TensorRT v7.1
- CUB v1.8.0
- Protobuf v3.0.0
-
Example: Bash
git clone -b master https://github.com/nvidia/TensorRT TensorRT cd TensorRT git submodule update --init --recursive export TRT_SOURCE=`pwd`
Example: Powershell
git clone -b master https://github.com/nvidia/TensorRT TensorRT cd TensorRT git submodule update --init --recursive $Env:TRT_SOURCE = $(Get-Location)
-
To build the TensorRT OSS, obtain the corresponding TensorRT 7.1 binary release from NVidia Developer Zone. For a list of key features, known and fixed issues, refer to the TensorRT 7.1 Release Notes.
Example: Ubuntu 18.04 with cuda-11.0
Download and extract the latest TensorRT 7.1 GA package for Ubuntu 18.04 and CUDA 11.0
cd ~/Downloads tar -xvzf TensorRT-7.1.3.4.Ubuntu-18.04.x86_64-gnu.cuda-11.0.cudnn8.0.tar.gz export TRT_RELEASE=`pwd`/TensorRT-7.1.3.4
Example: Ubuntu 18.04 with cuda-11.0 on PowerPC
Download and extract the latest TensorRT 7.1 GA package for Ubuntu 18.04 and CUDA 11.0
cd ~/Downloads # Download TensorRT-7.1.3.4.Ubuntu-18.04.powerpc64le-gnu.cuda-11.0.cudnn8.0.tar.gz tar -xvzf TensorRT-7.1.3.4.Ubuntu-18.04.powerpc64le-gnu.cuda-11.0.cudnn8.0.tar.gz export TRT_RELEASE=`pwd`/TensorRT-7.1.3.4
Example: CentOS/RedHat 7 with cuda-11.0
Download and extract the TensorRT 7.1 GA for CentOS/RedHat 7 and CUDA 11.0 tar package
cd ~/Downloads tar -xvzf TensorRT-7.1.3.4.CentOS-8.0.x86_64-gnu.cuda-11.0.cudnn8.0.tar.gz export TRT_RELEASE=`pwd`/TensorRT-7.1.3.4
Example: Ubuntu 16.04 with cuda-11.0
Download and extract the TensorRT 7.1 GA for Ubuntu 16.04 and CUDA 11.0 tar package
cd ~/Downloads tar -xvzf TensorRT-7.1.3.4.Ubuntu-16.04.x86_64-gnu.cuda-11.0.cudnn8.0.tar.gz export TRT_RELEASE=`pwd`/TensorRT-7.1.3.4
Example: Ubuntu18.04 cross compile QNX with cuda-10.2
Download and extract the TensorRT 7.1 GA for QNX and CUDA 10.2 tar package
cd ~/Downloads tar -xvzf TensorRT-7.1.3.4.Ubuntu-18.04.aarch64-qnx.cuda-10.2.cudnn7.6.tar.gz export TRT_RELEASE=`pwd`/TensorRT-7.1.3.4 export QNX_HOST=/path/to/qnx/toolchain/host/linux/x86_64 export QNX_TARGET=/path/to/qnx/toolchain/target/qnx7
Example: Windows with cuda-11.0
Download and extract the TensorRT 7.1 GA for Windows and CUDA 11.0 zip package and add msbuild to PATH
cd ~\Downloads Expand-Archive .\TensorRT-7.1.3.4.Windows10.x86_64.cuda-11.0.cudnn8.0.zip $Env:TRT_RELEASE = '$(Get-Location)\TensorRT-7.1.3.4' $Env:PATH += 'C:\Program Files (x86)\Microsoft Visual Studio\2017\Professional\MSBuild\15.0\Bin\'
-
JetPack example
Using the SDK manager, download the host components of the PDK version or Jetpack specified in the name of the Dockerfile. To do this:
- [SDK Manager Step 01] Log into the SDK manager
- [SDK Manager Step 02] Select the correct platform and Target OS System (should be corresponding to the name of the Dockerfile you are building (e.g. Jetson AGX Xavier,
Linux Jetpack 4.4
), then clickContinue
- [SDK Manager Step 03] Under
Download & Install Options
make note of or change the download folder and Select Download now. Install later. then agree to the license terms and clickContinue
You should now have all expected files to build the container. Move these into thedocker/jetpack_files
folder.
-
Install the System Packages list of components in the Prerequisites section.
-
Alternatively, use the build containers as described below:
-
The docker container can be built using the included Dockerfiles and build script. The build container is configured with the environment and packages required for building TensorRT OSS.
Example: Ubuntu 18.04 with cuda-11.0
./docker/build.sh --file docker/ubuntu.Dockerfile --tag tensorrt-ubuntu --os 18.04 --cuda 11.0
Example: Ubuntu 16.04 with cuda-11.0
./docker/build.sh --file docker/ubuntu.Dockerfile --tag tensorrt-ubuntu1604 --os 16.04 --cuda 11.0
Example: CentOS/RedHat 7 with cuda-11.0
./docker/build.sh --file docker/centos.Dockerfile --tag tensorrt-centos --os 7 --cuda 11.0
Example: Cross compile for JetPack 4.4 with cuda-10.2
./docker/build.sh --file docker/ubuntu-cross-aarch64.Dockerfile --tag tensorrt-ubuntu-jetpack --os 18.04 --cuda 10.2
Example: Cross compile for PowerPC with cuda-11.0
./docker/build.sh --file docker/ubuntu-cross-ppc64le.Dockerfile --tag tensorrt-ubuntu-ppc --os 18.04 --cuda 11.0
-
./docker/launch.sh --tag tensorrt-ubuntu --gpus all --release $TRT_RELEASE --source $TRT_SOURCE
NOTE: To run TensorRT/CUDA programs in the build container, install NVIDIA Docker support. Docker versions < 19.03 require
nvidia-docker2
and--runtime=nvidia
flag for docker run commands. On versions >= 19.03, you need thenvidia-container-toolkit
package and--gpus all
flag.
-
Generate Makefiles and build.
Example: Linux
cd $TRT_SOURCE mkdir -p build && cd build cmake .. -DTRT_LIB_DIR=$TRT_RELEASE/lib -DTRT_OUT_DIR=`pwd`/out make -j$(nproc)
Example: Bare-metal 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: 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=$TRT_SOURCE/cmake/toolchains/cmake_qnx.toolchain make -j$(nproc)
Example: 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 11.0. To override this, for example to 10.2, append
-DCUDA_VERSION=10.2
to the cmake command. - Samples may fail to link on CentOS7. To work around this create the following symbolic link:
ln -s $TRT_OUT_DIR/libnvinfer_plugin.so $TRT_OUT_DIR/libnvinfer_plugin.so.7
The required CMake arguments are:
-
TRT_LIB_DIR
: Path to the TensorRT installation directory containing libraries. -
TRT_OUT_DIR
: Output directory where generated build artifacts will be copied.
The following CMake build parameters are optional:
-
CMAKE_BUILD_TYPE
: Specify if binaries generated are for release or debug (contain debug symbols). Values consists of [Release
] |Debug
-
CUDA_VERISON
: The version of CUDA to target, for example [11.0
]. -
CUDNN_VERSION
: The version of cuDNN to target, for example [8.0
]. -
NVCR_SUFFIX
: Optional nvcr/cuda image suffix. Set to "-rc" for CUDA11 RC builds until general availability. Blank by default. -
PROTOBUF_VERSION
: The version of Protobuf to use, for example [3.0.0
]. Note: Changing this will not configure CMake to use a system version of Protobuf, it will configure CMake to download and try building that version. -
CMAKE_TOOLCHAIN_FILE
: The path to a toolchain file for cross compilation. -
BUILD_PARSERS
: Specify if the parsers should be built, for example [ON
] |OFF
. If turned OFF, CMake will try to find precompiled versions of the parser libraries to use in compiling samples. First in${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
.
Other build options with limited applicability:
-
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"
- NVidia A100:
-
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
- The default CUDA version used by CMake is 11.0. To override this, for example to 10.2, append
whl files for the TensorRT python API are in the python
directory of the TensorRT release
Example install for python3:
pip3 install $TRT_RELEASE/python/tensorrt-7.1.3.4-cp36-none-linux_x86_64.whl
- See Release Notes.