DU-08731-001 v4.0.1 | June 2018 TENSORRT 4.0.1 ... · PDF file Getting Started TensorRT 4.0.1 DU-08731-001_v4.0.1 | 3 ‣ The installation instructions below assume you want the full

TENSORRT 4.0.1

DU-08731-001_v4.0.1 | June 2018

Installation Guide

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TABLE OF CONTENTS

Chapter 1. Overview............................................................................................ 1 Chapter 2. Getting Started.....................................................................................2 Chapter 3. Downloading TensorRT............................................................................4 Chapter 4.  Installing TensorRT................................................................................ 5

4.1. Debian Installation........................................................................................7 4.2. Tar File Installation.......................................................................................9 4.3. Additional Installation Methods....................................................................... 10

Chapter 5. Upgrading from TensorRT 3.0.x to TensorRT 4.x.......................................... 12 Chapter 6. Uninstalling TensorRT........................................................................... 14 Chapter 7.  Installing PyCUDA................................................................................ 16

7.1. Updating CUDA...........................................................................................16 Chapter 8. Troubleshooting...................................................................................18 Appendix A. Appendix......................................................................................... 19

A.1. ACKNOWLEDGEMENTS...................................................................................19

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Chapter 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.

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.

https://packages.ubuntu.com/trusty-updates/gcc-4.8 https://packages.ubuntu.com/xenial-updates/gcc-5 https://developer.android.com/ndk/downloads/revision_history https://gcc.gnu.org/gcc-5

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Chapter 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.

https://wiki.tiker.net/PyCuda/Installation/Linux http://docs.nvidia.com/deeplearning/sdk/tensorrt-release-notes/index.html

Getting Started

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‣ 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.

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Chapter 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. 3. Complete the TensorRT Download Survey. 4. Select the checkbox to agree to the license terms. 5. Click the package you want to install. Your download begins.

https://developer.nvidia.com/tensorrt

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Chapter 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.

Installing TensorRT

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Product or Component Previously Released Version Current Version

Version Description

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-converter-

tf debian package

4.1.0 4.1.2UFF

uff.whl file 0.3.0 0.4.0

+0.1 while we are

developing the

core functionality.

Set to 1.0 when

we have all base

functionality in

place.

graphsurgeon-tf

debian package

4.1.2graphsurgeon-tf

graphsurgeon-

*.whl file

0.2.0

+0.1 while we are

developing the

core functionality.

Set to 1.0 when

we have all base

functionality in

place.

libnvinfer

python package ‣ python-

libnvinfer

‣ python- libnvinfer-

dev

‣ python- libnvinfer-

doc

‣ python3- libnvinfer

‣ python3- libnvinfer-

dev

‣ python3- libnvinfer-

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.

Installing TensorRT

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Product or Component Previously Released Version Current Version

Version Description

doc debian

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