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NVIDIA/TensorRT: NVIDIA® TensorRT™ is an SDK for high-performance deep learning inference on NVIDIA GPUs. This repository contains the open source components of TensorRT.

This repository contains the Open Source Software (OSS) components of NVIDIA TensorRT. It includes the sources for TensorRT plugins and ONNX parser, 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.

• For code contributions to TensorRT-OSS, please see our Contribution Guide and Coding Guidelines.
• For a summary of new additions and updates shipped with TensorRT-OSS releases, please refer to the Changelog.
• For business inquiries, please contact researchinquiries@nvidia.com
• For press and other inquiries, please contact Hector Marinez at hmarinez@nvidia.com

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.

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

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

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

TensorRT GA build

• TensorRT v10.13.0.35
  • Available from direct download links listed below

System Packages

• CUDA
  • Recommended versions:
  • cuda-12.9.0
  • cuda-11.8.0
• CUDNN (optional)
  • cuDNN 8.9
• GNU make >= v4.1
• cmake >= v3.13
• python >= v3.8, <= v3.10.x
• pip >= v19.0
• Essential utilities
  • git, pkg-config, wget

Optional Packages

• Containerized build

  • Docker >= 19.03
  • NVIDIA Container Toolkit

• PyPI packages (for demo applications/tests)

  • onnx
  • onnxruntime
  • tensorflow-gpu >= 2.5.1
  • Pillow >= 9.0.1
  • pycuda < 2021.1
  • numpy
  • pytest

• Code formatting tools (for contributors)

  • Clang-format
  • Git-clang-format

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

1. git clone -b main 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 with the direct links below:

   • TensorRT 10.13.0.35 for CUDA 11.8, Linux x86_64
   • TensorRT 10.13.0.35 for CUDA 12.9, Linux x86_64
   • TensorRT 10.13.0.35 for CUDA 11.8, Windows x86_64
   • TensorRT 10.13.0.35 for CUDA 12.9, Windows x86_64

   Example: Ubuntu 20.04 on x86-64 with cuda-12.9

   cd ~/Downloads
   tar -xvzf TensorRT-10.13.0.35.Linux.x86_64-gnu.cuda-12.9.tar.gz
   export TRT_LIBPATH=`pwd`/TensorRT-10.13.0.35

   Example: Windows on x86-64 with cuda-12.9

   Expand-Archive -Path TensorRT-10.13.0.35.Windows.win10.cuda-12.9.zip
   $env:TRT_LIBPATH="$pwd\TensorRT-10.13.0.35\lib"

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.

   Example: Ubuntu 20.04 on x86-64 with cuda-12.9 (default)

   ./docker/build.sh --file docker/ubuntu-20.04.Dockerfile --tag tensorrt-ubuntu20.04-cuda12.9

   Example: Rockylinux8 on x86-64 with cuda-12.9

   ./docker/build.sh --file docker/rockylinux8.Dockerfile --tag tensorrt-rockylinux8-cuda12.9

   Example: Ubuntu 22.04 cross-compile for Jetson (aarch64) with cuda-12.9 (JetPack SDK)

   ./docker/build.sh --file docker/ubuntu-cross-aarch64.Dockerfile --tag tensorrt-jetpack-cuda12.9

   Example: Ubuntu 22.04 on aarch64 with cuda-12.9

   ./docker/build.sh --file docker/ubuntu-22.04-aarch64.Dockerfile --tag tensorrt-aarch64-ubuntu22.04-cuda12.9

2. Launch the TensorRT-OSS build container.

   Example: Ubuntu 20.04 build container

   ./docker/launch.sh --tag tensorrt-ubuntu20.04-cuda12.9 --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.

• Generate Makefiles and build

  Example: Linux (x86-64) build with default cuda-12.9

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

  Example: Linux (aarch64) build with default cuda-12.9

  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-12.9

  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=12.9
  CC=/usr/bin/gcc make -j$(nproc)

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

  Example: Ubuntu 22.04 Cross-Compile for Jetson (aarch64) with cuda-12.9 (JetPack)

  cd $TRT_OSSPATH
  mkdir -p build && cd build
  cmake .. -DCMAKE_TOOLCHAIN_FILE=$TRT_OSSPATH/cmake/toolchains/cmake_aarch64.toolchain -DCUDA_VERSION=12.9 -DCUDNN_LIB=/pdk_files/cudnn/usr/lib/aarch64-linux-gnu/libcudnn.so -DCUBLAS_LIB=/usr/local/cuda-12.9/targets/aarch64-linux/lib/stubs/libcublas.so -DCUBLASLT_LIB=/usr/local/cuda-12.9/targets/aarch64-linux/lib/stubs/libcublasLt.so -DTRT_LIB_DIR=/pdk_files/tensorrt/lib
  make -j$(nproc)

  Example: Native builds on Windows (x86) with cuda-12.9

  cd $TRT_OSSPATH
  mkdir -p build
  cd -p build
  cmake .. -DTRT_LIB_DIR="$env:TRT_LIBPATH" -DCUDNN_ROOT_DIR="$env:CUDNN_PATH" -DTRT_OUT_DIR="$pwd\\out"
  msbuild TensorRT.sln /property:Configuration=Release -m:$env:NUMBER_OF_PROCESSORS

  NOTE: The default CUDA version used by CMake is 12.9.0. To override this, for example to 11.8, append -DCUDA_VERSION=11.8 to the cmake command.

• 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_VERSION: 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). Currently supported options: x86_64 (default).

• TensorRT Developer Home
• TensorRT QuickStart Guide
• TensorRT Developer Guide
• TensorRT Sample Support Guide
• TensorRT ONNX Tools
• TensorRT Discussion Forums
• TensorRT Release Notes

• Please refer to TensorRT Release Notes

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