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<!DOCTYPE html>
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<p class="caption"><span class="caption-text">Notes</span></p>
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<li class="toctree-l1"><a class="reference internal" href="notes/autograd.html">Autograd mechanics</a><ul>
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<p class="caption"><span class="caption-text">Package Reference</span></p>
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<div class="section" id="module-torch.multiprocessing">
<span id="multiprocessing-package-torch-multiprocessing"></span><h1>Multiprocessing package - torch.multiprocessing<a class="headerlink" href="#module-torch.multiprocessing" title="Permalink to this headline">¶</a></h1>
<p>torch.multiprocessing is a wrapper around the native <a class="reference external" href="https://docs.python.org/2/library/multiprocessing.html#module-multiprocessing" title="(in Python v2.7)"><code class="xref py py-mod docutils literal"><span class="pre">multiprocessing</span></code></a>
module. It registers custom reducers, that use shared memory to provide shared
views on the same data in different processes. Once the tensor/storage is moved
to shared_memory (see <a class="reference internal" href="tensors.html#torch.Tensor.share_memory_" title="torch.Tensor.share_memory_"><code class="xref py py-func docutils literal"><span class="pre">share_memory_()</span></code></a>), it will be possible
to send it to other processes without making any copies.</p>
<p>The API is 100% compatible with the original module - it’s enough to change
<code class="docutils literal"><span class="pre">import</span> <span class="pre">multiprocessing</span></code> to <code class="docutils literal"><span class="pre">import</span> <span class="pre">torch.multiprocessing</span></code> to have all the
tensors sent through the queues or shared via other mechanisms, moved to shared
memory.</p>
<p>Because of the similarity of APIs we do not document most of this package
contents, and we recommend referring to very good docs of the original module.</p>
<div class="admonition warning">
<p class="first admonition-title">Warning</p>
<p class="last">If the main process exits abruptly (e.g. because of an incoming signal),
Python’s <code class="docutils literal"><span class="pre">multiprocessing</span></code> sometimes fails to clean up its children.
It’s a known caveat, so if you’re seeing any resource leaks after
interrupting the interpreter, it probably means that this has just happened
to you.</p>
</div>
<div class="section" id="strategy-management">
<h2>Strategy management<a class="headerlink" href="#strategy-management" title="Permalink to this headline">¶</a></h2>
<dl class="function">
<dt id="torch.multiprocessing.get_all_sharing_strategies">
<code class="descclassname">torch.multiprocessing.</code><code class="descname">get_all_sharing_strategies</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/multiprocessing.html#get_all_sharing_strategies"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.multiprocessing.get_all_sharing_strategies" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns a set of sharing strategies supported on a current system.</p>
</dd></dl>
<dl class="function">
<dt id="torch.multiprocessing.get_sharing_strategy">
<code class="descclassname">torch.multiprocessing.</code><code class="descname">get_sharing_strategy</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/multiprocessing.html#get_sharing_strategy"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.multiprocessing.get_sharing_strategy" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns the current strategy for sharing CPU tensors.</p>
</dd></dl>
<dl class="function">
<dt id="torch.multiprocessing.set_sharing_strategy">
<code class="descclassname">torch.multiprocessing.</code><code class="descname">set_sharing_strategy</code><span class="sig-paren">(</span><em>new_strategy</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/multiprocessing.html#set_sharing_strategy"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.multiprocessing.set_sharing_strategy" title="Permalink to this definition">¶</a></dt>
<dd><p>Sets the strategy for sharing CPU tensors.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>new_strategy</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#str" title="(in Python v2.7)"><em>str</em></a>) – Name of the selected strategy. Should be one of
the values returned by <a class="reference internal" href="#torch.multiprocessing.get_all_sharing_strategies" title="torch.multiprocessing.get_all_sharing_strategies"><code class="xref py py-func docutils literal"><span class="pre">get_all_sharing_strategies()</span></code></a>.</td>
</tr>
</tbody>
</table>
</dd></dl>
</div>
<div class="section" id="sharing-cuda-tensors">
<h2>Sharing CUDA tensors<a class="headerlink" href="#sharing-cuda-tensors" title="Permalink to this headline">¶</a></h2>
<p>Sharing CUDA tensors between processes is supported only in Python 3, using
a <code class="docutils literal"><span class="pre">spawn</span></code> or <code class="docutils literal"><span class="pre">forkserver</span></code> start methods. <a class="reference external" href="https://docs.python.org/2/library/multiprocessing.html#module-multiprocessing" title="(in Python v2.7)"><code class="docutils literal"><span class="pre">multiprocessing</span></code></a> in
Python 2 can only create subprocesses using <code class="docutils literal"><span class="pre">fork</span></code>, and it’s not supported
by the CUDA runtime.</p>
<div class="admonition warning">
<p class="first admonition-title">Warning</p>
<p class="last">CUDA API requires that the allocation exported to other processes remains
valid as long as it’s used by them. You should be careful and ensure that
CUDA tensors you shared don’t go out of scope as long as it’s necessary.
This shouldn’t be a problem for sharing model parameters, but passing other
kinds of data should be done with care. Note that this restriction doesn’t
apply to shared CPU memory.</p>
</div>
</div>
<div class="section" id="sharing-strategies">
<h2>Sharing strategies<a class="headerlink" href="#sharing-strategies" title="Permalink to this headline">¶</a></h2>
<p>This section provides a brief overview into how different sharing strategies
work. Note that it applies only to CPU tensor - CUDA tensors will always use
the CUDA API, as that’s the only way they can be shared.</p>
<div class="section" id="file-descriptor-file-descriptor">
<h3>File descriptor - <code class="docutils literal"><span class="pre">file_descriptor</span></code><a class="headerlink" href="#file-descriptor-file-descriptor" title="Permalink to this headline">¶</a></h3>
<div class="admonition note">
<p class="first admonition-title">Note</p>
<p class="last">This is the default strategy (except for macOS and OS X where it’s not
supported).</p>
</div>
<p>This strategy will use file descriptors as shared memory handles. Whenever a
storage is moved to shared memory, a file descriptor obtained from <code class="docutils literal"><span class="pre">shm_open</span></code>
is cached with the object, and when it’s going to be sent to other processes,
the file descriptor will be transferred (e.g. via UNIX sockets) to it. The
receiver will also cache the file descriptor and <code class="docutils literal"><span class="pre">mmap</span></code> it, to obtain a shared
view onto the storage data.</p>
<p>Note that if there will be a lot of tensors shared, this strategy will keep a
large number of file descriptors open most of the time. If your system has low
limits for the number of open file descriptors, and you can’t rise them, you
should use the <code class="docutils literal"><span class="pre">file_system</span></code> strategy.</p>
</div>
<div class="section" id="file-system-file-system">
<h3>File system - <code class="docutils literal"><span class="pre">file_system</span></code><a class="headerlink" href="#file-system-file-system" title="Permalink to this headline">¶</a></h3>
<p>This strategy will use file names given to <code class="docutils literal"><span class="pre">shm_open</span></code> to identify the shared
memory regions. This has a benefit of not requiring the implementation to cache
the file descriptors obtained from it, but at the same time is prone to shared
memory leaks. The file can’t be deleted right after its creation, because other
processes need to access it to open their views. If the processes fatally
crash, or are killed, and don’t call the storage destructors, the files will
remain in the system. This is very serious, because they keep using up the
memory until the system is restarted, or they’re freed manually.</p>
<p>To counter the problem of shared memory file leaks, <a class="reference internal" href="#module-torch.multiprocessing" title="torch.multiprocessing"><code class="xref py py-mod docutils literal"><span class="pre">torch.multiprocessing</span></code></a>
will spawn a daemon named <code class="docutils literal"><span class="pre">torch_shm_manager</span></code> that will isolate itself from
the current process group, and will keep track of all shared memory allocations.
Once all processes connected to it exit, it will wait a moment to ensure there
will be no new connections, and will iterate over all shared memory files
allocated by the group. If it finds that any of them still exist, they will be
deallocated. We’ve tested this method and it prooved to be robust to various
failures. Still, if your system has high enough limits, and <code class="docutils literal"><span class="pre">file_descriptor</span></code>
is a supported strategy, we do not recommend switching to this one.</p>
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