## Abstract

This TensorRT 8.0.0 Early Access (EA) Installation Guide provides the installation requirements, a list of what is included in the TensorRT package, and step-by-step instructions for installing TensorRT.

For previously released TensorRT installation documentation, see TensorRT Archives.

## 1. Overview

The core of NVIDIA® TensorRT™ is a C++ library that facilitates high-performance inference on NVIDIA graphics processing units (GPUs). TensorRT takes a trained network, which consists of a network definition and a set of trained parameters, and produces a highly optimized runtime engine that performs inference for that network.

TensorRT provides API's via C++ and Python that help to express deep learning models via the Network Definition API or load a pre-defined model via the parsers that allow TensorRT to optimize and run them on an NVIDIA GPU. TensorRT applies graph optimizations, layer fusion, among other optimizations, while also finding the fastest implementation of that model leveraging a diverse collection of highly optimized kernels. TensorRT also supplies a runtime that you can use to execute this network on all of NVIDIA’s GPU’s from the Kepler generation onwards.

TensorRT also includes optional high speed mixed precision capabilities introduced in the Tegra™ X1, and extended with the Pascal™, Volta™, Turing™, and NVIDIA® Ampere GPU architectures.

## 2. Getting Started

Ensure you are familiar with the following installation requirements and notes.
• The Windows zip package for TensorRT does not provide Python support. Python may be supported in the future.
• If you are using the TensorRT Python API and PyCUDA isn’t already installed on your system, see Installing PyCUDA. If you encounter any issues with PyCUDA usage, you may need to recompile it yourself. For more information, see Installing PyCUDA on Linux.
• Ensure you are familiar with the Release Notes. The current version of the release notes can be found online at TensorRT Release Notes.
• Verify that you have the CUDA Toolkit installed; versions 10.2, 11.0 update 1, 11.1 update 1, 11.2 update 2, and 11.3 are supported.
• The TensorFlow to TensorRT model export requires TensorFlow 1.15.5.
• The PyTorch examples have been tested with PyTorch 1.8.0, but may work with older versions.
• The TensorRT ONNX parser has been tested with ONNX 1.8.0 and supports opset 11.
• If the target system has both TensorRT and one or more training frameworks installed on it, the simplest strategy is to use the same version of cuDNN for the training frameworks as the one that TensorRT ships with. If this is not possible, or for some reason strongly undesirable, be careful to properly manage the side-by-side installation of cuDNN on the single system. In some cases, depending on the training framework being used, this may not be possible without patching the training framework sources.
• The libnvcaffe_parser.so library functionality from previous versions is included in libnvparsers.so since TensorRT 5.0. The installed symbolic link for libnvcaffe_parser.so is updated to point to the new libnvparsers.so library. The static library libnvcaffe_parser.a is also symbolically linked to libnvparsers_static.a.
• The installation instructions below assume you want the full TensorRT; both the C++ and TensorRT Python APIs. In some environments and use cases, you may not want to install the Python functionality. In which case, simply don’t install the Debian or RPM packages labeled Python or the whl files. None of the C++ API functionality depends on Python. You would need to install the UFF whl file if you want to export UFF files from TensorFlow models.

Ensure you are a member of the NVIDIA Developer Program. If not, follow the prompts to gain access.

## Procedure

2. Select the version of TensorRT that you are interested in.
3. Select the check-box to agree to the license terms.

## 4. Installing TensorRT

You can choose between the following installation options when installing TensorRT; Debian or RPM packages, a pip wheel file, a tar file, or a zip file.

The Debian and RPM installations automatically install any dependencies, however, it:
• requires sudo or root privileges to install
• provides no flexibility as to which location TensorRT is installed into
• requires that the CUDA Toolkit and cuDNN have also been installed using Debian or RPM packages.
• does not allow more than one minor version of TensorRT to be installed at the same time

The tar file provides more flexibility, such as installing multiple versions of TensorRT at the same time. However, you need to ensure that you have the necessary dependencies already installed and you must manage LD_LIBRARY_PATH yourself. For more information, see Tar File Installation.

The zip file is the only option currently for Windows. It does not support any other platforms besides Windows. Ensure that you have the necessary dependencies already installed. For more information, see Zip File Installation.

TensorRT versions:TensorRT is a product made up of separately versioned components. The version on the product conveys important information about the significance of new features while the library version conveys information about the compatibility or incompatibility of the API. The following table shows the versioning of the TensorRT components.

Table 1. Versioning of TensorRT components
Product or Component Previously Released Version Current Version Version Description
TensorRT product 7.2.3 8.0.0

+1.0 when significant new capabilities are added.

+0.1 when capabilities have been improved.

nvinfer libraries, headers, samples, and documentation. 7.2.3 8.0.0

+1.0 when the API or ABI changes in a non-compatible way.

+0.1 when the API or ABI changes are backward compatible

UFF uff-converter-tf Debian and RPM packages 7.2.3 8.0.0

+0.1 while we are developing the core functionality.

Set to 1.0 when we have all base functionality in place.

uff-*.whl file 0.6.8 0.6.9
graphsurgeon graphsurgeon-tf Debian and RPM packages 7.2.3 8.0.0

+0.1 while we are developing the core functionality.

Set to 1.0 when we have all base functionality in place.

graphsurgeon-*.whl file 0.4.4 0.4.5
onnx-graphsurgeon onnx-graphsurgeon Debian and RPM packages 7.2.3 8.0.0

+0.1 while we are developing the core functionality.

Set to 1.0 when we have all base functionality in place.

onnx_graphsurgeon-*.whl file 0.2.6 0.2.6
libnvinfer python packages1
• python-libnvinfer
• python-libnvinfer-dev
• python3-libnvinfer
• python3-libnvinfer-dev
Debian and RPM packages
7.2.3 8.0.0

+1.0 when the API or ABI changes in a non-compatible way.

+0.1 when the API or ABI changes are backward compatible.

tensorrt.whl file 7.2.3 8.0.0

### 4.1. Debian Installation

This section contains instructions for a developer installation. This installation method is for new users or users who want the complete developer installation, including samples and documentation for both the C++ and Python APIs.

For advanced users who are already familiar with TensorRT and want to get their application running quickly, are using an NVIDIA CUDA container with cuDNN included, or want to setup automation, follow the network repo installation instructions (see Using The NVIDIA CUDA Network Repo For Debian Installation).

Attention: TensorRT requires compatibility libraries from the same CUDA version that TensorRT was built with for proper functionality when using either CUDA 11.1 or CUDA 11.2. If you used a CUDA local repo to install CUDA and you are targeting either CUDA 11.1 or CUDA 11.2, then you must also install the CUDA local repo matching the CUDA version that was used to build TensorRT.
Note: The following commands are examples for amd64, however, the commands are identical for ppc64el and arm64.

### Procedure

1. Download the TensorRT local repo file that matches the Ubuntu version and CPU architecture that you are using.
2. Install TensorRT from the Debian local repo package. Replace ubuntuxx04, cudax.x, trt8.x.x.x-ea and yyyymmdd with your specific OS version, CUDA version, TensorRT version and package date.
os="ubuntuxx04"
tag="cudax.x-trt8.x.x.x-ea-yyyymmdd"
sudo dpkg -i nv-tensorrt-repo-${os}-${tag}_1-1_amd64.deb
sudo apt-key add /var/nv-tensorrt-repo-${tag}/7fa2af80.pub sudo apt-get update sudo apt-get install tensorrt  If using Python 3.x: sudo apt-get install python3-libnvinfer-dev The following additional packages will be installed: python3-libnvinfer If you would like to run the samples that require ONNX graphsurgeon or use the Python module for your own project, run: sudo apt-get install onnx-graphsurgeon 3. Verify the installation. dpkg -l | grep TensorRT You should see something similar to the following: ii graphsurgeon-tf 8.0.0-1+cuda11.3 amd64 GraphSurgeon for TensorRT package ii libnvinfer-bin 8.0.0-1+cuda11.3 amd64 TensorRT binaries ii libnvinfer-dev 8.0.0-1+cuda11.3 amd64 TensorRT development libraries and headers ii libnvinfer-doc 8.0.0-1+cuda11.3 all TensorRT documentation ii libnvinfer-plugin-dev 8.0.0-1+cuda11.3 amd64 TensorRT plugin libraries ii libnvinfer-plugin8 8.0.0-1+cuda11.3 amd64 TensorRT plugin libraries ii libnvinfer-samples 8.0.0-1+cuda11.3 all TensorRT samples ii libnvinfer8 8.0.0-1+cuda11.3 amd64 TensorRT runtime libraries ii libnvonnxparsers-dev 8.0.0-1+cuda11.3 amd64 TensorRT ONNX libraries ii libnvonnxparsers8 8.0.0-1+cuda11.3 amd64 TensorRT ONNX libraries ii libnvparsers-dev 8.0.0-1+cuda11.3 amd64 TensorRT parsers libraries ii libnvparsers8 8.0.0-1+cuda11.3 amd64 TensorRT parsers libraries ii python3-libnvinfer 8.0.0-1+cuda11.3 amd64 Python 3 bindings for TensorRT ii python3-libnvinfer-dev 8.0.0-1+cuda11.3 amd64 Python 3 development package for TensorRT ii tensorrt 8.0.0.x-1+cuda11.3 amd64 Meta package of TensorRT ii uff-converter-tf 8.0.0-1+cuda11.3 amd64 UFF converter for TensorRT package ii onnx-graphsurgeon 8.0.0-1+cuda11.3 amd64 ONNX GraphSurgeon for TensorRT package ### 4.1.1. Using The NVIDIA CUDA Network Repo For Debian Installation This installation method is for advanced users who are already familiar with TensorRT and want to get their application running quickly or to setup automation, such as when using containers. New users or users who want the complete installation, including samples and documentation, should follow the local repo installation instructions (see Debian Installation). #### About this task Note: If you are using an NVIDIA CUDA container with cuDNN included, then the NVIDIA CUDA network repository will already be set up and you can skip step 1. #### Procedure 1. To install the NVIDIA CUDA network repository, follow the instructions at the CUDA Toolkit Download page. 1. Select the Linux operating system. 2. Select the desired architecture. 3. Select the Ubuntu distribution. 4. Select the desired Ubuntu version. 5. Select the “deb (network)” installer type. 6. Enter the commands provided into your terminal. You can omit the final apt-get install command if you do not require the entire CUDA toolkit. While installing TensorRT, apt downloads the required CUDA and cuDNN dependencies for you automatically. 2. Install the TensorRT package that fits your particular needs. 1. For only running TensorRT C++ applications: sudo apt-get install libnvinfer8 libnvonnxparsers8 libnvparsers8 libnvinfer-plugin8 2. For also building TensorRT C++ applications: sudo apt-get install libnvinfer-dev libnvonnxparsers-dev libnvparsers-dev libnvinfer-plugin-dev 3. For running TensorRT Python applications: sudo apt-get install python3-libnvinfer 3. When using the NVIDIA CUDA network repository, Ubuntu will by default install TensorRT for the latest CUDA version. The following commands will install libnvinfer8 for an older CUDA version and hold the libnvinfer8 package at this version. Replace 8.x.x with your version of TensorRT and cudax.x with your CUDA version for your install. version="8.x.x-1+cudax.x" sudo apt-get install libnvinfer8=${version} libnvonnxparsers8=${version} libnvparsers8=${version} libnvinfer-plugin8=${version} libnvinfer-dev=${version} libnvonnxparsers-dev=${version} libnvparsers-dev=${version} libnvinfer-plugin-dev=${version} python3-libnvinfer=${version}

sudo apt-mark hold libnvinfer8 libnvonnxparsers8 libnvparsers8 libnvinfer-plugin8 libnvinfer-dev libnvonnxparsers-dev libnvparsers-dev libnvinfer-plugin-dev python3-libnvinfer
If you want to upgrade to the latest version of TensorRT or the latest version of CUDA, then you can unhold the libnvinfer8 package using the following command.
sudo apt-mark unhold libnvinfer8 libnvonnxparsers8 libnvparsers8 libnvinfer-plugin8 libnvinfer-dev libnvonnxparsers-dev libnvparsers-dev libnvinfer-plugin-dev python3-libnvinfer

You may need to repeat these steps for libcudnn8 to prevent cuDNN from being updated to the latest CUDA version. Refer to the TensorRT Release Notes for the specific version of cuDNN that was tested with your version of TensorRT. Example commands for downgrading and holding the cuDNN version can be found in Upgrading TensorRT. See the cuDNN Installation Guide for additional information.

If the NVIDIA CUDA network repository and a TensorRT local repository are enabled at the same time you may observe package conflicts with either TensorRT or cuDNN. You will need to configure APT so that it prefers local packages over network packages. You can do this by creating a new file at /etc/apt/preferences.d/local-repo with the following lines:
Package: *
Pin: origin ""
Pin-Priority: 1001

Note: This preference change will affect more than just TensorRT in the unlikely event that you have other repositories which are also not downloaded over HTTP(S). To revert APT to its original behavior simply remove the newly created file.

### 4.2. App Server Installation

This type of installation is for cloud users or container users who will be going to production.

If you are going to be deploying the application to a server and running an already existing application in a minimal or standalone environment, then this type of installation allows you to set up a runtime environment instead of a full development environment. It provides a simple list of packages you can install if you want to run an application you've already developed.

When setting up servers which will host TensorRT powered applications, you can simply install any of the following Debian packages using apt-get:
• the libnvinfer8 package (C++) plus any additional library packages you require, or
• the python3-libnvinfer package (Python 3.x).

### 4.3. RPM Installation

This section contains instructions for installing TensorRT from an RPM package. This installation method is for new users or users who want the complete installation, including samples and documentation.

For advanced users who are already familiar with TensorRT and want to get their application running quickly or to setup automation, follow the network repo installation instructions (see Using The NVIDIA CUDA Network Repo For RPM Installation).

### Before you begin

Note:
• Before issuing the following commands, you'll need to replace cudax.x, trt8.x.x.x-ea, and yyyymmdd with your specific CUDA version, TensorRT version, and package date.
• The following example commands are for x86_64, but the commands should be identical for ppc64le.
Attention: If you used a CUDA local repo to install CUDA and you are targeting CUDA 11.2, then you must also install the CUDA 11.1 local repo.

### Procedure

1. Download the TensorRT local repo file that matches the RHEL/CentOS version and CPU architecture you are using.
2. Install TensorRT from the RPM local repo package.
os="rhelx"
tag="cudax.x-trt8.x.x.x-ea-yyyymmdd"
sudo rpm -Uvh nv-tensorrt-repo-${os}-${tag}-1-1.x86_64.rpm
sudo yum clean expire-cache

The packages which can be installed are:
graphsurgeon-tf.x86_64
libnvinfer-bin.x86_64
libnvinfer-devel.x86_64
libnvinfer-doc.x86_64
libnvinfer-plugin-devel.x86_64
libnvinfer-plugin8.x86_64
libnvinfer-samples.x86_64
libnvinfer8.x86_64
libnvonnxparsers-devel.x86_64
libnvonnxparsers8.x86_64
libnvparsers-devel.x86_64
libnvparsers8.x86_64
python3-libnvinfer.x86_64
python3-libnvinfer-devel.x86_64
tensorrt.x86_64
uff-converter-tf.x86_64
onnx-graphsurgeon.x86_64

Install TensorRT.
sudo yum install tensorrt
If using Python 3.x:
sudo yum install python3-libnvinfer-devel
The following additional packages will be installed:
python3-libnvinfer
If you would like to run the samples that require ONNX graphsurgeon or use the Python module for your own project, run:
sudo yum install onnx-graphsurgeon
3. Verify the installation.
1. Run:
rpm -qa | grep tensorrt
You should see something similar to the following:
tensorrt-8.0.0.x-1.cuda11.3.x86_64
2. Run:
rpm -qa | grep -e libnvinfer -e libnv.*parsers
You should see something similar to the following:
libnvinfer-doc-8.0.0-1.cuda11.3.x86_64
libnvinfer-plugin8-8.0.0-1.cuda11.3.x86_64
libnvinfer-devel-8.0.0-1.cuda11.3.x86_64
libnvinfer-bin-8.0.0-1.cuda11.3.x86_64
libnvinfer8-8.0.0-1.cuda11.3.x86_64
libnvinfer-samples-8.0.0-1.cuda11.3.x86_64
libnvinfer-plugin-devel-8.0.0-1.cuda11.3.x86_64
libnvonnxparsers8-8.0.0-1.cuda11.3.x86_64
libnvonnxparsers-devel-8.0.0-1.cuda11.3.x86_64
libnvparsers8-8.0.0-1.cuda11.3.x86_64
libnvparsers-devel-8.0.0-1.cuda11.3.x86_64
python3-libnvinfer-8.0.0-1.cuda11.3.x86_64
python3-libnvinfer-devel-8.0.0-1.cuda11.3.x86_64
3. Run:
rpm -qa | grep graphsurgeon-tf
You should see something similar to the following:
graphsurgeon-tf-8.0.0-1.cuda11.3.x86_64
4. Run:
rpm -qa | grep uff-converter-tf
You should see something similar to the following:
uff-converter-tf-8.0.0-1.cuda11.3.x86_64
5. Run:
rpm -qa | grep onnx-graphsurgeon
You should see something similar to the following:
onnx-graphsurgeon-8.0.0-1.cuda11.3.x86_64

### 4.3.1. Using The NVIDIA CUDA Network Repo For RPM Installation

This installation method is for advanced users who are already familiar with TensorRT and want to get their application running quickly or to setup automation. New users or users who want the complete installation, including samples and documentation, should follow the local repo installation instructions (see RPM Installation).

Note: If you are using an NVIDIA CUDA container with cuDNN included, then the NVIDIA CUDA network repository will already be set up and you can skip step 1.

#### Procedure

1. To install the NVIDIA CUDA network repository, follow the instructions at the CUDA Toolkit Download page.
1. Select the Linux operating system.
2. Select the desired architecture.
3. Select the CentOS or RHEL distribution.
4. Select the desired CentOS/RHEL version.
5. Select the “rpm (network)” installer type.
6. Enter the commands provided into your terminal.

You can omit the final yum/dnf install command if you do not require the entire CUDA toolkit. While installing TensorRT, yum/dnf downloads the required CUDA and cuDNN dependencies for you automatically.

2. Install the TensorRT package that fits your particular needs.
1. For only running TensorRT C++ applications:
sudo yum install libnvinfer8 libnvparsers8 libnvonnxparsers8 libnvinfer-plugin8
2. For also building TensorRT C++ applications:
sudo yum install libnvinfer-devel libnvparsers-devel libnvonnxparsers-devel libnvinfer-plugin-devel
3. For running TensorRT Python applications:
sudo yum install python3-libnvinfer
3. When using the NVIDIA CUDA network repository, RHEL will by default install TensorRT for the latest CUDA version. The following commands will install libnvinfer8 for an older CUDA version and hold the libnvinfer8 package at this version. Replace 8.x.x with your version of TensorRT and cudax.x with your CUDA version for your install.
version="8.x.x-1.cudax.x"
$pip install nvidia-tensorrt  ##################################################################  2. To verify that your installation is working, use the following Python commands to: • Import the tensorrt Python module. • Confirm that the correct version of TensorRT has been installed. • Create a Builder object to verify that your CUDA installation is working. python3 >>> import tensorrt >>> print(tensorrt.__version__) >>> assert tensorrt.Builder(tensorrt.Logger())  If the final Python command fails with an error message similar to the error message below, then you may not have the NVIDIA driver installed or the NVIDIA driver may not be working properly. If you are running inside a container, then try starting from one of the nvidia/cuda:x.y-base-<os> containers. [TensorRT] ERROR: CUDA initialization failure with error 100. Please check your CUDA installation: ... If the Python commands above worked, then you should now be able to run any of the TensorRT Python samples to further confirm that your TensorRT installation is working. For more information about TensorRT samples, refer to the Sample Support Guide. ### 4.5. Tar File Installation ### Procedure 1. Install the following dependencies, if not already present: If you choose to install CUDA 11.1 or CUDA 11.2, then you must also install the CUDA toolkit matching the CUDA version TensorRT was built with, which can be installed using a Debian/RPM package (local repo or network repo) or using a tar package and setting LD_LIBRARY_PATH to the appropriate location. 2. Download the TensorRT tar file that matches the Linux distribution you are using. 3. Choose where you want to install TensorRT. This tar file will install everything into a subdirectory called TensorRT-8.x.x.x. 4. Unpack the tar file. version="8.x.x.x" arch=$(uname -m)
cuda="cuda-x.x"
cudnn="cudnn8.x"
tar xzvf TensorRT-${version}.Linux.${arch}-gnu.${cuda}.${cudnn}.tar.gz
Where:
• 8.x.x.x is your TensorRT version
• cuda-x.x is CUDA version 10.2, 11.0, or 11.3
• cudnn8.x is cuDNN version 8.2
This directory will have sub-directories like lib, include, data, etc…
ls TensorRT-${version} bin data doc graphsurgeon include lib onnx_graphsurgeon python samples targets TensorRT-Release-Notes.pdf uff  5. Add the absolute path to the TensorRTlib directory to the environment variable LD_LIBRARY_PATH: export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:<TensorRT-${version}/lib> 6. Install the Python TensorRT wheel file. cd TensorRT-${version}/python

If using Python 3.x:
sudo pip3 install tensorrt-*-cp3x-none-linux_x86_64.whl
7. Install the Python UFF wheel file. This is only required if you plan to use TensorRT with TensorFlow.
cd TensorRT-${version}/uff If using Python 3.x: sudo pip3 install uff-0.6.9-py2.py3-none-any.whl Check the installation with: which convert-to-uff 8. Install the Python graphsurgeon wheel file. cd TensorRT-${version}/graphsurgeon

If using Python 3.x:
sudo pip3 install graphsurgeon-0.4.5-py2.py3-none-any.whl

9. Install the Python onnx-graphsurgeon wheel file.
cd TensorRT-${version}/onnx_graphsurgeon If using Python 3.x: sudo pip3 install onnx_graphsurgeon-0.2.6-py2.py3-none-any.whl 10. Verify the installation: 1. Ensure that the installed files are located in the correct directories. For example, run the tree -d command to check whether all supported installed files are in place in the lib, include, data, etc… directories. 2. Build and run one of the shipped samples, for example, sampleMNIST in the installed directory. You should be able to compile and execute the sample without additional settings. For more information, see the “Hello World” For TensorRT (sampleMNIST). 3. The Python samples are in the samples/python directory. ### 4.6. Zip File Installation This section contains instructions for installing TensorRT from a zip file. ### Before you begin Ensure that you have the following dependencies installed. If you choose to install CUDA 11.1 or CUDA 11.2, then you must also install the CUDA toolkit matching the CUDA version TensorRT was built with using a CUDA exe/msi package or using a zip package and setting PATH to the appropriate location. ### About this task This section contains instructions for installing TensorRT from a zip package on Windows 10. ### Procedure 1. Download the TensorRT zip file that matches the Windows version you are using. 2. Choose where you want to install TensorRT. The zip file will install everything into a subdirectory called TensorRT-8.x.x.x. This new subdirectory will be referred to as <installpath> in the steps below. 3. Unzip the TensorRT-8.x.x.x.Windows10.x86_64.cuda-x.x.cudnnx.x.zip file to the location that you chose. Replace: 1. 8.x.x.x with the TensorRT version 2. cuda-x.x with the CUDA version, and 3. cudnnx.x with the cuDNN version for your particular download. 4. Add the TensorRT library files to your system PATH. There are two ways to accomplish this task: 1. Leave the DLL files where they were unzipped and add <installpath>/lib to your system PATH. You can add a new path to your system PATH using the steps below. 1. Press the Windows key and search for "environment variables" which should present you with the option Edit the system environment variables and click it. 2. Click Environment Variables… at the bottom of the window. 3. Under System variables, select Path and click Edit…. 4. Click either New or Browse to add a new item that contains <installpath>/lib. 5. Continue to click OK until all the newly opened windows are closed. 6. If your cuDNN libraries were not copied to the CUDA installation directory and instead left where they were unzipped, then repeat the above steps for the cuDNNbin directory. 2. Copy the DLL files from <installpath>/lib to your CUDA installation directory, for example, C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\vX.Y\bin, where vX.Y is your CUDA version. The CUDA installer should have already added the CUDA path to your system PATH. 5. To verify that your installation is working you should open a Visual Studio Solution file from one of the samples, such as “Hello World” For TensorRT (sampleMNIST), and confirm that you are able to build and run the sample. If you want to use TensorRT in your own project, ensure that the following is present in your Visual Studio Solution project properties: 1. <installpath>/lib has been added to your PATH variable and is present under VC++ Directories > Executable Directories. 2. <installpath>/include is present under C/C++ > General > AdditionalDirectories. 3. nvinfer.lib and any other LIB files that your project requires are present under Linker > Input > Additional Dependencies. Note: In order to build the included samples, you should have Visual Studio 2017 (https://visualstudio.microsoft.com/downloads/) installed. The community edition is sufficient to build the TensorRT samples. 6. If you are using TensorFlow or PyTorch, install the uff, graphsurgeon, and onnx_graphsurgeon wheel packages. You must prepare the Python environment before installing uff, graphsurgeon or onnx_graphsurgeon. If using Python 3.x: python3 -m pip install <installpath>\graphsurgeon\graphsurgeon-0.4.5-py2.py3-none-any.whl python3 -m pip install <installpath>\uff\uff-0.6.9-py2.py3-none-any.whl python3 -m pip install <installpath>\onnx_graphsurgeon\onnx_graphsurgeon-0.2.6-py2.py3-none-any.whl  ### 4.7. Additional Installation Methods Aside from installing TensorRT from the product package, you can also install TensorRT from the following locations. TensorRT container The TensorRT container provides an easy method for deploying TensorRT with all necessary dependencies already packaged in the container. For information about installing TensorRT via a container, see the TensorRT Container Release Notes. JetPack JetPack bundles all Jetson platform software, including TensorRT. Use it to flash your Jetson Developer Kit with the latest OS image, install NVIDIA SDKs, and jump-start your development environment. For information about installing TensorRT through JetPack, see the JetPack documentation. For JetPack downloads, see Develop: Jetpack. NVIDIA DriveWorks With every release, TensorRT delivers features to make the DRIVE Development Platform an excellent computing platform for Autonomous Driving. For more information about installing TensorRT through DriveWorks, see the DriveWorks documentation. For DriveWorks downloads, see NVIDIA Developer: Drive Downloads. ## 5. Upgrading TensorRT Upgrading TensorRT to the latest version is only supported when the currently installed TensorRT version is equal to or newer than the last two public releases. For example, TensorRT 8.0.x supports upgrading from TensorRT 7.1.x and 7.2.x. If you want to upgrade from an unsupported version, then you should upgrade incrementally until you reach the latest version of TensorRT. ### 5.1. Ubuntu And Windows Users The following section provides step-by-step instructions for upgrading TensorRT for Ubuntu and Windows users. ### 5.1.1. Upgrading From TensorRT 7.x.x To TensorRT 8.0.x These upgrade instructions are for Ubuntu and Windows users only. When upgrading from TensorRT 7.x.x to TensorRT 8.0.x, ensure you are familiar with the following. #### About this task Using a Debian file • The Debian packages are designed to upgrade your development environment without removing any runtime components that other packages and programs might rely on. If you installed TensorRT 7.x.x via a Debian package and you upgrade to TensorRT 8.0.x, your documentation, samples, and headers will all be updated to the TensorRT 8.0.x content. After you have downloaded the new local repo, use apt-get to upgrade your system to the new version of TensorRT. os="ubuntuxx04" tag="cudax.x-trt8.x.x.x-ea-yyyymmdd" sudo dpkg -i nv-tensorrt-repo-${os}-${tag}_1-1_amd64.deb sudo apt-get update sudo apt-get install tensorrt libcudnn8 Python 2.7 is no longer supported and can be removed: sudo apt-get purge python-libnvinfer-dev If using Python 3.x: sudo apt-get install python3-libnvinfer-dev • If you are using the uff-converter and/or graphsurgeon, then you should also upgrade those Debian packages to the latest versions. sudo apt-get install uff-converter-tf graphsurgeon-tf onnx-graphsurgeon • After you upgrade, ensure you have a directory /usr/src/tensorrt and the corresponding version shown by the dpkg -l tensorrt command is 8.x.x.x. • If installing a Debian package on a system where the previously installed version was from a tar file, note that the Debian package will not remove the previously installed files. Unless a side-by-side installation is desired, it would be best to remove the older version before installing the new version to avoid compiling against outdated libraries. • If you are currently or were previously using the NVIDIA CUDA network repository, then it may conflict with the version of libcudnn8 that is expected to be installed from the local repository for TensorRT. The following commands will change libcudnn8 to version 8.2.x.x, which is supported and tested with TensorRT 8.0.x, and hold the libcudnn8 package at this version. Replace cudax.x with the appropriate CUDA version for your install. version="8.2.x.x-1+cudax.x" sudo apt-get install libcudnn8=${version} libcudnn8-dev=${version} sudo apt-mark hold libcudnn8 libcudnn8-dev Using a tar file • If you are upgrading using the tar file installation method, then install TensorRT into a new location. Tar file installations can support multiple use cases including having a full installation of TensorRT 7.x.x with headers and documentation side-by-side with a full installation of TensorRT 8.0.x. If the intention is to have the new version of TensorRT replace the old version, then the old version should be removed once the new version is verified. • If installing a tar file on a system where the previously installed version was from a Debian package, note that the tar file installation will not remove the previously installed packages. Unless a side-by-side installation is desired, it would be best to remove the previously installed libnvinfer8,libnvinfer-dev,libnvinfer-samples and other related packages to avoid confusion. Using a zip file • If you are upgrading using the zip file installation method, then install TensorRT into a new location. Zip file installations can support multiple use cases including having a full installation of TensorRT 7.x.x with headers and documentation side-by-side with a full installation of TensorRT 8.0.x. If the intention is to have the new version of TensorRT replace the old version, then the old version should be removed once the new version is verified. • After unzipping the new version of TensorRT you will need to either update the PATH environment variable to point to the new install location or copy the DLL files to the location where you previously installed the TensorRT libraries. Refer to Zip File Installation for more information about setting the PATH environment variable. ### 5.2. RedHat And CentOS Users The following section provides step-by-step instructions for upgrading TensorRT for RedHat and CentOS users. ### 5.2.1. Upgrading From TensorRT 7.x.x To TensorRT 8.0.x These upgrade instructions are for Red Hat Enterprise Linux (RHEL) and CentOS users only. When upgrading from TensorRT 7.x.x to TensorRT 8.0.x, ensure you are familiar with the following. #### About this task Using an RPM file • The RPM packages are designed to upgrade your development environment without removing any runtime components that other packages and programs might rely on. If you installed TensorRT 7.x.x via an RPM package and you want to upgrade to TensorRT 8.0.x, your documentation, samples, and headers will all be updated to the TensorRT 8.0.x content. After you have downloaded the new local repo, issue: os="rhelx" tag="cudax.x-trt8.x.x.x-ea-yyyymmdd" sudo rpm -Uvh nv-tensorrt-repo-${os}-${tag}-1-1.x86_64.rpm sudo yum clean expire-cache sudo yum install tensorrt libcudnn8 Python 2.7 is no longer supported and can be removed: sudo yum erase python-libnvinfer-devel If using Python 3.x: sudo yum install python3-libnvinfer-devel • If using uff-converter and/or graphsurgeon: sudo yum install uff-converter-tf graphsurgeon-tf onnx-graphsurgeon • After you upgrade, ensure you see the /usr/src/tensorrt directory and the corresponding version shown by the rpm -qa tensorrt command is 8.x.x.x. • If you are currently or were previously using the NVIDIA CUDA network repository, then it may conflict with the version of libcudnn8 that is expected to be installed from the local repository for TensorRT. The following commands will change libcudnn8 to version 8.2.x.x, which is supported and tested with TensorRT 8.0.x, and hold the libcudnn8 package at this version. Replace cudax.x with the appropriate CUDA version for your install. version="8.2.x.x-1.cudax.x" sudo yum downgrade libcudnn8-${version} libcudnn8-devel-\${version}
sudo yum install yum-plugin-versionlock
sudo yum versionlock libcudnn8 libcudnn8-devel

## 6. Uninstalling TensorRT

This section provides step-by-step instructions for ways in which you can uninstall TensorRT.

To uninstall TensorRT using the untarred file, simply delete the tar files and reset LD_LIBRARY_PATH to its original value.

To uninstall TensorRT using the zip file, simply delete the unzipped files and remove the newly added path from the PATH environment variable.

To uninstall TensorRT using the Debian or RPM packages, follow these steps:

## Procedure

1. Uninstall libnvinfer8 which was installed using the Debian or RPM packages.
sudo apt-get purge "libnvinfer*"
Or
sudo yum erase "libnvinfer*"
2. Uninstall uff-converter-tf, graphsurgeon-tf, and onnx-graphsurgeon which were also installed using the Debian or RPM packages.
sudo apt-get purge graphsurgeon-tf onnx-graphsurgeon
Or
sudo yum erase graphsurgeon-tf onnx-graphsurgeon

The uff-converter-tf package will also be removed with the above command.

You can use the following command to uninstall uff-converter-tf and not remove graphsurgeon-tf, however, it is no longer required.
sudo apt-get purge uff-converter-tf
Or
sudo yum erase uff-converter-tf
You can later use autoremove to uninstall graphsurgeon-tf as well.
sudo apt-get autoremove
Or
sudo yum autoremove
3. Uninstall the Python TensorRT wheel file.
If using Python 3.x:
sudo pip3 uninstall tensorrt
4. Uninstall the Python UFF wheel file.
If using Python 3.x:
sudo pip3 uninstall uff
5. Uninstall the Python GraphSurgeon wheel file.
If using Python 3.x:
sudo pip3 uninstall graphsurgeon
6. Uninstall the Python ONNX GraphSurgeon wheel file.
If using Python 3.x:
sudo pip3 uninstall onnx-graphsurgeon

## 7. Installing PyCUDA

This section provides useful information regarding PyCUDA including how to install.

Attention: If you have to update your CUDA version on your system, do not install PyCUDA at this time. Perform the steps in Updating CUDA first, then install PyCUDA.
PyCUDA is used within Python wrappers to access NVIDIA’s CUDA APIs. Some of the key features of PyCUDA include:
• Maps all of CUDA into Python.
• Enables run-time code generation (RTCG) for flexible, fast, automatically tuned codes.
• Add-on packages for FFT and LAPACK available.
To install PyCUDA first make sure nvcc is in your PATH, then issue the following command:
pip install 'pycuda<2021.1'
If you encounter any issues with PyCUDA usage after installing PyCUDA with the above command, you may need to recompile it yourself. For more information, see Installing PyCUDA on Linux.

### 7.1. Updating CUDA

Existing installations of PyCUDA will not automatically work with a newly installed CUDA Toolkit. That is because PyCUDA will only work with a CUDA Toolkit that is already on the target system when PyCUDA was installed. This requires that PyCUDA be updated after the newer version of the CUDA Toolkit is installed.
The steps below are the most reliable method to ensure that everything works in a compatible fashion after the CUDA Toolkit on your system has been upgraded.
1. Uninstall the existing PyCUDA installation.
2. Update CUDA. For more information, see the NVIDIA CUDA Installation Guide.
3. Install PyCUDA. To install PyCUDA, issue the following command:
pip install 'pycuda<2021.1'

## 8. Troubleshooting

For troubleshooting support refer to your support engineer or post your questions onto the NVIDIA Developer Forum.

NVIDIA Developer Forum

## A. Appendix

The following section provides our list of acknowledgements.

### A.1. ACKNOWLEDGEMENTS

TensorRT uses elements from the following software, whose licenses are reproduced below.

This license applies to all parts of Protocol Buffers except the following:
• Atomicops support for generic gcc, located in src/google/protobuf/stubs/atomicops_internals_generic_gcc.h. This file is copyrighted by Red Hat Inc.
• Atomicops support for AIX/POWER, located in src/google/protobuf/stubs/atomicops_internals_power.h. This file is copyrighted by Bloomberg Finance LP.
Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
• Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
• Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
• Neither the name of Google Inc. nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

Code generated by the Protocol Buffer compiler is owned by the owner of the input file used when generating it. This code is not standalone and requires a support library to be linked with it. This support library is itself covered by the above license.

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### BVLC Caffe

All contributions by the University of California:

All other contributions:

Caffe uses a shared copyright model: each contributor holds copyright over their contributions to Caffe. The project versioning records all such contribution and copyright details. If a contributor wants to further mark their specific copyright on a particular contribution, they should indicate their copyright solely in the commit message of the change when it is committed.

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### half.h

Copyright (c) 2012-2017 Christian Rau <rauy@users.sourceforge.net>

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

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### jQuery.js

jQuery.js is generated automatically under doxygen.

In all cases TensorRT uses the functions under the MIT license.

### CRC

policies, either expressed or implied, of the Regents of the University of California.

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Director, Office of Technology Licensing

University of California, Berkeley

### getopt.c

Copyright (c) 2002 Todd C. Miller <Todd.Miller@courtesan.com>

Permission to use, copy, modify, and distribute this software for any purpose with or without fee is hereby granted, provided that the above copyright notice and this permission notice appear in all copies.

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Sponsored in part by the Defense Advanced Research Projects Agency (DARPA) and Air Force Research Laboratory, Air Force Materiel Command, USAF, under agreement number F39502-99-1-0512.

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