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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/amp_examples.html">Automatic Mixed Precision examples</a></li>
<li class="toctree-l1"><a class="reference internal" href="notes/autograd.html">Autograd mechanics</a></li>
<li class="toctree-l1"><a class="reference internal" href="notes/broadcasting.html">Broadcasting semantics</a></li>
<li class="toctree-l1"><a class="reference internal" href="notes/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="notes/large_scale_deployments.html">Features for large-scale deployments</a></li>
<li class="toctree-l1"><a class="reference internal" href="notes/multiprocessing.html">Multiprocessing best practices</a></li>
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<li class="toctree-l1"><a class="reference internal" href="tensorboard.html">torch.utils.tensorboard</a></li>
<li class="toctree-l1"><a class="reference internal" href="type_info.html">Type Info</a></li>
<li class="toctree-l1"><a class="reference internal" href="named_tensor.html">Named Tensors</a></li>
<li class="toctree-l1"><a class="reference internal" href="name_inference.html">Named Tensors operator coverage</a></li>
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<div class="section" id="torch-futures">
<span id="futures-docs"></span><h1>torch.futures<a class="headerlink" href="#torch-futures" title="Permalink to this headline">¶</a></h1>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>The <code class="docutils literal notranslate"><span class="pre">torch.futures</span></code> package is a <strong>Prototype</strong> feature and subject to change.</p>
</div>
<p>This package provides a <a class="reference internal" href="#torch.futures.Future" title="torch.futures.Future"><code class="xref py py-class docutils literal notranslate"><span class="pre">Future</span></code></a> type that encapsulates
an asynchronous execution and a set of utility functions to simplify operations
on <a class="reference internal" href="#torch.futures.Future" title="torch.futures.Future"><code class="xref py py-class docutils literal notranslate"><span class="pre">Future</span></code></a> objects. Currently, the
<a class="reference internal" href="#torch.futures.Future" title="torch.futures.Future"><code class="xref py py-class docutils literal notranslate"><span class="pre">Future</span></code></a> type is primarily used by the
<a class="reference internal" href="rpc.html#distributed-rpc-framework"><span class="std std-ref">Distributed RPC Framework</span></a>.</p>
<span class="target" id="module-torch.futures"></span><dl class="class">
<dt id="torch.futures.Future">
<em class="property">class </em><code class="sig-prename descclassname">torch.futures.</code><code class="sig-name descname">Future</code><a class="headerlink" href="#torch.futures.Future" title="Permalink to this definition">¶</a></dt>
<dd><p>Wrapper around a <code class="docutils literal notranslate"><span class="pre">torch._C.Future</span></code> which encapsulates an asynchronous
execution of a callable, e.g. <a class="reference internal" href="rpc.html#torch.distributed.rpc.rpc_async" title="torch.distributed.rpc.rpc_async"><code class="xref py py-meth docutils literal notranslate"><span class="pre">rpc_async()</span></code></a>. It
also exposes a set of APIs to add callback functions and set results.</p>
<dl class="method">
<dt id="torch.futures.Future.done">
<code class="sig-name descname">done</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → bool<a class="reference internal" href="_modules/torch/futures.html#Future.done"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.futures.Future.done" title="Permalink to this definition">¶</a></dt>
<dd><p>Return <code class="docutils literal notranslate"><span class="pre">True</span></code> if this <code class="docutils literal notranslate"><span class="pre">Future</span></code> is done. A <code class="docutils literal notranslate"><span class="pre">Future</span></code> is done if it
has a result or an exception.</p>
</dd></dl>
<dl class="method">
<dt id="torch.futures.Future.set_result">
<code class="sig-name descname">set_result</code><span class="sig-paren">(</span><em class="sig-param">result: T</em><span class="sig-paren">)</span> → None<a class="reference internal" href="_modules/torch/futures.html#Future.set_result"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.futures.Future.set_result" title="Permalink to this definition">¶</a></dt>
<dd><p>Set the result for this <code class="docutils literal notranslate"><span class="pre">Future</span></code>, which will mark this <code class="docutils literal notranslate"><span class="pre">Future</span></code> as
completed and trigger all attached callbacks. Note that a <code class="docutils literal notranslate"><span class="pre">Future</span></code>
cannot be marked completed twice.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>result</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.9)"><em>object</em></a>) – the result object of this <code class="docutils literal notranslate"><span class="pre">Future</span></code>.</p>
</dd>
</dl>
<dl>
<dt>Example::</dt><dd><div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span> <span class="nn">threading</span>
<span class="gp">>>> </span><span class="kn">import</span> <span class="nn">time</span>
<span class="gp">>>> </span><span class="kn">import</span> <span class="nn">torch</span>
<span class="go">>>></span>
<span class="gp">>>> </span><span class="k">def</span> <span class="nf">slow_set_future</span><span class="p">(</span><span class="n">fut</span><span class="p">,</span> <span class="n">value</span><span class="p">):</span>
<span class="gp">>>> </span> <span class="n">time</span><span class="o">.</span><span class="n">sleep</span><span class="p">(</span><span class="mf">0.5</span><span class="p">)</span>
<span class="gp">>>> </span> <span class="n">fut</span><span class="o">.</span><span class="n">set_result</span><span class="p">(</span><span class="n">value</span><span class="p">)</span>
<span class="go">>>></span>
<span class="gp">>>> </span><span class="n">fut</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">futures</span><span class="o">.</span><span class="n">Future</span><span class="p">()</span>
<span class="gp">>>> </span><span class="n">t</span> <span class="o">=</span> <span class="n">threading</span><span class="o">.</span><span class="n">Thread</span><span class="p">(</span>
<span class="gp">>>> </span> <span class="n">target</span><span class="o">=</span><span class="n">slow_set_future</span><span class="p">,</span>
<span class="gp">>>> </span> <span class="n">args</span><span class="o">=</span><span class="p">(</span><span class="n">fut</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span> <span class="o">*</span> <span class="mi">3</span><span class="p">)</span>
<span class="gp">>>> </span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">t</span><span class="o">.</span><span class="n">start</span><span class="p">()</span>
<span class="go">>>></span>
<span class="gp">>>> </span><span class="nb">print</span><span class="p">(</span><span class="n">fut</span><span class="o">.</span><span class="n">wait</span><span class="p">())</span> <span class="c1"># tensor([3., 3.])</span>
<span class="gp">>>> </span><span class="n">t</span><span class="o">.</span><span class="n">join</span><span class="p">()</span>
</pre></div>
</div>
</dd>
</dl>
</dd></dl>
<dl class="method">
<dt id="torch.futures.Future.then">
<code class="sig-name descname">then</code><span class="sig-paren">(</span><em class="sig-param">callback</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/futures.html#Future.then"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.futures.Future.then" title="Permalink to this definition">¶</a></dt>
<dd><p>Append the given callback function to this <code class="docutils literal notranslate"><span class="pre">Future</span></code>, which will be run
when the <code class="docutils literal notranslate"><span class="pre">Future</span></code> is completed. Multiple callbacks can be added to
the same <code class="docutils literal notranslate"><span class="pre">Future</span></code>, and will be invoked in the same order as they were
added. The callback must take one argument, which is the reference to
this <code class="docutils literal notranslate"><span class="pre">Future</span></code>. The callback function can use the <code class="docutils literal notranslate"><span class="pre">Future.wait()</span></code> API
to get the value.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>callback</strong> (<code class="docutils literal notranslate"><span class="pre">Callable</span></code>) – a <code class="docutils literal notranslate"><span class="pre">Callable</span></code> that takes this <code class="docutils literal notranslate"><span class="pre">Future</span></code> as
the only argument.</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>A new <code class="docutils literal notranslate"><span class="pre">Future</span></code> object that holds the return value of the
<code class="docutils literal notranslate"><span class="pre">callback</span></code> and will be marked as completed when the given
<code class="docutils literal notranslate"><span class="pre">callback</span></code> finishes.</p>
</dd>
</dl>
<dl>
<dt>Example::</dt><dd><div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span> <span class="nn">torch</span>
<span class="go">>>></span>
<span class="gp">>>> </span><span class="k">def</span> <span class="nf">callback</span><span class="p">(</span><span class="n">fut</span><span class="p">):</span>
<span class="gp">>>> </span> <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"RPC return value is </span><span class="si">{</span><span class="n">fut</span><span class="o">.</span><span class="n">wait</span><span class="p">()</span><span class="si">}</span><span class="s2">."</span><span class="p">)</span>
<span class="go">>>></span>
<span class="gp">>>> </span><span class="n">fut</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">futures</span><span class="o">.</span><span class="n">Future</span><span class="p">()</span>
<span class="gp">>>> </span><span class="c1"># The inserted callback will print the return value when</span>
<span class="gp">>>> </span><span class="c1"># receiving the response from "worker1"</span>
<span class="gp">>>> </span><span class="n">cb_fut</span> <span class="o">=</span> <span class="n">fut</span><span class="o">.</span><span class="n">then</span><span class="p">(</span><span class="n">callback</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">chain_cb_fut</span> <span class="o">=</span> <span class="n">cb_fut</span><span class="o">.</span><span class="n">then</span><span class="p">(</span>
<span class="gp">>>> </span> <span class="k">lambda</span> <span class="n">x</span> <span class="p">:</span> <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Chained cb done. </span><span class="si">{</span><span class="n">x</span><span class="o">.</span><span class="n">wait</span><span class="p">()</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="gp">>>> </span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">fut</span><span class="o">.</span><span class="n">set_result</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span>
<span class="go">>>></span>
<span class="gp">>>> </span><span class="c1"># Outputs are:</span>
<span class="gp">>>> </span><span class="c1"># RPC return value is 5.</span>
<span class="gp">>>> </span><span class="c1"># Chained cb done. None</span>
</pre></div>
</div>
</dd>
</dl>
</dd></dl>
<dl class="method">
<dt id="torch.futures.Future.value">
<code class="sig-name descname">value</code><span class="sig-paren">(</span><em class="sig-param">self: torch._C.Future</em><span class="sig-paren">)</span> → object<a class="headerlink" href="#torch.futures.Future.value" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>
<dl class="method">
<dt id="torch.futures.Future.wait">
<code class="sig-name descname">wait</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → T<a class="reference internal" href="_modules/torch/futures.html#Future.wait"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.futures.Future.wait" title="Permalink to this definition">¶</a></dt>
<dd><p>Block until the value of this <code class="docutils literal notranslate"><span class="pre">Future</span></code> is ready.</p>
<dl class="field-list simple">
<dt class="field-odd">Returns</dt>
<dd class="field-odd"><p>The value held by this <code class="docutils literal notranslate"><span class="pre">Future</span></code>. If the function (callback or RPC)
creating the value has thrown an error, this <code class="docutils literal notranslate"><span class="pre">wait</span></code> method will
also throw an error.</p>
</dd>
</dl>
</dd></dl>
</dd></dl>
<dl class="function">
<dt id="torch.futures.collect_all">
<code class="sig-prename descclassname">torch.futures.</code><code class="sig-name descname">collect_all</code><span class="sig-paren">(</span><em class="sig-param">futures: List[torch.jit.Future]</em><span class="sig-paren">)</span> → torch.futures.Future[List[torch.jit.Future]]<a class="reference internal" href="_modules/torch/futures.html#collect_all"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.futures.collect_all" title="Permalink to this definition">¶</a></dt>
<dd><p>Collects the provided <a class="reference internal" href="#torch.futures.Future" title="torch.futures.Future"><code class="xref py py-class docutils literal notranslate"><span class="pre">Future</span></code></a> objects into a single
combined <a class="reference internal" href="#torch.futures.Future" title="torch.futures.Future"><code class="xref py py-class docutils literal notranslate"><span class="pre">Future</span></code></a> that is completed when all of the
sub-futures are completed.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>futures</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.9)"><em>list</em></a>) – a list of <a class="reference internal" href="#torch.futures.Future" title="torch.futures.Future"><code class="xref py py-class docutils literal notranslate"><span class="pre">Future</span></code></a> objects.</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>Returns a <a class="reference internal" href="#torch.futures.Future" title="torch.futures.Future"><code class="xref py py-class docutils literal notranslate"><span class="pre">Future</span></code></a> object to a list of the passed
in Futures.</p>
</dd>
</dl>
<dl>
<dt>Example::</dt><dd><div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span> <span class="nn">torch</span>
<span class="go">>>></span>
<span class="gp">>>> </span><span class="n">fut0</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">futures</span><span class="o">.</span><span class="n">Future</span><span class="p">()</span>
<span class="gp">>>> </span><span class="n">fut1</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">futures</span><span class="o">.</span><span class="n">Future</span><span class="p">()</span>
<span class="go">>>></span>
<span class="gp">>>> </span><span class="n">fut</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">futures</span><span class="o">.</span><span class="n">collect_all</span><span class="p">([</span><span class="n">fut0</span><span class="p">,</span> <span class="n">fut1</span><span class="p">])</span>
<span class="go">>>></span>
<span class="gp">>>> </span><span class="n">fut0</span><span class="o">.</span><span class="n">set_result</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">fut1</span><span class="o">.</span><span class="n">set_result</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
<span class="go">>>></span>
<span class="gp">>>> </span><span class="n">fut_list</span> <span class="o">=</span> <span class="n">fut</span><span class="o">.</span><span class="n">wait</span><span class="p">()</span>
<span class="gp">>>> </span><span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"fut0 result = </span><span class="si">{</span><span class="n">fut_list</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">wait</span><span class="p">()</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="gp">>>> </span><span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"fut1 result = </span><span class="si">{</span><span class="n">fut_list</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">wait</span><span class="p">()</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="gp">>>> </span><span class="c1"># outputs:</span>
<span class="gp">>>> </span><span class="c1"># fut0 result = 0</span>
<span class="gp">>>> </span><span class="c1"># fut1 result = 1</span>
</pre></div>
</div>
</dd>
</dl>
</dd></dl>
<dl class="function">
<dt id="torch.futures.wait_all">
<code class="sig-prename descclassname">torch.futures.</code><code class="sig-name descname">wait_all</code><span class="sig-paren">(</span><em class="sig-param">futures: List[torch.jit.Future]</em><span class="sig-paren">)</span> → List<a class="reference internal" href="_modules/torch/futures.html#wait_all"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.futures.wait_all" title="Permalink to this definition">¶</a></dt>
<dd><p>Waits for all provided futures to be complete, and returns
the list of completed values.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>futures</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.9)"><em>list</em></a>) – a list of <a class="reference internal" href="#torch.futures.Future" title="torch.futures.Future"><code class="xref py py-class docutils literal notranslate"><span class="pre">Future</span></code></a> object.</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>A list of the completed <a class="reference internal" href="#torch.futures.Future" title="torch.futures.Future"><code class="xref py py-class docutils literal notranslate"><span class="pre">Future</span></code></a> results. This
method will throw an error if <code class="docutils literal notranslate"><span class="pre">wait</span></code> on any
<a class="reference internal" href="#torch.futures.Future" title="torch.futures.Future"><code class="xref py py-class docutils literal notranslate"><span class="pre">Future</span></code></a> throws.</p>
</dd>
</dl>
</dd></dl>
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