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trtorch.logging
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TRTorch
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Ahead-of-time compilation of TorchScript / PyTorch JIT for NVIDIA GPUs
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<h1 id="index--page-root">
TRTorch
<a class="headerlink" href="#index--page-root" title="Permalink to this headline">
¶
</a>
</h1>
<h2 id="ahead-of-time-compilation-of-torchscript-pytorch-jit-for-nvidia-gpus">
Ahead-of-time compilation of TorchScript / PyTorch JIT for NVIDIA GPUs
<a class="headerlink" href="#ahead-of-time-compilation-of-torchscript-pytorch-jit-for-nvidia-gpus" title="Permalink to this headline">
¶
</a>
</h2>
<p>
TRTorch is a compiler for PyTorch/TorchScript, targeting NVIDIA GPUs via NVIDIA’s TensorRT Deep Learning Optimizer and Runtime.
Unlike PyTorch’s Just-In-Time (JIT) compiler, TRTorch is an Ahead-of-Time (AOT) compiler, meaning that before you deploy your
TorchScript code, you go through an explicit compile step to convert a standard TorchScript program into an module targeting
a TensorRT engine. TRTorch operates as a PyTorch extention and compiles modules that integrate into the JIT runtime seamlessly.
After compilation using the optimized graph should feel no different than running a TorchScript module.
You also have access to TensorRT’s suite of configurations at compile time, so you are able to specify
operating precision (FP32/FP16/INT8) and other settings for your module.
</p>
<p>
More Information / System Architecture:
</p>
<ul class="simple">
<li>
<p>
<a class="reference external" href="https://developer.nvidia.com/gtc/2020/video/s21671">
GTC 2020 Talk
</a>
</p>
</li>
</ul>
<h2 id="getting-started">
Getting Started
<a class="headerlink" href="#getting-started" title="Permalink to this headline">
¶
</a>
</h2>
<ul class="simple">
<li>
<p>
<a class="reference internal" href="tutorials/installation.html#installation">
<span class="std std-ref">
Installation
</span>
</a>
</p>
</li>
<li>
<p>
<a class="reference internal" href="tutorials/getting_started.html#getting-started">
<span class="std std-ref">
Getting Started
</span>
</a>
</p>
</li>
<li>
<p>
<a class="reference internal" href="tutorials/ptq.html#ptq">
<span class="std std-ref">
Post Training Quantization (PTQ)
</span>
</a>
</p>
</li>
<li>
<p>
<a class="reference internal" href="tutorials/trtorchc.html#trtorchc">
<span class="std std-ref">
trtorchc
</span>
</a>
</p>
</li>
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<h2 id="notebooks">
Notebooks
<a class="headerlink" href="#notebooks" title="Permalink to this headline">
¶
</a>
</h2>
<ul class="simple">
<li>
<p>
<span class="xref std std-ref">
lenet
</span>
</p>
</li>
</ul>
<div class="toctree-wrapper compound">
</div>
<h2 id="python-api-documenation">
Python API Documenation
<a class="headerlink" href="#python-api-documenation" title="Permalink to this headline">
¶
</a>
</h2>
<ul class="simple">
<li>
<p>
<a class="reference internal" href="py_api/trtorch.html#trtorch-py">
<span class="std std-ref">
trtorch
</span>
</a>
</p>
</li>
</ul>
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</div>
<h2 id="c-api-documenation">
C++ API Documenation
<a class="headerlink" href="#c-api-documenation" title="Permalink to this headline">
¶
</a>
</h2>
<ul class="simple">
<li>
<p>
<a class="reference internal" href="_cpp_api/namespace_trtorch.html#namespace-trtorch">
<span class="std std-ref">
Namespace trtorch
</span>
</a>
</p>
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