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<div class="section" id="torch-jit-save">
<h1>torch.jit.save<a class="headerlink" href="#torch-jit-save" title="Permalink to this headline">¶</a></h1>
<dl class="function">
<dt id="torch.jit.save">
<code class="sig-prename descclassname">torch.jit.</code><code class="sig-name descname">save</code><span class="sig-paren">(</span><em class="sig-param">m</em>, <em class="sig-param">f</em>, <em class="sig-param">_extra_files=None</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/jit/_serialization.html#save"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.jit.save" title="Permalink to this definition">¶</a></dt>
<dd><p>Save an offline version of this module for use in a separate process. The
saved module serializes all of the methods, submodules, parameters, and
attributes of this module. It can be loaded into the C++ API using
<code class="docutils literal notranslate"><span class="pre">torch::jit::load(filename)</span></code> or into the Python API with
<a class="reference internal" href="torch.jit.load.html#torch.jit.load" title="torch.jit.load"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.jit.load</span></code></a>.</p>
<p>To be able to save a module, it must not make any calls to native Python
functions. This means that all submodules must be subclasses of
<a class="reference internal" href="torch.jit.ScriptModule.html#torch.jit.ScriptModule" title="torch.jit.ScriptModule"><code class="xref py py-class docutils literal notranslate"><span class="pre">ScriptModule</span></code></a> as well.</p>
<div class="admonition danger">
<p class="admonition-title">Danger</p>
<p>All modules, no matter their device, are always loaded onto the CPU
during loading. This is different from <a class="reference internal" href="torch.load.html#torch.load" title="torch.load"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.load()</span></code></a>’s semantics
and may change in the future.</p>
</div>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>m</strong> – A <a class="reference internal" href="torch.jit.ScriptModule.html#torch.jit.ScriptModule" title="torch.jit.ScriptModule"><code class="xref py py-class docutils literal notranslate"><span class="pre">ScriptModule</span></code></a> to save.</p></li>
<li><p><strong>f</strong> – A file-like object (has to implement write and flush) or a string
containing a file name.</p></li>
<li><p><strong>_extra_files</strong> – Map from filename to contents which will be stored as part of <cite>f</cite>.</p></li>
</ul>
</dd>
</dl>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>torch.jit.save attempts to preserve the behavior of some operators
across versions. For example, dividing two integer tensors in
PyTorch 1.5 performed floor division, and if the module
containing that code is saved in PyTorch 1.5 and loaded in PyTorch 1.6
its division behavior will be preserved. The same module saved in
PyTorch 1.6 will fail to load in PyTorch 1.5, however, since the
behavior of division changed in 1.6, and 1.5 does not know how to
replicate the 1.6 behavior.</p>
</div>
<p>Example:</p>
<div class="highlight-python3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">import</span> <span class="nn">io</span>
<span class="k">class</span> <span class="nc">MyModule</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
<span class="k">return</span> <span class="n">x</span> <span class="o">+</span> <span class="mi">10</span>
<span class="n">m</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">script</span><span class="p">(</span><span class="n">MyModule</span><span class="p">())</span>
<span class="c1"># Save to file</span>
<span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="s1">'scriptmodule.pt'</span><span class="p">)</span>
<span class="c1"># This line is equivalent to the previous</span>
<span class="n">m</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="s2">"scriptmodule.pt"</span><span class="p">)</span>
<span class="c1"># Save to io.BytesIO buffer</span>
<span class="n">buffer</span> <span class="o">=</span> <span class="n">io</span><span class="o">.</span><span class="n">BytesIO</span><span class="p">()</span>
<span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">buffer</span><span class="p">)</span>
<span class="c1"># Save with extra files</span>
<span class="n">extra_files</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'foo.txt'</span><span class="p">:</span> <span class="sa">b</span><span class="s1">'bar'</span><span class="p">}</span>
<span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="s1">'scriptmodule.pt'</span><span class="p">,</span> <span class="n">_extra_files</span><span class="o">=</span><span class="n">extra_files</span><span class="p">)</span>
</pre></div>
</div>
</dd></dl>
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