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https://github.com/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.
https://github.com/NVIDIA/TensorRT

deep-learning gpu-acceleration inference nvidia tensorrt

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

Lists

README 

        
[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0) [![Documentation](https://img.shields.io/badge/TensorRT-documentation-brightgreen.svg)](https://docs.nvidia.com/deeplearning/sdk/tensorrt-developer-guide/index.html)



# 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.



* For code contributions to TensorRT-OSS, please see our [Contribution Guide](CONTRIBUTING.md) and [Coding Guidelines](CODING-GUIDELINES.md).

* For a summary of new additions and updates shipped with TensorRT-OSS releases, please refer to the [Changelog](CHANGELOG.md).

* For business inquiries, please contact [[email protected]](mailto:[email protected])

* For press and other inquiries, please contact Hector Marinez at [[email protected]](mailto:[email protected])



Need enterprise support? NVIDIA global support is available for TensorRT with the [NVIDIA AI Enterprise software suite](https://www.nvidia.com/en-us/data-center/products/ai-enterprise/). Check out [NVIDIA LaunchPad](https://www.nvidia.com/en-us/launchpad/ai/ai-enterprise/) for free access to a set of hands-on labs with TensorRT hosted on NVIDIA infrastructure.



Join the [TensorRT and Triton community](https://www.nvidia.com/en-us/deep-learning-ai/triton-tensorrt-newsletter/) 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:

```bash

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](https://developer.nvidia.com/nvidia-tensorrt-download) v8.6.1.6



**System Packages**

* [CUDA](https://developer.nvidia.com/cuda-toolkit)

  * Recommended versions:

  * cuda-12.2.0 + cuDNN-8.8

  * cuda-11.8.0 + cuDNN-8.8

* [GNU make](https://ftp.gnu.org/gnu/make/) >= v4.1

* [cmake](https://github.com/Kitware/CMake/releases) >= v3.13

* [python]() >= v3.6.9, <= v3.10.x

* [pip](https://pypi.org/project/pip/#history) >= v19.0

* Essential utilities

  * [git](https://git-scm.com/downloads), [pkg-config](https://www.freedesktop.org/wiki/Software/pkg-config/), [wget](https://www.gnu.org/software/wget/faq.html#download)



**Optional Packages**

* Containerized build

  * [Docker](https://docs.docker.com/install/) >= 19.03

  * [NVIDIA Container Toolkit](https://github.com/NVIDIA/nvidia-docker)

* Toolchains and SDKs

  * (Cross compilation for Jetson platform) [NVIDIA JetPack](https://developer.nvidia.com/embedded/jetpack) >= 5.0 (current support only for TensorRT 8.4.0 and TensorRT 8.5.2)

  * (Cross compilation for QNX platform) [QNX Toolchain](https://blackberry.qnx.com/en)

* PyPI packages (for demo applications/tests)

  * [onnx](https://pypi.org/project/onnx/)

  * [onnxruntime](https://pypi.org/project/onnxruntime/)

  * [tensorflow-gpu](https://pypi.org/project/tensorflow/) >= 2.5.1

  * [Pillow](https://pypi.org/project/Pillow/) >= 9.0.1

  * [pycuda](https://pypi.org/project/pycuda/) < 2021.1

  * [numpy](https://pypi.org/project/numpy/)

  * [pytest](https://pypi.org/project/pytest/)

* Code formatting tools (for contributors)

  * [Clang-format](https://clang.llvm.org/docs/ClangFormat.html)

  * [Git-clang-format](https://github.com/llvm-mirror/clang/blob/master/tools/clang-format/git-clang-format)



  > NOTE: [onnx-tensorrt](https://github.com/onnx/onnx-tensorrt), [cub](http://nvlabs.github.io/cub/), and [protobuf](https://github.com/protocolbuffers/protobuf.git) packages are downloaded along with TensorRT OSS, and not required to be installed.



## Downloading TensorRT Build



1. #### Download TensorRT OSS

	```bash

	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 pre-installed 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](https://developer.nvidia.com/nvidia-tensorrt-download).



    **Example: Ubuntu 20.04 on x86-64 with cuda-12.0**



    ```bash

    cd ~/Downloads

    tar -xvzf TensorRT-8.6.1.6.Linux.x86_64-gnu.cuda-12.0.tar.gz

    export TRT_LIBPATH=`pwd`/TensorRT-8.6.1.6

    ```



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 `/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](#prerequisites) *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-12.1 (default)**

    ```bash

    ./docker/build.sh --file docker/ubuntu-20.04.Dockerfile --tag tensorrt-ubuntu20.04-cuda12.1

    ```

    **Example: CentOS/RedHat 7 on x86-64 with cuda-11.8**

    ```bash

    ./docker/build.sh --file docker/centos-7.Dockerfile --tag tensorrt-centos7-cuda11.8 --cuda 11.8.0

    ```

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

    ```bash

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

    ```

    **Example: Ubuntu 20.04 on aarch64 with cuda-11.8**

    ```bash

    ./docker/build.sh --file docker/ubuntu-20.04-aarch64.Dockerfile --tag tensorrt-aarch64-ubuntu20.04-cuda11.8 --cuda 11.8.0

    ```



2. #### Launch the TensorRT-OSS build container.

    **Example: Ubuntu 20.04 build container**

	```bash

	./docker/launch.sh --tag tensorrt-ubuntu20.04-cuda12.1 --gpus all

	```

	> NOTE:

  
 1. Use the `--tag` corresponding to build container generated in Step 1.

  
 2. [NVIDIA Container Toolkit](#prerequisites) 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 ` for launching Jupyter notebooks.



## Building TensorRT-OSS

* Generate Makefiles and build.



    **Example: Linux (x86-64) build with default cuda-12.1**

	```bash

	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:

    ```bash

    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-12.1**

	```bash

	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**

    ```bash

    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)**

    ```bash

    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 v5.1 only supports TensorRT 8.5.2.



	> NOTE:

	
 1. The default CUDA version used by CMake is 11.4.1. To override this, for example to 11.8, append `-DCUDA_VERSION=11.8` 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_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](https://developer.nvidia.com/cuda-gpus). 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



* [TensorRT Developer Home](https://developer.nvidia.com/tensorrt)

* [TensorRT QuickStart Guide](https://docs.nvidia.com/deeplearning/tensorrt/quick-start-guide/index.html)

* [TensorRT Developer Guide](https://docs.nvidia.com/deeplearning/tensorrt/developer-guide/index.html)

* [TensorRT Sample Support Guide](https://docs.nvidia.com/deeplearning/tensorrt/sample-support-guide/index.html)

* [TensorRT ONNX Tools](https://docs.nvidia.com/deeplearning/tensorrt/index.html#tools)

* [TensorRT Discussion Forums](https://devtalk.nvidia.com/default/board/304/tensorrt/)

* [TensorRT Release Notes](https://docs.nvidia.com/deeplearning/tensorrt/release-notes/index.html)



## Known Issues



* Please refer to [TensorRT 8.6 Release Notes](https://docs.nvidia.com/deeplearning/tensorrt/release-notes/tensorrt-8.html#tensorrt-8)