TensorRT Open Source Software

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.

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

TensorRT v10.0.0.6
- Available from direct download links listed below

System Packages

CUDA
- Recommended versions:
- cuda-12.2.0 + cuDNN-8.9
- cuda-11.8.0 + 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.

Downloading TensorRT Build

Download TensorRT OSS

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

(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 10.0.0.6 for CUDA 11.8, Linux x86_64
- TensorRT 10.0.0.6 for CUDA 12.4, Linux x86_64
Example: Ubuntu 20.04 on x86-64 with cuda-12.4
```
cd ~/Downloads
tar -xvzf TensorRT-10.0.0.6.Linux.x86_64-gnu.cuda-12.4.tar.gz
export TRT_LIBPATH=`pwd`/TensorRT-10.0.0.6
```

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.

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-12.3.2 (default)
```
./docker/build.sh --file docker/ubuntu-20.04.Dockerfile --tag tensorrt-ubuntu20.04-cuda12.3.2
```
Launch the TensorRT-OSS build container.

Example: Ubuntu 20.04 build container
```
./docker/launch.sh --tag tensorrt-ubuntu20.04-cuda12.3.2 --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-12.3.2
```
 cd $TRT_OSSPATH
 mkdir -p build && cd build
 cmake .. -DTRT_LIB_DIR=$TRT_LIBPATH -DTRT_OUT_DIR=`pwd`/out
 make -j$(nproc)
```
NOTE:
1. The default CUDA version used by CMake is 12.2.0. 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).