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<!DOCTYPE html>
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<title>torch.multiprocessing — PyTorch 0.1.11 documentation</title>
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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>
<li class="toctree-l2"><a class="reference internal" href="../../notes/autograd.html#excluding-subgraphs-from-backward">Excluding subgraphs from backward</a><ul>
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<li class="toctree-l3"><a class="reference internal" href="../../notes/cuda.html#use-pinned-memory-buffers">Use pinned memory buffers</a></li>
<li class="toctree-l3"><a class="reference internal" href="../../notes/cuda.html#use-nn-dataparallel-instead-of-multiprocessing">Use nn.DataParallel instead of multiprocessing</a></li>
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<p class="caption"><span class="caption-text">Package Reference</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../../torch.html">torch</a><ul>
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<li class="toctree-l2"><a class="reference internal" href="../../nn.html#recurrent-layers">Recurrent layers</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../../nn.html#rnn"><span class="hidden-section">RNN</span></a></li>
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<li class="toctree-l2"><a class="reference internal" href="../../nn.html#loss-functions">Loss functions</a><ul>
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<h1>Source code for torch.multiprocessing</h1><div class="highlight"><pre>
<span></span><span class="sd">"""</span>
<span class="sd">torch.multiprocessing is a wrapper around the native :mod:`multiprocessing`</span>
<span class="sd">module. It registers custom reducers, that use shared memory to provide shared</span>
<span class="sd">views on the same data in different processes. Once the tensor/storage is moved</span>
<span class="sd">to shared_memory (see :func:`~torch.Tensor.share_memory_`), it will be possible</span>
<span class="sd">to send it to other processes without making any copies.</span>
<span class="sd">The API is 100% compatible with the original module - it's enough to change</span>
<span class="sd">``import multiprocessing`` to ``import torch.multiprocessing`` to have all the</span>
<span class="sd">tensors sent through the queues or shared via other mechanisms, moved to shared</span>
<span class="sd">memory.</span>
<span class="sd">Because of the similarity of APIs we do not document most of this package</span>
<span class="sd">contents, and we recommend referring to very good docs of the original module.</span>
<span class="sd">"""</span>
<span class="kn">import</span> <span class="nn">sys</span>
<span class="kn">from</span> <span class="nn">.reductions</span> <span class="k">import</span> <span class="n">init_reductions</span>
<span class="kn">import</span> <span class="nn">multiprocessing</span>
<span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'set_sharing_strategy'</span><span class="p">,</span> <span class="s1">'get_sharing_strategy'</span><span class="p">,</span>
<span class="s1">'get_all_sharing_strategies'</span><span class="p">]</span>
<span class="kn">from</span> <span class="nn">multiprocessing</span> <span class="k">import</span> <span class="o">*</span>
<span class="n">__all__</span> <span class="o">+=</span> <span class="n">multiprocessing</span><span class="o">.</span><span class="n">__all__</span>
<span class="k">if</span> <span class="n">sys</span><span class="o">.</span><span class="n">version_info</span> <span class="o"><</span> <span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">):</span>
<span class="sd">"""Override basic classes in Python 2.7 and Python 3.3 to use ForkingPickler</span>
<span class="sd"> for serialization. Later versions of Python already use ForkingPickler."""</span>
<span class="kn">from</span> <span class="nn">.queue</span> <span class="k">import</span> <span class="n">Queue</span><span class="p">,</span> <span class="n">SimpleQueue</span>
<span class="kn">from</span> <span class="nn">.pool</span> <span class="k">import</span> <span class="n">Pool</span>
<span class="k">if</span> <span class="n">sys</span><span class="o">.</span><span class="n">platform</span> <span class="o">==</span> <span class="s1">'darwin'</span><span class="p">:</span>
<span class="n">_sharing_strategy</span> <span class="o">=</span> <span class="s1">'file_system'</span>
<span class="n">_all_sharing_strategies</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'file_system'</span><span class="p">}</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">_sharing_strategy</span> <span class="o">=</span> <span class="s1">'file_descriptor'</span>
<span class="n">_all_sharing_strategies</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'file_descriptor'</span><span class="p">,</span> <span class="s1">'file_system'</span><span class="p">}</span>
<div class="viewcode-block" id="set_sharing_strategy"><a class="viewcode-back" href="../../multiprocessing.html#torch.multiprocessing.set_sharing_strategy">[docs]</a><span class="k">def</span> <span class="nf">set_sharing_strategy</span><span class="p">(</span><span class="n">new_strategy</span><span class="p">):</span>
<span class="sd">"""Sets the strategy for sharing CPU tensors.</span>
<span class="sd"> Arguments:</span>
<span class="sd"> new_strategy (str): Name of the selected strategy. Should be one of</span>
<span class="sd"> the values returned by :func:`get_all_sharing_strategies()`.</span>
<span class="sd"> """</span>
<span class="k">global</span> <span class="n">_sharing_strategy</span>
<span class="k">assert</span> <span class="n">new_strategy</span> <span class="ow">in</span> <span class="n">_all_sharing_strategies</span>
<span class="n">_sharing_strategy</span> <span class="o">=</span> <span class="n">new_strategy</span></div>
<div class="viewcode-block" id="get_sharing_strategy"><a class="viewcode-back" href="../../multiprocessing.html#torch.multiprocessing.get_sharing_strategy">[docs]</a><span class="k">def</span> <span class="nf">get_sharing_strategy</span><span class="p">():</span>
<span class="sd">"""Returns the current strategy for sharing CPU tensors."""</span>
<span class="k">return</span> <span class="n">_sharing_strategy</span></div>
<div class="viewcode-block" id="get_all_sharing_strategies"><a class="viewcode-back" href="../../multiprocessing.html#torch.multiprocessing.get_all_sharing_strategies">[docs]</a><span class="k">def</span> <span class="nf">get_all_sharing_strategies</span><span class="p">():</span>
<span class="sd">"""Returns a set of sharing strategies supported on a current system."""</span>
<span class="k">return</span> <span class="n">_all_sharing_strategies</span></div>
<span class="n">init_reductions</span><span class="p">()</span>
</pre></div>
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