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MARD1NO / TensorRT

TensorRT is a C++ library for high performance inference on NVIDIA GPUs and deep learning accelerators.

Home Page:https://developer.nvidia.com/tensorrt

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License Documentation

TensorRT Open Source Software

This repository contains the Open Source Software (OSS) components of NVIDIA TensorRT. It includes the sources for TensorRT plugins and parsers (Caffe and ONNX), as well as sample applications demonstrating usage and capabilities of the TensorRT platform. These open source software components are a subset of the TensorRT General Availability (GA) release with some extensions and bug-fixes.

Need enterprise support? NVIDIA global support is available for TensorRT with the NVIDIA AI Enterprise software suite. Check out NVIDIA LaunchPad for free access to a set of hands-on labs with TensorRT hosted on NVIDIA infrastructure.

Join the TensorRT and Triton community and stay current on the latest product updates, bug fixes, content, best practices, and more.

Prebuilt TensorRT Python Package

We provide the TensorRT Python package for an easy installation.
To install:

pip install tensorrt

You can skip the Build section to enjoy TensorRT with Python.

Build

Prerequisites

To build the TensorRT-OSS components, you will first need the following software packages.

TensorRT GA build

System Packages

Optional Packages

Downloading TensorRT Build

  1. Download TensorRT OSS

    git clone -b master https://github.com/nvidia/TensorRT TensorRT
cd TensorRT
git submodule update --init --recursive

  2. (Optional - if not using TensorRT container) Specify the TensorRT GA release build path

    If using the TensorRT OSS build container, TensorRT libraries are preinstalled under /usr/lib/x86_64-linux-gnu and you may skip this step.

    Else download and extract the TensorRT GA build from NVIDIA Developer Zone.

    Example: Ubuntu 20.04 on x86-64 with cuda-11.8.0

    cd ~/Downloads
tar -xvzf TensorRT-8.5.1.7.Linux.x86_64-gnu.cuda-11.8.cudnn8.6.tar.gz
export TRT_LIBPATH=`pwd`/TensorRT-8.5.1.7

  3. (Optional - for Jetson builds only) Download the JetPack SDK

    1. Download and launch the JetPack SDK manager. Login with your NVIDIA developer account.
    2. Select the platform and target OS (example: Jetson AGX Xavier, Linux Jetpack 5.0), and click Continue.
    3. Under Download & Install Options change the download folder and select Download now, Install later. Agree to the license terms and click Continue.
    4. Move the extracted files into the <TensorRT-OSS>/docker/jetpack_files folder.

Setting Up The Build Environment

For Linux platforms, we recommend that you generate a docker container for building TensorRT OSS as described below. For native builds, please install the prerequisite System Packages.

  1. Generate the TensorRT-OSS build container.

    The TensorRT-OSS build container can be generated using the supplied Dockerfiles and build scripts. The build containers are configured for building TensorRT OSS out-of-the-box.

    Example: Ubuntu 20.04 on x86-64 with cuda-11.8.0 (default)

    ./docker/build.sh --file docker/ubuntu-20.04.Dockerfile --tag tensorrt-ubuntu20.04-cuda11.8

    Example: CentOS/RedHat 7 on x86-64 with cuda-10.2

    ./docker/build.sh --file docker/centos-7.Dockerfile --tag tensorrt-centos7-cuda10.2 --cuda 10.2

    Example: Ubuntu 20.04 cross-compile for Jetson (aarch64) with cuda-11.4.2 (JetPack SDK)

    ./docker/build.sh --file docker/ubuntu-cross-aarch64.Dockerfile --tag tensorrt-jetpack-cuda11.4

    Example: Ubuntu 20.04 on aarch64 with cuda-11.4.2

    ./docker/build.sh --file docker/ubuntu-20.04-aarch64.Dockerfile --tag tensorrt-aarch64-ubuntu20.04-cuda11.4

  2. Launch the TensorRT-OSS build container.

    Example: Ubuntu 20.04 build container

    ./docker/launch.sh --tag tensorrt-ubuntu20.04-cuda11.8 --gpus all

    NOTE:
    1. Use the --tag corresponding to build container generated in Step 1.
    2. NVIDIA Container Toolkit is required for GPU access (running TensorRT applications) inside the build container.
    3. sudo password for Ubuntu build containers is 'nvidia'.
    4. Specify port number using --jupyter <port> for launching Jupyter notebooks.

Building TensorRT-OSS

  • Generate Makefiles and build.

    Example: Linux (x86-64) build with default cuda-11.8.0

     cd $TRT_OSSPATH
 mkdir -p build && cd build
 cmake .. -DTRT_LIB_DIR=$TRT_LIBPATH -DTRT_OUT_DIR=`pwd`/out
 make -j$(nproc)

    NOTE: On CentOS7, the default g++ version does not support C++14. For native builds (not using the CentOS7 build container), first install devtoolset-8 to obtain the updated g++ toolchain as follows:

    yum -y install centos-release-scl
yum-config-manager --enable rhel-server-rhscl-7-rpms
yum -y install devtoolset-8
export PATH="/opt/rh/devtoolset-8/root/bin:${PATH}

    Example: Linux (aarch64) build with default cuda-11.8.0

     cd $TRT_OSSPATH
 mkdir -p build && cd build
 cmake .. -DTRT_LIB_DIR=$TRT_LIBPATH -DTRT_OUT_DIR=`pwd`/out -DCMAKE_TOOLCHAIN_FILE=$TRT_OSSPATH/cmake/toolchains/cmake_aarch64-native.toolchain
 make -j$(nproc)

    Example: Native build on Jetson (aarch64) with cuda-11.4

    cd $TRT_OSSPATH
mkdir -p build && cd build
cmake .. -DTRT_LIB_DIR=$TRT_LIBPATH -DTRT_OUT_DIR=`pwd`/out -DTRT_PLATFORM_ID=aarch64 -DCUDA_VERSION=11.4
CC=/usr/bin/gcc make -j$(nproc)

    NOTE: C compiler must be explicitly specified via CC= for native aarch64 builds of protobuf.

    Example: Ubuntu 20.04 Cross-Compile for Jetson (aarch64) with cuda-11.4 (JetPack)

    cd $TRT_OSSPATH
mkdir -p build && cd build
cmake .. -DCMAKE_TOOLCHAIN_FILE=$TRT_OSSPATH/cmake/toolchains/cmake_aarch64.toolchain -DCUDA_VERSION=11.4 -DCUDNN_LIB=/pdk_files/cudnn/usr/lib/aarch64-linux-gnu/libcudnn.so -DCUBLAS_LIB=/usr/local/cuda-11.4/targets/aarch64-linux/lib/stubs/libcublas.so -DCUBLASLT_LIB=/usr/local/cuda-11.4/targets/aarch64-linux/lib/stubs/libcublasLt.so -DTRT_LIB_DIR=/pdk_files/tensorrt/lib

make -j$(nproc)

    NOTE: The latest JetPack SDK v5.0 only supports TensorRT 8.4.0.

    NOTE:
    1. The default CUDA version used by CMake is 11.8.0. To override this, for example to 10.2, append -DCUDA_VERSION=10.2 to the cmake command.
    2. 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.8

  • Required CMake build 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.

  • Optional CMake build arguments:

    • 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 [11.7.1].
    • CUDNN_VERSION: The version of cuDNN to target, for example [8.6].
    • 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.
    • 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

References

TensorRT Resources

Known Issues

About

TensorRT is a C++ library for high performance inference on NVIDIA GPUs and deep learning accelerators.

https://developer.nvidia.com/tensorrt

License:Apache License 2.0

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