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Installation — Torch-TensorRT master documentation
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Installation
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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 torch-tensorrt -f https://github.com/NVIDIA/Torch-TensorRT/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/Torch-TensorRT/releases">
https://github.com/NVIDIA/Torch-TensorRT/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>
Torch-TensorRT 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 your 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">$(</span>cat <PATH_TO_TORCHTRT_ROOT>/.bazelversion<span class="k">)</span>
mkdir bazel
<span class="nb">cd</span> bazel
curl -fSsL -O https://github.com/bazelbuild/bazel/releases/download/<span class="nv">$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==1.10.0+cuda113, TensorRT 8.0 and cuDNN 8.2 for CUDA 11.3 however Torch-TensorRT itself supports
TensorRT and cuDNN for other CUDA versions 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>
<section id="choosing-the-right-abi">
<span id="abis">
</span>
<h4 id="choosing-the-right-abi">
Choosing the Right ABI
<a class="headerlink" href="#choosing-the-right-abi" title="Permalink to this headline">
¶
</a>
</h4>
<p>
Likely the most complicated thing about compiling Torch-TensorRT is selecting the correct ABI. There are two options
which are incompatible with each other, pre-cxx11-abi and the cxx11-abi. The complexity comes from the fact that while
the most popular distribution of PyTorch (wheels downloaded from pytorch.org/pypi directly) use the pre-cxx11-abi, most
other distributions you might encounter (e.g. ones from NVIDIA - NGC containers, and builds for Jetson as well as certain
libtorch builds and likely if you build PyTorch from source) use the cxx11-abi. It is important you compile Torch-TensorRT
using the correct ABI to function properly. Below is a table with general pairings of PyTorch distribution sources and the
recommended commands:
</p>
<table>
<colgroup>
<col style="width: 33%"/>
<col style="width: 31%"/>
<col style="width: 36%"/>
</colgroup>
<thead>
<tr class="row-odd">
<th class="head">
<p>
PyTorch Source
</p>
</th>
<th class="head">
<p>
Recommended C++ Compilation Command
</p>
</th>
<th class="head">
<p>
Recommended Python Compilation Command
</p>
</th>