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<p class="caption"><span class="caption-text">Notes</span></p>
<ul class="current">
<li class="toctree-l1"><a class="reference internal" href="amp_examples.html">Automatic Mixed Precision examples</a></li>
<li class="toctree-l1"><a class="reference internal" href="autograd.html">Autograd mechanics</a></li>
<li class="toctree-l1"><a class="reference internal" href="broadcasting.html">Broadcasting semantics</a></li>
<li class="toctree-l1"><a class="reference internal" href="cpu_threading_torchscript_inference.html">CPU threading and TorchScript inference</a></li>
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<li class="toctree-l1"><a class="reference internal" href="ddp.html">Distributed Data Parallel</a></li>
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<li class="toctree-l1"><a class="reference internal" href="modules.html">Modules</a></li>
<li class="toctree-l1 current"><a class="current reference internal" href="#">Multiprocessing best practices</a></li>
<li class="toctree-l1"><a class="reference internal" href="randomness.html">Reproducibility</a></li>
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<div class="section" id="multiprocessing-best-practices">
<span id="id1"></span><h1>Multiprocessing best practices<a class="headerlink" href="#multiprocessing-best-practices" title="Permalink to this headline">¶</a></h1>
<p><a class="reference internal" href="../multiprocessing.html#module-torch.multiprocessing" title="torch.multiprocessing"><code class="xref py py-mod docutils literal notranslate"><span class="pre">torch.multiprocessing</span></code></a> is a drop in replacement for Python’s
<a class="reference external" href="https://docs.python.org/3/library/multiprocessing.html#module-multiprocessing" title="(in Python v3.10)"><code class="docutils literal notranslate"><span class="pre">multiprocessing</span></code></a> module. It supports the exact same operations,
but extends it, so that all tensors sent through a
<a class="reference external" href="https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Queue" title="(in Python v3.10)"><code class="docutils literal notranslate"><span class="pre">multiprocessing.Queue</span></code></a>, will have their data moved into shared
memory and will only send a handle to another process.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>When a <a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">Tensor</span></code></a> is sent to another process, the
<a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">Tensor</span></code></a> data is shared. If <a class="reference internal" href="../generated/torch.Tensor.grad.html#torch.Tensor.grad" title="torch.Tensor.grad"><code class="xref py py-attr docutils literal notranslate"><span class="pre">torch.Tensor.grad</span></code></a> is
not <code class="docutils literal notranslate"><span class="pre">None</span></code>, it is also shared. After a <a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">Tensor</span></code></a> without
a <a class="reference internal" href="../generated/torch.Tensor.grad.html#torch.Tensor.grad" title="torch.Tensor.grad"><code class="xref py py-attr docutils literal notranslate"><span class="pre">torch.Tensor.grad</span></code></a> field is sent to the other process, it
creates a standard process-specific <code class="docutils literal notranslate"><span class="pre">.grad</span></code> <a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">Tensor</span></code></a> that
is not automatically shared across all processes, unlike how the
<a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">Tensor</span></code></a>’s data has been shared.</p>
</div>
<p>This allows to implement various training methods, like Hogwild, A3C, or any
others that require asynchronous operation.</p>
<div class="section" id="cuda-in-multiprocessing">
<span id="multiprocessing-cuda-note"></span><h2>CUDA in multiprocessing<a class="headerlink" href="#cuda-in-multiprocessing" title="Permalink to this headline">¶</a></h2>
<p>The CUDA runtime does not support the <code class="docutils literal notranslate"><span class="pre">fork</span></code> start method; either the <code class="docutils literal notranslate"><span class="pre">spawn</span></code> or <code class="docutils literal notranslate"><span class="pre">forkserver</span></code> start method are
required to use CUDA in subprocesses.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>The start method can be set via either creating a context with
<code class="docutils literal notranslate"><span class="pre">multiprocessing.get_context(...)</span></code> or directly using
<code class="docutils literal notranslate"><span class="pre">multiprocessing.set_start_method(...)</span></code>.</p>
</div>
<p>Unlike CPU tensors, the sending process is required to keep the original tensor
as long as the receiving process retains a copy of the tensor. It is implemented
under the hood but requires users to follow the best practices for the program
to run correctly. For example, the sending process must stay alive as long as
the consumer process has references to the tensor, and the refcounting can not
save you if the consumer process exits abnormally via a fatal signal. See
<a class="reference internal" href="../multiprocessing.html#multiprocessing-cuda-sharing-details"><span class="std std-ref">this section</span></a>.</p>
<p>See also: <a class="reference internal" href="cuda.html#cuda-nn-ddp-instead"><span class="std std-ref">Use nn.parallel.DistributedDataParallel instead of multiprocessing or nn.DataParallel</span></a></p>
</div>
<div class="section" id="best-practices-and-tips">
<h2>Best practices and tips<a class="headerlink" href="#best-practices-and-tips" title="Permalink to this headline">¶</a></h2>
<div class="section" id="avoiding-and-fighting-deadlocks">
<h3>Avoiding and fighting deadlocks<a class="headerlink" href="#avoiding-and-fighting-deadlocks" title="Permalink to this headline">¶</a></h3>
<p>There are a lot of things that can go wrong when a new process is spawned, with
the most common cause of deadlocks being background threads. If there’s any
thread that holds a lock or imports a module, and <code class="docutils literal notranslate"><span class="pre">fork</span></code> is called, it’s very
likely that the subprocess will be in a corrupted state and will deadlock or
fail in a different way. Note that even if you don’t, Python built in
libraries do - no need to look further than <a class="reference external" href="https://docs.python.org/3/library/multiprocessing.html#module-multiprocessing" title="(in Python v3.10)"><code class="docutils literal notranslate"><span class="pre">multiprocessing</span></code></a>.
<a class="reference external" href="https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Queue" title="(in Python v3.10)"><code class="docutils literal notranslate"><span class="pre">multiprocessing.Queue</span></code></a> is actually a very complex class, that
spawns multiple threads used to serialize, send and receive objects, and they
can cause aforementioned problems too. If you find yourself in such situation
try using a <code class="xref py py-class docutils literal notranslate"><span class="pre">multiprocessing.queues.SimpleQueue</span></code>, that doesn’t
use any additional threads.</p>
<p>We’re trying our best to make it easy for you and ensure these deadlocks don’t
happen but some things are out of our control. If you have any issues you can’t
cope with for a while, try reaching out on forums, and we’ll see if it’s an
issue we can fix.</p>
</div>
<div class="section" id="reuse-buffers-passed-through-a-queue">
<h3>Reuse buffers passed through a Queue<a class="headerlink" href="#reuse-buffers-passed-through-a-queue" title="Permalink to this headline">¶</a></h3>
<p>Remember that each time you put a <a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">Tensor</span></code></a> into a
<a class="reference external" href="https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Queue" title="(in Python v3.10)"><code class="docutils literal notranslate"><span class="pre">multiprocessing.Queue</span></code></a>, it has to be moved into shared memory.
If it’s already shared, it is a no-op, otherwise it will incur an additional
memory copy that can slow down the whole process. Even if you have a pool of
processes sending data to a single one, make it send the buffers back - this
is nearly free and will let you avoid a copy when sending next batch.</p>
</div>
<div class="section" id="asynchronous-multiprocess-training-e-g-hogwild">
<h3>Asynchronous multiprocess training (e.g. Hogwild)<a class="headerlink" href="#asynchronous-multiprocess-training-e-g-hogwild" title="Permalink to this headline">¶</a></h3>
<p>Using <a class="reference internal" href="../multiprocessing.html#module-torch.multiprocessing" title="torch.multiprocessing"><code class="xref py py-mod docutils literal notranslate"><span class="pre">torch.multiprocessing</span></code></a>, it is possible to train a model
asynchronously, with parameters either shared all the time, or being
periodically synchronized. In the first case, we recommend sending over the whole
model object, while in the latter, we advise to only send the
<a class="reference internal" href="../generated/torch.nn.Module.html#torch.nn.Module.state_dict" title="torch.nn.Module.state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code></a>.</p>
<p>We recommend using <a class="reference external" href="https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Queue" title="(in Python v3.10)"><code class="docutils literal notranslate"><span class="pre">multiprocessing.Queue</span></code></a> for passing all kinds
of PyTorch objects between processes. It is possible to e.g. inherit the tensors
and storages already in shared memory, when using the <code class="docutils literal notranslate"><span class="pre">fork</span></code> start method,
however it is very bug prone and should be used with care, and only by advanced
users. Queues, even though they’re sometimes a less elegant solution, will work
properly in all cases.</p>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>You should be careful about having global statements, that are not guarded
with an <code class="docutils literal notranslate"><span class="pre">if</span> <span class="pre">__name__</span> <span class="pre">==</span> <span class="pre">'__main__'</span></code>. If a different start method than
<code class="docutils literal notranslate"><span class="pre">fork</span></code> is used, they will be executed in all subprocesses.</p>
</div>
<div class="section" id="hogwild">
<h4>Hogwild<a class="headerlink" href="#hogwild" title="Permalink to this headline">¶</a></h4>
<p>A concrete Hogwild implementation can be found in the <a class="reference external" href="https://github.com/pytorch/examples/tree/master/mnist_hogwild">examples repository</a>,
but to showcase the overall structure of the code, there’s also a minimal
example below as well:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch.multiprocessing</span> <span class="k">as</span> <span class="nn">mp</span>
<span class="kn">from</span> <span class="nn">model</span> <span class="kn">import</span> <span class="n">MyModel</span>
<span class="k">def</span> <span class="nf">train</span><span class="p">(</span><span class="n">model</span><span class="p">):</span>
<span class="c1"># Construct data_loader, optimizer, etc.</span>
<span class="k">for</span> <span class="n">data</span><span class="p">,</span> <span class="n">labels</span> <span class="ow">in</span> <span class="n">data_loader</span><span class="p">:</span>
<span class="n">optimizer</span><span class="o">.</span><span class="n">zero_grad</span><span class="p">()</span>
<span class="n">loss_fn</span><span class="p">(</span><span class="n">model</span><span class="p">(</span><span class="n">data</span><span class="p">),</span> <span class="n">labels</span><span class="p">)</span><span class="o">.</span><span class="n">backward</span><span class="p">()</span>
<span class="n">optimizer</span><span class="o">.</span><span class="n">step</span><span class="p">()</span> <span class="c1"># This will update the shared parameters</span>
<span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s1">'__main__'</span><span class="p">:</span>
<span class="n">num_processes</span> <span class="o">=</span> <span class="mi">4</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">MyModel</span><span class="p">()</span>
<span class="c1"># NOTE: this is required for the ``fork`` method to work</span>
<span class="n">model</span><span class="o">.</span><span class="n">share_memory</span><span class="p">()</span>
<span class="n">processes</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">rank</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num_processes</span><span class="p">):</span>
<span class="n">p</span> <span class="o">=</span> <span class="n">mp</span><span class="o">.</span><span class="n">Process</span><span class="p">(</span><span class="n">target</span><span class="o">=</span><span class="n">train</span><span class="p">,</span> <span class="n">args</span><span class="o">=</span><span class="p">(</span><span class="n">model</span><span class="p">,))</span>
<span class="n">p</span><span class="o">.</span><span class="n">start</span><span class="p">()</span>
<span class="n">processes</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">p</span><span class="p">)</span>
<span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">processes</span><span class="p">:</span>
<span class="n">p</span><span class="o">.</span><span class="n">join</span><span class="p">()</span>
</pre></div>
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