Abstract

This TensorRT 5.1.5 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 which 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 allows 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, and Turing 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.

  • On IBM PowerPC only a tar file installation is supported at this time. Debian and RPM installations may be supported in a future release.

  • 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 9.0, 10.0 and 10.1 are supported.

  • The TensorFlow to TensorRT model export requires TensorFlow 1.12.0.

  • The PyTorch examples have been tested with PyTorch 1.0, but may work with older versions.

  • 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 file 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.

3. Downloading TensorRT

Ensure you are a member of the NVIDIA Developer Program. If not, follow the prompts to gain access.
  1. Go to: https://developer.nvidia.com/tensorrt.
  2. Click Download Now.
  3. Select the version of TensorRT that you are interested in.
  4. Select the check-box to agree to the license terms.
  5. Click the package you want to install. Your download begins.

4. Installing TensorRT

You can choose between the following installation options when installing TensorRT; Debian or RPM packages, 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.

The tar file provides more flexibility, however, you need to ensure that you have the necessary dependencies already installed. 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 5.1.3 5.1.5 +1.0 when significant new capabilities are added.

+0.1 when capabilities have been improved.

nvinfer library, headers, samples, and documentation. 5.1.3 5.1.5 +1.0 when the API or ABI changes in a non-compatible way.

+0.1 when the API or ABI changes are backward compatible

UFF1 uff-converter-tf Debian and RPM packages2 5.1.3 5.1.5 +0.1 while we are developing the core functionality.

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

uff-*.whl file 0.6.3 0.6.3
graphsurgeon1 graphsurgeon-tf Debian and RPM packages2 5.1.3 5.1.5 +0.1 while we are developing the core functionality.

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

graphsurgeon-*.whl file 0.4.1 0.4.1
libnvinfer python packages1
  • python-libnvinfer
  • python-libnvinfer-dev
  • python3-libnvinfer
  • python3-libnvinfer-dev
Debian and RPM packages2		 5.1.3 5.1.5 +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 5.1.3 5.1.5

4.1. Debian Installation

This section contains instructions for a developer installation and an app server installation.
Note: A Debian installation is not supported on IBM PowerPC at this time.

This installation method is for new users or users who want the complete installation, including Python, 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 Machine Learning Network Repo For Debian Installation).

Developer Installation: The following instructions sets up a full TensorRT development environment with samples, documentation and both the C++ and Python API.
Attention: If only the C++ development environment is desired, you can modify the following instructions and simply not install the Python packages.
Note: Before issuing the following commands, you'll need to replace ubuntu1x04, cudax.x, trt5.1.x.x and yyyymmdd with your specific OS version, CUDA version, TensorRT version and package date. The following commands are examples.
  1. Download the TensorRT local repo file that matches the Ubuntu version you are using.
  2. Install TensorRT from the Debian local repo package. 
    $ sudo dpkg -i  
nv-tensorrt-repo-ubuntu1x04-cudax.x-trt5.1.x.x-ga-yyyymmdd_1-1_amd64.deb
$ sudo apt-key add /var/nv-tensorrt-repo-cudax.x-trt5.1.x.x-ga-yyyymmdd/7fa2af80.pub

$ sudo apt-get update
$ sudo apt-get install tensorrt

    If using Python 2.7:
    $ sudo apt-get install python-libnvinfer-dev
    The following additional packages will be installed: 
      python-libnvinfer

    If using Python 3.x:
    $ sudo apt-get install python3-libnvinfer-dev
    The following additional packages will be installed: 
      python3-libnvinfer

    If you plan to use TensorRT with TensorFlow:
    $ sudo apt-get install uff-converter-tf
    The graphsurgeon-tf package will also be installed with the above command.
  3. Verify the installation.
    $ dpkg -l | grep TensorRT

    You should see something similar to the following:
    ii  graphsurgeon-tf	5.1.5-1+cuda10.1	amd64	GraphSurgeon for TensorRT package
ii  libnvinfer-dev	5.1.5-1+cuda10.1	amd64	TensorRT development libraries and headers
ii  libnvinfer-samples	5.1.5-1+cuda10.1	amd64	TensorRT samples and documentation
ii  libnvinfer5		5.1.5-1+cuda10.1	amd64	TensorRT runtime libraries
ii  python-libnvinfer	5.1.5-1+cuda10.1	amd64	Python bindings for TensorRT
ii  python-libnvinfer-dev	5.1.5-1+cuda10.1	amd64	Python development package for TensorRT
ii  python3-libnvinfer	5.1.5-1+cuda10.1	amd64	Python 3 bindings for TensorRT
ii  python3-libnvinfer-dev	5.1.5-1+cuda10.1	amd64	Python 3 development package for TensorRT
ii  tensorrt	5.1.5.x-1+cuda10.1	amd64	Meta package of TensorRT
ii  uff-converter-tf	5.1.5-1+cuda10.1	amd64	UFF converter for TensorRT package
App Server Installation: When setting up servers which will host TensorRT powered applications, you can simply install any of the following:
  • the libnvinfer5 package (C++), or
  • the python-libnvinfer package (Python 2.7), or
  • the python3-libnvinfer package (Python 3.x).
Issue the following commands if you want to run an application that was built with TensorRT using the Debian package, for example: 
$ sudo dpkg -i  
nv-tensorrt-repo-ubuntu1x04-cudax.x-trt5.1.x.x-ga-yyyymmdd_1-1_amd64.deb
$ sudo apt-key add /var/nv-tensorrt-repo-cudax.x-trt5.1.x.x-ga-yyyymmdd/7fa2af80.pub

$ sudo apt-get update
$ sudo apt-get install libnvinfer5

4.1.1. Using The NVIDIA Machine Learning Network Repo For Debian Installation

When only the C++ libraries and headers are required, you can install TensorRT from the NVIDIA Machine Learning network repository.

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 Python, samples and documentation, should follow the local repo installation instructions (see Debian Installation).

Note: It’s suggested that you setup the NVIDIA CUDA network repository first before setting up the NVIDIA Machine Learning network repository to satisfy package dependencies. We provide some example commands below to accomplish this task. For more information, see the NVIDIA CUDA Installation Guide for Linux for more information.
  1. Install the NVIDIA CUDA network repository installation package.
    $ wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1x04/x86_64/cuda-repo-ubuntu1x04_x.y.z-1_amd64.deb
$ sudo dpkg -i cuda-repo-*.deb
    Where:
    • OS version: ubuntu1x04 is 1404, 1604 or 1804
    • CUDA version: x.y.z is 9.0.176, 10.0.130 or 10.1.163
  2. Install the NVIDIA Machine Learning network repository installation package. Choose the wget command below that matches the Ubuntu version you are using.
    $ wget https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb
$ wget https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1604/x86_64/nvidia-machine-learning-repo-ubuntu1604_1.0.0-1_amd64.deb
$ wget https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64/nvidia-machine-learning-repo-ubuntu1804_1.0.0-1_amd64.deb

$ sudo dpkg -i nvidia-machine-learning-repo-*.deb
$ sudo apt-get update
  3. Install the TensorRT package that fits your particular needs.
    1. For only running TensorRT C++ applications:
      $ sudo apt-get install libnvinfer5
    2. For also building TensorRT C++ applications:
      $ sudo apt-get install libnvinfer-dev
  4. When using the NVIDIA Machine Learning network repository, Ubuntu will be default install TensorRT for the latest CUDA version. The following commands will install libnvinfer5 for an older CUDA version and hold the libnvinfer5 package at this version. Replace 5.1.x with your version of TensorRT and cuda10.0 with your CUDA version for your install.
    $ sudo apt-get install libnvinfer5=5.1.x-1+cuda10.0 \
    libnvinfer-dev=5.1.x-1+cuda10.0
$ sudo apt-mark hold libnvinfer5 libnvinfer-dev
    If you want to upgrade to the latest version of TensorRT or the latest version of CUDA, then you can unhold the libnvinfer5 package using the following command.
    $ sudo apt-mark unhold libnvinfer5 libnvinfer-dev

    You may need to repeat these steps for libcudnn7 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 From TensorRT 5.x.x To TensorRT 5.1.x. See the cuDNN Installation Guide for additional information.

    If both the NVIDIA Machine Learning 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. RPM Installation

This section contains instructions for installing TensorRT from an RPM package.
Note: An RPM installation is not supported on IBM PowerPC at this time.

This installation method is for new users or users who want the complete installation, including Python, 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 Machine Learning Network Repo For RPM Installation).

Note:
  • Before issuing the following commands, you'll need to replace cudax.x, trt5.1.x.x, and yyyymmdd with your specific CUDA version, TensorRT version, and package date. The following commands are examples.
  • If you want to install the Python 3 RPM package, you must first enable the EPEL repository. For more information about enabling EPEL, see the RPM package instructions in the CUDA Quick Start Guide.
  1. Download the TensorRT local repo file that matches the RHEL/CentOS version you are using.
  2. Install TensorRT from the RPM local repo package. 
    $ sudo rpm -Uvh nv-tensorrt-repo-rhel7-cudax.x-trt5.1.x.x-ga-yyyymmdd-1-1.x86_64.rpm
$ sudo yum clean expire-cache
$ sudo yum install tensorrt

    If using Python 2.7:
    $ sudo yum install python-libnvinfer-devel
    The following additional packages will be installed:
    python-libnvinfer
    If using Python 3:
    $ sudo yum install python3-libnvinfer-devel
    The following additional packages will be installed: 
    python3-libnvinfer
    and for the UFF converter (only required if you plan to use TensorRT with TensorFlow):
    $ sudo yum install uff-converter-tf
  3. Verify the installation.
    1. Run: 
      $ yum list | grep tensorrt
      You should see something similar to the following:
      tensorrt.x86_64                          5.1.5.x-1.cuda10.1            installed
    2. Run: 
      $ yum list | grep libnvinfer
      You should see something similar to the following:
      libnvinfer-devel.x86_64                   5.1.5-1.cuda10.1              installed
libnvinfer-samples.x86_64                 5.1.5-1.cuda10.1              installed
libnvinfer5.x86_64                        5.1.5-1.cuda10.1              installed
python-libnvinfer.x86_64                  5.1.5-1.cuda10.1              installed
python-libnvinfer-devel.x86_64            5.1.5-1.cuda10.1              installed
python3-libnvinfer.x86_64                 5.1.5-1.cuda10.1              installed
python3-libnvinfer-devel.x86_64           5.1.5-1.cuda10.1              installed
    3. Run: 
      $ yum list | grep graphsurgeon-tf
      You should see something similar to the following:
      graphsurgeon-tf.x86_64                     5.1.5-1.cuda10.1              installed
    4. Run: 
      $ yum list | grep uff-converter-tf
      You should see something similar to the following:
      uff-converter-tf.x86_64                    5.1.5-1.cuda10.1              installed

4.2.1. Using The NVIDIA Machine Learning Network Repo For RPM Installation

When only the C++ libraries and headers are required, you can install TensorRT from the NVIDIA Machine Learning network repository.

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 Python, samples and documentation, should follow the local repo installation instructions (see RPM Installation).

Note: It’s suggested that you setup the NVIDIA CUDA network repository first before setting up the NVIDIA Machine Learning network repository to satisfy package dependencies. We provide some example commands below to accomplish this task. For more information, see the NVIDIA CUDA Installation Guide for Linux.
  1. Install the NVIDIA CUDA network repository installation package.
    $ wget https://developer.download.nvidia.com/compute/cuda/repos/rhel7/x86_64/cuda-repo-rhel7-x.y.z-1.x86_64.rpm
$ sudo rpm -Uvh cuda-repo-*.rpm
    Where:
    • CUDA version: x.y.z is 9.0.176, 10.0.130 or 10.1.163
  2. Install the NVIDIA Machine Learning network repository installation package.
    $ wget https://developer.download.nvidia.com/compute/machine-learning/repos/rhel7/x86_64/nvidia-machine-learning-repo-rhel7-1.0.0-1.x86_64.rpm

$ sudo rpm -Uvh nvidia-machine-learning-repo-*.rpm
  3. Install the TensorRT package that fits your particular needs.
    1. For only running TensorRT C++ applications:
      $ sudo yum install libnvinfer5
    2. For also building TensorRT C++ applications:
      $ sudo yum install libnvinfer-devel
  4. When using the NVIDIA Machine Learning network repository, RHEL will by default install TensorRT for the latest CUDA version. The following commands will install libnvinfer5 for an older CUDA version and hold the libnvinfer5 package at this version. Replace 5.1.x with your version of TensorRT and cuda10.0 with your CUDA version for your install.
    $ sudo yum downgrade libnvinfer5-5.1.x-1.cuda10.0 \
    libnvinfer-devel-5.1.x-1.cuda10.0
$ sudo yum install yum-plugin-versionlock
$ sudo yum versionlock libnvinfer5 libnvinfer5-devel
    If you want to upgrade to the latest version of TensorRT or the latest version of CUDA, then you can unhold the libnvinfer5 package using the following command.
    $ sudo yum versionlock delete libnvinfer5 libnvinfer5-devel

    You may need to repeat these steps for libcudnn7 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 From TensorRT 5.x.x To TensorRT 5.1.x For RedHat And CentOS Users. See the cuDNN Installation Guide for additional information.

4.3. Tar File Installation

Note: Before issuing the following commands, you'll need to replace 5.1.x.x with your specific TensorRT version. The following commands are examples.
  1. Install the following dependencies, if not already present:
    • Install the CUDA Toolkit 9.0, 10.0 or 10.1
    • cuDNN 7.5.0
    • Python 2 or Python 3 (Optional)
  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-5.1.x.x.
  4. Unpack the tar file.
    $ tar xzvf TensorRT-5.1.x.x.<os>.<arch>-gnu.cuda-x.x.cudnn7.x.tar.gz
    Where:
    • 5.1.x.x is your TensorRT version
    • <os> is Ubuntu-14.04.5, Ubuntu-16.04.5, Ubuntu-18.04.2, Red-Hat, or CentOS-Linux
    • <arch> is x86_64 or ppc64le
    • cuda-x.x is CUDA version 9.0, 10.0, or 10.1
    • cudnn7.x is cuDNN version 7.5
    This directory will have sub-directories like lib, include, data, etc…
    $ ls TensorRT-5.1.x.x
bin  data  doc  graphsurgeon  include  lib  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:<eg:TensorRT-5.1.x.x/lib>
  6. Install the Python TensorRT wheel file.
    $ cd TensorRT-5.1.x.x/python

    If using Python 2.7:
    $ sudo pip2 install tensorrt-5.1.x.x-cp27-none-linux_x86_64.whl

    If using Python 3.x:
    $ sudo pip3 install tensorrt-5.1.x.x-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-5.1.x.x/uff

    If using Python 2.7:
    $ sudo pip2 install uff-0.6.3-py2.py3-none-any.whl

    If using Python 3.x:
    $ sudo pip3 install uff-0.6.3-py2.py3-none-any.whl

    In either case:
    $ which convert-to-uff
/usr/local/bin/convert-to-uff
  8. Install the Python graphsurgeon wheel file.
    $ cd TensorRT-5.1.x.x/graphsurgeon

    If using Python 2.7:
    $ sudo pip2 install graphsurgeon-0.4.1-py2.py3-none-any.whl

    If using Python 3.x:
    $ sudo pip3 install graphsurgeon-0.4.1-py2.py3-none-any.whl
  9. 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 about sampleMNSIT, see the "Hello World" For TensorRT sample.
    3. The Python samples are in the samples/python directory.

4.4. Zip File Installation

Ensure that you have the following dependencies installed.
  • CUDA Toolkit (CUDA versions 9.0, 10.0, and 10.1 are supported)
  • cuDNN (cuDNN version 7.5.0 is supported)

This section contains instructions for installing TensorRT from a zip package on Windows 10.

  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-5.1.x.x. This new sub directory will be referred to as <installpath> in the steps below.
  3. Unzip the TensorRT-5.1.x.x.Windows10.x86_64.cuda-x.x.cudnnx.x.zip file to the location that you chose. Replace:
    1. 5.1.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 which 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 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 is 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.

4.5. 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

5.1. Upgrading From TensorRT 5.x.x To TensorRT 5.1.x

When upgrading from TensorRT 5.x.x to TensorRT 5.1.x, ensure you are familiar with the following notes:

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 5.x.x via a Debian package and you upgrade to TensorRT 5.1.x, your documentation, samples, and headers will all be updated to the TensorRT 5.1.x content. After you have downloaded the new local repo, use apt-get to upgrade your system to the new version of TensorRT.
    sudo dpkg -i nv-tensorrt-repo-ubuntu1x04-cudax.x-trt5.1.x.x-ga-yyyymmdd_1-1_amd64.deb
sudo apt-get update
sudo apt-get install tensorrt libnvinfer-samples libcudnn7

  • 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

  • After you upgrade, ensure you have a directory called /usr/src/ and the corresponding version shown by the dpkg -l command is 5.1.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 Machine Learning network repository, then it may conflict with the version of libcudnn7 that is expected to be installed from the local repository for TensorRT. The following commands will downgrade libcudnn7 to version 7.5.x.x, which is supported and tested with TensorRT 5.1.x, and hold the libcudnn7 package at this version. Replace cuda10.1 with the appropriate CUDA version for your install. 
    sudo apt-get install libcudnn7=7.5.x.x-1+cuda10.1 \
  libcudnn7-dev=7.5.x.x-1+cuda10.1
sudo apt-mark hold libcudnn7 libcudnn7-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 5.x.x with headers and documentation side-by-side with a full installation of TensorRT 5.1.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 libnvinfer5, libnvinfer-dev, and libnvinfer-samples 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 5.x.x with headers and documentation side-by-side with a full installation of TensorRT 5.1.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. Upgrading From TensorRT 5.x.x To TensorRT 5.1.x For RedHat And CentOS Users

These upgrade instructions are for Red Hat Enterprise Linux (RHEL) and CentOS users only. When upgrading from TensorRT 5.x.x to TensorRT 5.1.x, ensure you are familiar with the following notes:

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 5.x.x via an RPM package and you want to upgrade to TensorRT 5.1.x, your documentation, samples, and headers will all be updated to the TensorRT 5.1.x content. After you have downloaded the new local repo, issue:
    sudo rpm -Uvh nv-tensorrt-repo-rhel7-cudax.x-trt5.1.x.x-ga-yyyymmdd-1-1.x86_64.rpm
sudo yum clean expire-cache
sudo yum install tensorrt libcudnn7

  • If using Python 2.7:
    sudo yum install python-libnvinfer-devel

  • If using Python 3:
    sudo yum install python3-libnvinfer-devel

  • If using uff-converter and/or graphsurgeon:
    sudo yum install uff-converter-tf graphsurgeon-tf

  • After you upgrade, ensure you see the /usr/src/tensorrt directory and the corresponding version shown by the rpm -qa command is 5.1.x.x.

  • If you are currently or were previously using the NVIDIA Machine Learning network repository, then it may conflict with the version of libcudnn7 that is expected to be installed from the local repository for TensorRT. The following commands will downgrade libcudnn7 to version 7.5.x.x, which is supported and tested with TensorRT 5.1.x, and hold the libcudnn7 package at this version. Replace cuda10.1 with the appropriate CUDA version for your install.
    sudo yum downgrade libcudnn7-7.5.x.x-1.cuda10.1 \
  libcudnn7-devel-7.5.x.x-1.cuda10.1
sudo yum install yum-plugin-versionlock
sudo yum versionlock libcudnn7 libcudnn7-devel

5.3. Upgrading From TensorRT 4.0.x To TensorRT 5.1.x

When upgrading from TensorRT 4.0.x to TensorRT 5.1.x, ensure you are familiar with the following notes:

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 4.0.x via a Debian package and you upgrade to TensorRT 5.1.x, your documentation, samples, and headers will all be updated to the TensorRT 5.1.x content. After you have downloaded the new local repo, use apt-get to upgrade your system to the new version of TensorRT.
    sudo dpkg -i nv-tensorrt-repo-ubuntu1x04-cudax.x-trt5.1.x.x-ga-yyyymmdd_1-1_amd64.deb
sudo apt-get update
sudo apt-get install tensorrt libcudnn7
    If using Python 2.7:
    $ sudo apt-get install 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

  • After you upgrade, ensure you have a directory called /usr/src/ and the corresponding version shown by the dpkg -l command is 5.1.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 Machine Learning network repository, then it may conflict with the version of libcudnn7 that is expected to be installed from the local repository for TensorRT. The following commands will downgrade libcudnn7 to version 7.5.x.x, which is supported and tested with TensorRT 5.1.x, and hold the libcudnn7 package at this version. Replace cuda10.1 with the appropriate CUDA version for your install. 
    sudo apt-get install libcudnn7=7.5.x.x-1+cuda10.1 \
  libcudnn7-dev=7.5.x.x-1+cuda10.1
sudo apt-mark hold libcudnn7 libcudnn7-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 4.0.x with headers and documentation side-by-side with a full installation of TensorRT 5.1.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 libnvinfer4, libnvinfer-dev, and libnvinfer-samples packages to avoid confusion.

6. Uninstalling TensorRT

To uninstall TensorRT using the tar 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:

  1. Uninstall libnvinfer5 which was installed using the Debian or RPM packages.
    $ sudo apt-get purge "libnvinfer*"
    Or
    $ sudo yum erase "libnvinfer*"
  2. Uninstall uff-converter-tf and graphsurgeon-tf, which were also installed using the Debian or RPM packages.
    $ sudo apt-get purge "graphsurgeon-tf"
    Or
    $ sudo yum erase "graphsurgeon-tf"

    The uff-converter-tf 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 --purge autoremove
    Or
    $ sudo yum autoremove
  3. Uninstall the Python TensorRT wheel file.
    If using Python 2.7:
    $ sudo pip2 uninstall tensorrt
    If using Python 3.x: 
    $ sudo pip3 uninstall tensorrt
  4. Uninstall the Python UFF wheel file.
    If using Python 2.7:
    $ sudo pip2 uninstall uff
    If using Python 3.x: 
    $ sudo pip3 uninstall uff
  5. Uninstall the Python GraphSurgeon wheel file.
    If using Python 2.7:
    $ sudo pip2 uninstall graphsurgeon
    If using Python 3.x: 
    $ sudo pip3 uninstall graphsurgeon

7. Installing PyCUDA

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.
  • Added robustness: automatic management of object lifetimes, automatic error checking
  • Added convenience: comes with ready-made on-GPU linear algebra, reduction, scan.
  • Add-on packages for FFT and LAPACK available.
  • Fast. Near-zero wrapping overhead.
To install PyCUDA first make sure nvcc is in your PATH, then issue the following command: 
pip install 'pycuda>=2017.1.1'
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 CUDA Installation Guide.
  3. Install PyCUDA. To install PyCUDA, issue the following command: 
    pip install 'pycuda>=2017.1.1'

8. Troubleshooting

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

A. Appendix

A.1. ACKNOWLEDGEMENTS

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

Google Protobuf

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.
Copyright 2014, Google Inc. All rights reserved.
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.

Google Flatbuffers

Apache License Version 2.0, January 2004 http://www.apache.org/licenses/

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1 These components are not included in the zip file installation for Windows.
2 Debian and RPM packages are not supported on IBM PowerPC.