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Installation — TRTorch master documentation
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<section id="installation">
<span id="id1">
</span>
<h1 id="tutorials-installation--page-root">
Installation
<a class="headerlink" href="#tutorials-installation--page-root" title="Permalink to this headline">
¶
</a>
</h1>
<section id="precompiled-binaries">
<h2 id="precompiled-binaries">
Precompiled Binaries
<a class="headerlink" href="#precompiled-binaries" title="Permalink to this headline">
¶
</a>
</h2>
<section id="dependencies">
<h3 id="dependencies">
Dependencies
<a class="headerlink" href="#dependencies" title="Permalink to this headline">
¶
</a>
</h3>
<p>
You need to have either PyTorch or LibTorch installed based on if you are using Python or C++
and you must have CUDA, cuDNN and TensorRT installed.
</p>
<blockquote>
<div>
<ul class="simple">
<li>
<p>
<a class="reference external" href="https://www.pytorch.org">
https://www.pytorch.org
</a>
</p>
</li>
<li>
<p>
<a class="reference external" href="https://developer.nvidia.com/cuda">
https://developer.nvidia.com/cuda
</a>
</p>
</li>
<li>
<p>
<a class="reference external" href="https://developer.nvidia.com/cudnn">
https://developer.nvidia.com/cudnn
</a>
</p>
</li>
<li>
<p>
<a class="reference external" href="https://developer.nvidia.com/tensorrt">
https://developer.nvidia.com/tensorrt
</a>
</p>
</li>
</ul>
</div>
</blockquote>
</section>
<section id="python-package">
<h3 id="python-package">
Python Package
<a class="headerlink" href="#python-package" title="Permalink to this headline">
¶
</a>
</h3>
<p>
You can install the python package using
</p>
<div class="highlight-sh notranslate">
<div class="highlight">
<pre><span></span>pip3 install trtorch -f https://github.com/NVIDIA/TRTorch/releases
</pre>
</div>
</div>
</section>
<section id="c-binary-distribution">
<span id="bin-dist">
</span>
<h3 id="c-binary-distribution">
C++ Binary Distribution
<a class="headerlink" href="#c-binary-distribution" title="Permalink to this headline">
¶
</a>
</h3>
<p>
Precompiled tarballs for releases are provided here:
<a class="reference external" href="https://github.com/NVIDIA/TRTorch/releases">
https://github.com/NVIDIA/TRTorch/releases
</a>
</p>
</section>
</section>
<section id="compiling-from-source">
<span id="compile-from-source">
</span>
<h2 id="compiling-from-source">
Compiling From Source
<a class="headerlink" href="#compiling-from-source" title="Permalink to this headline">
¶
</a>
</h2>
<section id="dependencies-for-compilation">
<span id="installing-deps">
</span>
<h3 id="dependencies-for-compilation">
Dependencies for Compilation
<a class="headerlink" href="#dependencies-for-compilation" title="Permalink to this headline">
¶
</a>
</h3>
<p>
TRTorch is built with Bazel, so begin by installing it.
</p>
<blockquote>
<div>
<ul class="simple">
<li>
<p>
The easiest way is to install bazelisk using the method of you choosing
<a class="reference external" href="https://github.com/bazelbuild/bazelisk">
https://github.com/bazelbuild/bazelisk
</a>
</p>
</li>
<li>
<p>
Otherwise you can use the following instructions to install binaries
<a class="reference external" href="https://docs.bazel.build/versions/master/install.html">
https://docs.bazel.build/versions/master/install.html
</a>
</p>
</li>
<li>
<p>
Finally if you need to compile from source (e.g. aarch64 until bazel distributes binaries for the architecture) you can use these instructions
</p>
</li>
</ul>
<div class="highlight-shell notranslate">
<div class="highlight">
<pre><span></span><span class="nb">export</span> <span class="nv">BAZEL_VERSION</span><span class="o">=</span><span class="k">$(</span>cat <PATH_TO_TRTORCH_ROOT>/.bazelversion<span class="k">)</span>
mkdir bazel
<span class="nb">cd</span> bazel
curl -fSsL -O https://github.com/bazelbuild/bazel/releases/download/<span class="nv">$BAZEL_VERSION</span>/bazel-<span class="nv">$BAZEL_VERSION</span>-dist.zip
unzip bazel-<span class="nv">$BAZEL_VERSION</span>-dist.zip
bash ./compile.sh
cp output/bazel /usr/local/bin/
</pre>
</div>
</div>
</div>
</blockquote>
<p>
You will also need to have CUDA installed on the system (or if running in a container, the system must have
the CUDA driver installed and the container must have CUDA)
</p>
<p>
The correct LibTorch version will be pulled down for you by bazel.
</p>
<blockquote>
<div>
<p>
NOTE: For best compatability with official PyTorch, use torch==1.9.0+cuda111, TensorRT 7.2 and cuDNN 8.1 for CUDA 11.1 however TRTorch itself supports
TensorRT and cuDNN for CUDA versions other than 11.1 for usecases such as using NVIDIA compiled distributions of PyTorch that use other versions of CUDA
e.g. aarch64 or custom compiled version of PyTorch.
</p>
</div>
</blockquote>
<p>
You then have two compilation options:
</p>
</section>
<section id="building-using-cudnn-tensorrt-tarball-distributions">
<span id="build-from-archive">
</span>
<h3 id="building-using-cudnn-tensorrt-tarball-distributions">
<strong>
Building using cuDNN & TensorRT tarball distributions
</strong>
<a class="headerlink" href="#building-using-cudnn-tensorrt-tarball-distributions" title="Permalink to this headline">
¶
</a>
</h3>
<blockquote>
<div>
<p>
This is recommended so as to build TRTorch hermetically and insures any compilation errors are not caused by version issues
</p>
<p>
Make sure when running TRTorch that these versions of the libraries are prioritized in your
<code class="docutils literal notranslate">
<span class="pre">
$LD_LIBRARY_PATH
</span>
</code>
</p>
</div>
</blockquote>
<dl class="simple">
<dt>
You need to download the tarball distributions of TensorRT and cuDNN from the NVIDIA website.
</dt>
<dd>
<ul class="simple">
<li>
<p>
<a class="reference external" href="https://developer.nvidia.com/cudnn">
https://developer.nvidia.com/cudnn
</a>
</p>
</li>
<li>
<p>
<a class="reference external" href="https://developer.nvidia.com/tensorrt">
https://developer.nvidia.com/tensorrt
</a>
</p>
</li>
</ul>
</dd>
</dl>
<p>
Place these files in a directory (the directories
<code class="docutils literal notranslate">
<span class="pre">
thrid_party/distdir/[x86_64-linux-gnu
</span>
<span class="pre">
|
</span>
<span class="pre">
aarch64-linux-gnu]
</span>
</code>
exist for this purpose)
</p>
<p>
Then compile referencing the directory with the tarballs
</p>
<blockquote>
<div>
<p>
If you get errors regarding the packages, check their sha256 hashes and make sure they match the ones listed in
<code class="docutils literal notranslate">
<span class="pre">
WORKSPACE
</span>
</code>
</p>
</div>
</blockquote>
<section id="release-build">
<h4 id="release-build">
Release Build
<a class="headerlink" href="#release-build" title="Permalink to this headline">
¶
</a>
</h4>
<div class="highlight-shell notranslate">
<div class="highlight">
<pre><span></span>bazel build //:libtrtorch -c opt --distdir thrid_party/distdir/<span class="o">[</span>x86_64-linux-gnu <span class="p">|</span> aarch64-linux-gnu<span class="o">]</span>
</pre>
</div>
</div>
<p>
A tarball with the include files and library can then be found in
<code class="docutils literal notranslate">
<span class="pre">
bazel-bin
</span>
</code>
</p>
</section>
<section id="debug-build">
<span id="build-from-archive-debug">
</span>
<h4 id="debug-build">
Debug Build
<a class="headerlink" href="#debug-build" title="Permalink to this headline">
¶
</a>
</h4>
<p>
To build with debug symbols use the following command
</p>
<div class="highlight-shell notranslate">
<div class="highlight">
<pre><span></span>bazel build //:libtrtorch -c dbg --distdir thrid_party/distdir/<span class="o">[</span>x86_64-linux-gnu <span class="p">|</span> aarch64-linux-gnu<span class="o">]</span>
</pre>
</div>
</div>
<p>
A tarball with the include files and library can then be found in
<code class="docutils literal notranslate">
<span class="pre">
bazel-bin
</span>
</code>
</p>
</section>
<section id="pre-cxx11-abi-build">
<h4 id="pre-cxx11-abi-build">
Pre CXX11 ABI Build
<a class="headerlink" href="#pre-cxx11-abi-build" title="Permalink to this headline">
¶
</a>
</h4>
<p>
To build using the pre-CXX11 ABI use the
<code class="docutils literal notranslate">
<span class="pre">
pre_cxx11_abi