TensorRT Installation Guide :: Deep Learning SDK Documentation

Abstract

This TensorRT Installation Guide provides the installation requirements, a list of what is included in the TensorRT package, and step-by-step instructions for installing TensorRT 4.0.1.

For previously released TensorRT installation documentation, see TensorRT Archives.

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.

You can describe a TensorRT network using a C++ or Python API, or you can import an existing Caffe, ONNX, or TensorFlow model using one of the provided parsers.

The TensorRT API includes import methods to help you express your trained deep learning models for TensorRT to optimize and run. TensorRT applies graph optimizations, layer fusion, and finds the fastest implementation of that model leveraging a diverse collection of highly optimized kernels, and a runtime that you can use to execute this network in an inference context.

TensorRT includes an infrastructure that allows you to leverage the high speed mixed precision capabilities of Pascal and Volta GPUs as an optional optimization.

TensorRT for Ubuntu 14.04 is built using gcc 4.8.4 gcc 4.8.

TensorRT for Ubuntu 16.04 is built using gcc 5.4.0 gcc 5.

TensorRT for Android is built using NDK r13b.

TensorRT for QNX is built using gcc 5.4.0 gcc 5.

2. Getting Started

Ensure you are familiar with the following installation requirements and notes.

If you are using the TensorRT Python API and PyCUDA isn’t already installed on your system, see Installing PyCUDA. If you are testing on a Tesla V100, or if you encounter any issues with PyCUDA usage, you will almost certainly 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 8.0, 9.0 and 9.2 are supported.
The TensorFlow to TensorRT model export requires TensorFlow 1.8 with GPU acceleration enabled.
PyTorch version 0.4.0 is required to run the Python examples, unless noted otherwise.
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 now called libnvparsers.so in TensorRT 4.0. 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 the new libnvparser2.a.
The sample tool giexec that was included with TensorRT 3.0 has been renamed to trtexec.
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 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 the UFF file from TensorFlow.

3. Downloading TensorRT

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

Go to: https://developer.nvidia.com/tensorrt.
Click Download.
Complete the TensorRT Download Survey.
Select the checkbox to agree to the license terms.
Click the package you want to install. Your download begins.

4. Installing TensorRT

You can choose between two installation options when installing TensorRT; a debian package or tar file.

The debian installation automatically installs any dependencies, but:

requires sudo or root privileges to install
provides no flexibility as to which location TensorRT is installed into
requires that the CUDA Toolkit has also been installed using a debian package.

The tar file provides more flexibility, however, you need to ensure that you have the necessary dependencies already installed.

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		4.0.0	4.0.1	+1.0 when significant new capabilities are added. +0.1 when capabilities have been improved.
`nvinfer` library, headers, samples, and documentation.		4.1.0	4.1.2	+1.0 when the API changes in a non-compatible way. +0.1 when the API changes are backward compatible
UFF	`uff-converter-tf` debian package	4.1.0	4.1.2	+0.1 while we are developing the core functionality. Set to 1.0 when we have all base functionality in place.
UFF	`uff.whl` file	0.3.0	0.4.0
`graphsurgeon-tf`	`graphsurgeon-tf` debian package		4.1.2	+0.1 while we are developing the core functionality. Set to 1.0 when we have all base functionality in place.
`graphsurgeon-tf`	`graphsurgeon-*.whl` file		0.2.0
`libnvinfer` python package	`python-libnvinfer` `python-libnvinfer-dev` `python-libnvinfer-doc` `python3-libnvinfer` `python3-libnvinfer-dev` `python3-libnvinfer-doc` debian package	4.1.0	4.1.2	+1.0 when the API changes in a non-compatible way. +0.1 when the API changes are backward compatible.
`libnvinfer` python package	`tensorrt.whl` file	4.0.0	4.0.1

4.1. Debian Installation

This section contains instructions for a developer installation and an app server installation. Choose which installation best fits your needs.

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 and UFF packages.

Note: Before issuing the following commands, you'll need to replace ubuntu1x04, cudax.x, trt4.x.x.x and yyyymmdd with your specific OS version, CUDA version, TensorRT version and package date. The following commands are examples.

Install TensorRT from the debian package.
```
$ sudo dpkg -i  
nv-tensorrt-repo-ubuntu1x04-cudax.x-ga-trt4.x.x.x-yyyymmdd_1-1_amd64.deb

$ sudo apt-get update
$ sudo apt-get install tensorrt
```
If using Python 2.7:
```
$ sudo apt-get install python-libnvinfer-doc
```
The following additional packages will be installed:
```
  python-libnvinfer python-libnvinfer-dev python-libnvinfer-doc
```
If using Python 3.5:
```
$ sudo apt-get install python3-libnvinfer-doc
```
The following additional packages will be installed:
```
  python3-libnvinfer python3-libnvinfer-dev python3-libnvinfer-doc
```
In either case:
```
$ sudo apt-get install uff-converter-tf
```
The graphsurgeon-tf package will also be installed with the above command.

Verify the installation.

$ dpkg -l | grep TensorRT

You should see something similar to the following:

ii  graphsurgeon-tf		  4.1.x-1+cuda9.0   amd64  GraphSurgeon for TensorRT package
ii  libnvinfer-dev           4.1.x-1+cuda9.0   amd64  TensorRT development libraries and headers
ii  libnvinfer-samples       4.1.x-1+cuda9.0   amd64  TensorRT samples and documentation
ii  libnvinfer4              4.1.x-1+cuda9.0   amd64  TensorRT runtime libraries
ii  python-libnvinfer        4.1.x-1+cuda9.0   amd64  Python bindings for TensorRT
ii  python-libnvinfer-dev    4.1.x-1+cuda9.0   amd64  Python development package for TensorRT
ii  python-libnvinfer-doc    4.1.x-1+cuda9.0   amd64  Documentation and samples of python bindings for TensorRT 
ii  python3-libnvinfer       4.1.x-1+cuda9.0   amd64  Python 3 bindings for TensorRT
ii  python3-libnvinfer-dev   4.1.x-1+cuda9.0   amd64  Python 3 development package for TensorRT
ii  python3-libnvinfer-doc   4.1.x-1+cuda9.0   amd64  Documentation and samples of python bindings for TensorRT 
ii  tensorrt                 4.x.x.x-1+cuda9.0 amd64  Meta package of TensorRT
ii  uff-converter-tf         4.1.x-1+cuda9.0   amd64  UFF converter for TensorRT pack

App Server Installation: When setting up servers which will host TensorRT powered applications, you can simply install any of the following:

the libnvinfer package (C++), or
the python-libnvinfer package (Python), or
the python3-libnvinfer package (Python).

Issue the following commands if you want to run an application that was built with TensorRT. Install TensorRT from the debian package, for example:

$ sudo dpkg -i  
nv-tensorrt-repo-ubuntu1x04-cudax.x-ga-trt4.x.x.x-yyyymmdd_1-1_amd64.deb

$ sudo apt-get update
$ sudo apt-get install libnvinfer

4.2. Tar File Installation

Note: Before issuing the following commands, you'll need to replace 4.x.x.x with your specific TensorRT version. The following commands are examples.

Install the following dependencies, if not already present:
- Install the CUDA Toolkit v8.0, 9.0 or 9.2
- cuDNN 7.1.3
- Python 2 or Python 3
Choose where you want to install TensorRT. This tar file will install everything into a directory called TensorRT-4.x.x.x.
Unpack the tar file.
```
$ tar xzvf TensorRT-4.x.x.x.Ubuntu-1x.04.x.x86_64-gnu.cuda-x.x.cudnn7.1.tar.gz

$ ls TensorRT-4.x.x.x
bin  data  doc  graphsurgeon  include  lib  python  samples  targets  TensorRT-Release-Notes.pdf  uff
```
Where:
- 4.x.x.x is your TensorRT version
- Ubuntu-1x.04.x is 14.04.5 or 16.04.4
- cuda-x.x is the CUDA version 8.0, 9.0 or 9.2. This directory will have sub-directories like lib, include, data, etc…
Add the absolute path to the TensorRT lib directory to the environment variable LD_LIBRARY_PATH:
```
$ export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:<eg:TensorRT-4.x.x.x/lib>
```

Install the Python TensorRT package.

$ cd TensorRT-4.x.x.x/python

If using Python 2.7:

$ sudo pip2 install tensorrt-4.x.x.x-cp27-cp27mu-linux_x86_64.whl

If using Python 3.5:

$ sudo pip3 install 
tensorrt-4.x.x.x-cp35-cp35m-linux_x86_64.whl

In either case:

$ which tensorrt
/usr/local/bin/tensorrt

Install the Python UFF package.

$ cd TensorRT-4.x.x.x/uff

$ sudo pip2 install uff-0.4.0-py2.py3-none-any.whl

or

$ sudo pip3 install uff-0.4.0-py2.py3-none-any.whl
$ which convert-to-uff
/usr/local/bin/convert-to-uff

Install the Python graphsurgeon package.

$ cd TensorRT-4.x.x.x/graphsurgeon
	$ sudo pip2 install graphsurgeon-0.2.0-py2.py3-none-any.whl

or

$ sudo pip3 install graphsurgeon-0.2.0-py2.py3-none-any.whl

When building the custom_layers Python example, point to the location where the tar package was installed into.
- Set the environment variable TENSORRT_INC_DIR to point to the <TAR_INSTALL_ROOT>/include directory.
- Set the environment variable TENSORRT_LIB_DIR to point to <TAR_INSTALL_ROOT>/lib directory.
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. The sample should be compiled and executable without additional settings. For more information about sampleMNSIT, see the TensorRT Developer Guide.

4.3. Additional Installation Methods

Aside from installing TensorRT from the product package, you can also install it 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, to 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 from TensorRT 3.0.x to TensorRT 4.x

When upgrading from TensorRT 3.0.x to TensorRT 4.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 3.0.x via a debian package and you upgrade to TensorRT 4.x, your documentation, samples, and headers will all be updated to the TensorRT 4.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-ga-trt4.x.x.x-yyyymmdd_1-1_amd64.deb
sudo apt-get update
sudo apt-get install tensorrt libcudnn7
```
After you upgrade, ensure you have a package called tensorrt and the corresponding version shown by the dpkg -l command is 4.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 libcudnn6 has been installed in parallel with libcudnn7, then you may need to switch the default libcudnn to libcudnn7 in order to properly build applications with TensorRT. TensorRT 4.0 does not support libcudnn6 and the behavior is unpredictable if libcudnn6 is used. You can switch to the latest libcudnn using update-alternatives in auto mode rather than manual mode, which will choose the last installed version of libcudnn. This can be done using the following command:
```
$ sudo update-alternatives --auto libcudnn
```
If you are currently or were previously using the machine learning debian repository, then it will conflict with the version of libcudnn7 that is expected to be installed with the local repository for TensorRT. The following commands will downgrade libcudnn7 to version 7.1.3.16, which is supported and tested with TensorRT 4.0, and hold the libcudnn7 package at this version. Replace cuda9.0 with the appropriate CUDA version for your install.
```
sudo apt-get install libcudnn7=7.1.3.16-1+cuda9.0 \
  libcudnn7-dev=7.1.3.16-1+cuda9.0
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 3.0.x with headers and documentation side-by-side with a full installation of TensorRT 4.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

Uninstall libnvinfer4 which was installed using a debian package.
```
$ sudo apt-get purge "libnvinfer*"
```
Uninstall uff-converter-tf and graphsurgeon-tf, which were also installed using debian packages.
```
$ sudo apt-get purge "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 the graphsurgeon-tf, however, it is no longer required.
```
$ sudo apt-get purge "uff-converter-tf"
```
You can later use autoremove to uninstall graphsurgeon-tf as well.
```
$ sudo apt-get --purge autoremove
```
Uninstall the Python TensorRT package.
If using Python 2.7:
```
$ sudo pip2 uninstall tensorrt
```
If using Python 3.4 or 3.5:
```
$ sudo pip3 uninstall tensorrt
```
Uninstall the Python UFF package.
If using Python 2.7:
```
$ sudo pip2 uninstall uff
```
If using Python 3.4 or 3.5:
```
$ sudo pip3 uninstall uff
```
Uninstall the Python GraphSurgeon package.
If using Python 2.7:
```
$ sudo pip2 uninstall graphsurgeon
```
If using Python 3.4 or 3.5:
```
$ 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, 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 will almost certainly 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.

Uninstall the existing PyCUDA installation.
Update CUDA. For more information, see the CUDA Installation Guide.
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.

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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Notices

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