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<li class="toctree-l1"><a class="reference internal" href="../notes/amp_examples.html">CUDA Automatic Mixed Precision examples</a></li>
<li class="toctree-l1"><a class="reference internal" href="../notes/autograd.html">Autograd mechanics</a></li>
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<li class="toctree-l1 current"><a class="reference internal" href="index.html">TorchDynamo Overview</a></li>
<li class="toctree-l1 current"><a class="current reference internal" href="#">Installing TorchDynamo</a></li>
<li class="toctree-l1"><a class="reference internal" href="get-started.html">Getting Started</a></li>
<li class="toctree-l1"><a class="reference internal" href="guards-overview.html">Guards Overview</a></li>
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<section id="installing-torchdynamo">
<h1>Installing TorchDynamo<a class="headerlink" href="#installing-torchdynamo" title="Permalink to this heading">¶</a></h1>
<p>This section describes how to install TorchDynamo.
TorchDynamo is included in the nightly binaries of PyTorch. For
more information, see <a class="reference external" href="https://pytorch.org/get-started/locally/">Getting Started</a>.</p>
<section id="requirements">
<h2>Requirements<a class="headerlink" href="#requirements" title="Permalink to this heading">¶</a></h2>
<p>You must have the following prerequisites to use TorchDynamo:</p>
<ul class="simple">
<li><p>A Linux or macOS environment</p></li>
<li><p>Python 3.8 (recommended). Python 3.7 through 3.10 are supported and
tested. Make sure to have a development version of Python installed
locally as well.</p></li>
</ul>
<section id="gpu-cuda-requirements">
<h3>GPU/CUDA Requirements<a class="headerlink" href="#gpu-cuda-requirements" title="Permalink to this heading">¶</a></h3>
<p>To use GPU back ends, and in particular Triton, make sure that
the CUDA that you have installed locally matches the PyTorch version you
are running.</p>
<p>The following command installs GPU PyTorch + TorchDynamo along with GPU
TorchDynamo dependencies (for CUDA 11.7):</p>
<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>pip3 install numpy --pre torch --force-reinstall --extra-index-url https://download.pytorch.org/whl/nightly/cu117
</pre></div>
</div>
</section>
<section id="cpu-requirements">
<h3>CPU requirements<a class="headerlink" href="#cpu-requirements" title="Permalink to this heading">¶</a></h3>
<p>There are no additional requirements for CPU TorchDynamo. CPU
TorchDynamo is included in the nightly versions of PyTorch.
To install, run the following command:</p>
<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>pip3 install --pre torch --extra-index-url https://download.pytorch.org/whl/nightly/cpu
</pre></div>
</div>
</section>
<section id="verify-installation">
<h3>Verify Installation<a class="headerlink" href="#verify-installation" title="Permalink to this heading">¶</a></h3>
<p>If you built PyTorch from source, then you can run the following
commands (from the PyTorch repo root directory)
to check that TorchDynamo is installed correctly:</p>
<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span><span class="nb">cd</span> tools/dynamo
python verify_dynamo.py
</pre></div>
</div>
<p>If you do not have the PyTorch source locally, you can alternatively
copy the script (<code class="docutils literal notranslate"><span class="pre">tools/dynamo/verify_dynamo.py</span></code>) from the PyTorch
repository and run it locally.</p>
</section>
</section>
<section id="docker-installation">
<h2>Docker Installation<a class="headerlink" href="#docker-installation" title="Permalink to this heading">¶</a></h2>
<p>We also provide all the required dependencies in the PyTorch nightly
binaries which you can download with the following command:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">docker</span> <span class="n">pull</span> <span class="n">ghcr</span><span class="o">.</span><span class="n">io</span><span class="o">/</span><span class="n">pytorch</span><span class="o">/</span><span class="n">pytorch</span><span class="o">-</span><span class="n">nightly</span>
</pre></div>
</div>
<p>And for ad hoc experiments just make sure that your container has access
to all your GPUs:</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>docker run --gpus all -it ghcr.io/pytorch/pytorch-nightly:latest /bin/bash
</pre></div>
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