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<div class="section" id="torch-deploy">
<h1>torch::deploy<a class="headerlink" href="#torch-deploy" title="Permalink to this headline">¶</a></h1>
<p><code class="docutils literal notranslate"><span class="pre">torch::deploy</span></code> is a system that allows you to run multiple embedded Python
interpreters in a C++ process without a shared global interpreter lock. For more
information on how <code class="docutils literal notranslate"><span class="pre">torch::deploy</span></code> works internally, please see the related
<a class="reference external" href="https://arxiv.org/pdf/2104.00254.pdf">arXiv paper</a>.</p>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>This is a prototype feature. Only Linux x86 is supported, and the API may
change without warning.</p>
</div>
<div class="section" id="getting-started">
<h2>Getting Started<a class="headerlink" href="#getting-started" title="Permalink to this headline">¶</a></h2>
<div class="section" id="installing-torch-deploy">
<h3>Installing <code class="docutils literal notranslate"><span class="pre">torch::deploy</span></code><a class="headerlink" href="#installing-torch-deploy" title="Permalink to this headline">¶</a></h3>
<p><code class="docutils literal notranslate"><span class="pre">torch::deploy</span></code> is not yet built by default in our binary releases, so to get
a copy of libtorch with <code class="docutils literal notranslate"><span class="pre">torch::deploy</span></code> enabled, follow the instructions for
<a class="reference external" href="https://github.com/pytorch/pytorch/#from-source">building PyTorch from source</a>.</p>
<p>When running <code class="docutils literal notranslate"><span class="pre">setup.py</span></code>, you will need to specify <code class="docutils literal notranslate"><span class="pre">USE_DEPLOY=1</span></code>, like:</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="nb">export</span> <span class="nv">CMAKE_PREFIX_PATH</span><span class="o">=</span><span class="si">${</span><span class="nv">CONDA_PREFIX</span><span class="k">:-</span><span class="s2">"</span><span class="k">$(</span>dirname <span class="k">$(</span>which conda<span class="k">))</span><span class="s2">/../"</span><span class="si">}</span>
<span class="nb">export</span> <span class="nv">USE_DEPLOY</span><span class="o">=</span><span class="m">1</span>
python setup.py develop
</pre></div>
</div>
</div>
<div class="section" id="creating-a-model-package-in-python">
<h3>Creating a model package in Python<a class="headerlink" href="#creating-a-model-package-in-python" title="Permalink to this headline">¶</a></h3>
<p><code class="docutils literal notranslate"><span class="pre">torch::deploy</span></code> can load and run Python models that are packaged with
<code class="docutils literal notranslate"><span class="pre">torch.package</span></code>. You can learn more about <code class="docutils literal notranslate"><span class="pre">torch.package</span></code> in the
<code class="docutils literal notranslate"><span class="pre">torch.package</span></code> <a class="reference external" href="https://pytorch.org/docs/stable/package.html#tutorials">documentation</a>.</p>
<p>For now, let’s create a simple model that we can load and run in <code class="docutils literal notranslate"><span class="pre">torch::deploy</span></code>.</p>
<div class="highlight-py notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">torch.package</span> <span class="kn">import</span> <span class="n">PackageExporter</span>
<span class="kn">import</span> <span class="nn">torchvision</span>
<span class="c1"># Instantiate some model</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">torchvision</span><span class="o">.</span><span class="n">models</span><span class="o">.</span><span class="n">resnet</span><span class="o">.</span><span class="n">resnet18</span><span class="p">()</span>
<span class="c1"># Package and export it.</span>
<span class="k">with</span> <span class="n">PackageExporter</span><span class="p">(</span><span class="s2">"my_package.pt"</span><span class="p">)</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span>
<span class="n">e</span><span class="o">.</span><span class="n">intern</span><span class="p">(</span><span class="s2">"torchvision.**"</span><span class="p">)</span>
<span class="n">e</span><span class="o">.</span><span class="n">extern</span><span class="p">(</span><span class="s2">"numpy.**"</span><span class="p">)</span>
<span class="n">e</span><span class="o">.</span><span class="n">extern</span><span class="p">(</span><span class="s2">"sys"</span><span class="p">)</span>
<span class="n">e</span><span class="o">.</span><span class="n">extern</span><span class="p">(</span><span class="s2">"PIL.*"</span><span class="p">)</span>
<span class="n">e</span><span class="o">.</span><span class="n">save_pickle</span><span class="p">(</span><span class="s2">"model"</span><span class="p">,</span> <span class="s2">"model.pkl"</span><span class="p">,</span> <span class="n">model</span><span class="p">)</span>
</pre></div>
</div>
<p>Note that since “numpy”, “sys” and “PIL” were marked as “extern”, <cite>torch.package</cite> will
look for these dependencies on the system that loads this package. They will not be packaged
with the model.</p>
<p>Now, there should be a file named <code class="docutils literal notranslate"><span class="pre">my_package.pt</span></code> in your working directory.</p>
</div>
<div class="section" id="loading-and-running-the-model-in-c">
<h3>Loading and running the model in C++<a class="headerlink" href="#loading-and-running-the-model-in-c" title="Permalink to this headline">¶</a></h3>
<p>Set an environment variable (e.g. $PATH_TO_EXTERN_PYTHON_PACKAGES) to indicate to the interpreters
where the external Python dependencies can be found. In the example below, the path to the
site-packages of a conda environment is provided.</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="nb">export</span> <span class="nv">PATH_TO_EXTERN_PYTHON_PACKAGES</span><span class="o">=</span> <span class="se">\</span>
<span class="s2">"~/anaconda/envs/deploy-example-env/lib/python3.8/site-packages"</span>
</pre></div>
</div>
<p>Let’s create a minimal C++ program to that loads the model.</p>
<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="cp">#include</span><span class="w"> </span><span class="cpf"><torch/csrc/deploy/deploy.h></span><span class="cp"></span>
<span class="cp">#include</span><span class="w"> </span><span class="cpf"><torch/csrc/deploy/path_environment.h></span><span class="cp"></span>
<span class="cp">#include</span><span class="w"> </span><span class="cpf"><torch/script.h></span><span class="cp"></span>
<span class="cp">#include</span><span class="w"> </span><span class="cpf"><torch/torch.h></span><span class="cp"></span>
<span class="cp">#include</span><span class="w"> </span><span class="cpf"><iostream></span><span class="cp"></span>
<span class="cp">#include</span><span class="w"> </span><span class="cpf"><memory></span><span class="cp"></span>
<span class="kt">int</span><span class="w"> </span><span class="nf">main</span><span class="p">(</span><span class="kt">int</span><span class="w"> </span><span class="n">argc</span><span class="p">,</span><span class="w"> </span><span class="k">const</span><span class="w"> </span><span class="kt">char</span><span class="o">*</span><span class="w"> </span><span class="n">argv</span><span class="p">[])</span><span class="w"> </span><span class="p">{</span><span class="w"></span>
<span class="w"> </span><span class="k">if</span><span class="w"> </span><span class="p">(</span><span class="n">argc</span><span class="w"> </span><span class="o">!=</span><span class="w"> </span><span class="mi">2</span><span class="p">)</span><span class="w"> </span><span class="p">{</span><span class="w"></span>
<span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">cerr</span><span class="w"> </span><span class="o"><<</span><span class="w"> </span><span class="s">"usage: example-app <path-to-exported-script-module></span><span class="se">\n</span><span class="s">"</span><span class="p">;</span><span class="w"></span>
<span class="w"> </span><span class="k">return</span><span class="w"> </span><span class="mi">-1</span><span class="p">;</span><span class="w"></span>
<span class="w"> </span><span class="p">}</span><span class="w"></span>
<span class="w"> </span><span class="c1">// Start an interpreter manager governing 4 embedded interpreters.</span>
<span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">shared_ptr</span><span class="o"><</span><span class="n">torch</span><span class="o">::</span><span class="n">deploy</span><span class="o">::</span><span class="n">Environment</span><span class="o">></span><span class="w"> </span><span class="n">env</span><span class="w"> </span><span class="o">=</span><span class="w"></span>
<span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">make_shared</span><span class="o"><</span><span class="n">torch</span><span class="o">::</span><span class="n">deploy</span><span class="o">::</span><span class="n">PathEnvironment</span><span class="o">></span><span class="p">(</span><span class="w"></span>
<span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">getenv</span><span class="p">(</span><span class="s">"PATH_TO_EXTERN_PYTHON_PACKAGES"</span><span class="p">)</span><span class="w"></span>
<span class="w"> </span><span class="p">);</span><span class="w"></span>
<span class="w"> </span><span class="n">torch</span><span class="o">::</span><span class="n">deploy</span><span class="o">::</span><span class="n">InterpreterManager</span><span class="w"> </span><span class="n">manager</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span><span class="w"> </span><span class="n">env</span><span class="p">);</span><span class="w"></span>
<span class="w"> </span><span class="k">try</span><span class="w"> </span><span class="p">{</span><span class="w"></span>
<span class="w"> </span><span class="c1">// Load the model from the torch.package.</span>
<span class="w"> </span><span class="n">torch</span><span class="o">::</span><span class="n">deploy</span><span class="o">::</span><span class="n">Package</span><span class="w"> </span><span class="n">package</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">manager</span><span class="p">.</span><span class="n">loadPackage</span><span class="p">(</span><span class="n">argv</span><span class="p">[</span><span class="mi">1</span><span class="p">]);</span><span class="w"></span>
<span class="w"> </span><span class="n">torch</span><span class="o">::</span><span class="n">deploy</span><span class="o">::</span><span class="n">ReplicatedObj</span><span class="w"> </span><span class="n">model</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">package</span><span class="p">.</span><span class="n">loadPickle</span><span class="p">(</span><span class="s">"model"</span><span class="p">,</span><span class="w"> </span><span class="s">"model.pkl"</span><span class="p">);</span><span class="w"></span>
<span class="w"> </span><span class="p">}</span><span class="w"> </span><span class="k">catch</span><span class="w"> </span><span class="p">(</span><span class="k">const</span><span class="w"> </span><span class="n">c10</span><span class="o">::</span><span class="n">Error</span><span class="o">&</span><span class="w"> </span><span class="n">e</span><span class="p">)</span><span class="w"> </span><span class="p">{</span><span class="w"></span>
<span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">cerr</span><span class="w"> </span><span class="o"><<</span><span class="w"> </span><span class="s">"error loading the model</span><span class="se">\n</span><span class="s">"</span><span class="p">;</span><span class="w"></span>
<span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">cerr</span><span class="w"> </span><span class="o"><<</span><span class="w"> </span><span class="n">e</span><span class="p">.</span><span class="n">msg</span><span class="p">();</span><span class="w"></span>
<span class="w"> </span><span class="k">return</span><span class="w"> </span><span class="mi">-1</span><span class="p">;</span><span class="w"></span>
<span class="w"> </span><span class="p">}</span><span class="w"></span>
<span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">cout</span><span class="w"> </span><span class="o"><<</span><span class="w"> </span><span class="s">"ok</span><span class="se">\n</span><span class="s">"</span><span class="p">;</span><span class="w"></span>
<span class="p">}</span><span class="w"></span>
</pre></div>
</div>
<p>This small program introduces many of the core concepts of <code class="docutils literal notranslate"><span class="pre">torch::deploy</span></code>.</p>
<p>An <code class="docutils literal notranslate"><span class="pre">InterpreterManager</span></code> abstracts over a collection of independent Python
interpreters, allowing you to load balance across them when running your code.</p>
<p><code class="docutils literal notranslate"><span class="pre">PathEnvironment</span></code> enables you to specify the location of Python
packages on your system which are external, but necessary, for your model.</p>
<p>Using the <code class="docutils literal notranslate"><span class="pre">InterpreterManager::loadPackage</span></code> method, you can load a
<code class="docutils literal notranslate"><span class="pre">torch.package</span></code> from disk and make it available to all interpreters.</p>
<p><code class="docutils literal notranslate"><span class="pre">Package::loadPickle</span></code> allows you to retrieve specific Python objects
from the package, like the ResNet model we saved earlier.</p>
<p>Finally, the model itself is a <code class="docutils literal notranslate"><span class="pre">ReplicatedObj</span></code>. This is an abstract handle to
an object that is replicated across multiple interpreters. When you interact
with a <code class="docutils literal notranslate"><span class="pre">ReplicatedObj</span></code> (for example, by calling <code class="docutils literal notranslate"><span class="pre">forward</span></code>), it will select
an free interpreter to execute that interaction.</p>
</div>
<div class="section" id="building-and-running-the-application">
<h3>Building and running the application<a class="headerlink" href="#building-and-running-the-application" title="Permalink to this headline">¶</a></h3>
<p>Locate <cite>libtorch_deployinterpreter.o</cite> on your system. This should have been
built when PyTorch was built from source. In the same PyTorch directory, locate
the deploy source files. Set these locations to an environment variable for the build.
An example of where these can be found on a system is shown below.</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="nb">export</span> <span class="nv">DEPLOY_INTERPRETER_PATH</span><span class="o">=</span><span class="s2">"/pytorch/build/torch/csrc/deploy/"</span>
<span class="nb">export</span> <span class="nv">DEPLOY_SRC_PATH</span><span class="o">=</span><span class="s2">"/pytorch/torch/csrc/deploy/"</span>
</pre></div>
</div>
<p>As <code class="docutils literal notranslate"><span class="pre">torch::deploy</span></code> is in active development, these manual steps will be removed
soon.</p>
<p>Assuming the above C++ program was stored in a file called, <cite>example-app.cpp</cite>, a
minimal CMakeLists.txt file would look like:</p>
<div class="highlight-cmake notranslate"><div class="highlight"><pre><span></span><span class="nb">cmake_minimum_required</span><span class="p">(</span><span class="s">VERSION</span><span class="w"> </span><span class="s">3.19</span><span class="w"> </span><span class="s">FATAL_ERROR</span><span class="p">)</span>
<span class="nb">project</span><span class="p">(</span><span class="s">deploy_tutorial</span><span class="p">)</span>
<span class="nb">find_package</span><span class="p">(</span><span class="s">fmt</span><span class="w"> </span><span class="s">REQUIRED</span><span class="p">)</span>
<span class="nb">find_package</span><span class="p">(</span><span class="s">Torch</span><span class="w"> </span><span class="s">REQUIRED</span><span class="p">)</span>
<span class="nb">add_library</span><span class="p">(</span><span class="s">torch_deploy_internal</span><span class="w"> </span><span class="s">STATIC</span>
<span class="w"> </span><span class="o">${</span><span class="nv">DEPLOY_INTERPRETER_PATH</span><span class="o">}</span><span class="s">/libtorch_deployinterpreter.o</span>
<span class="w"> </span><span class="o">${</span><span class="nv">DEPLOY_DIR</span><span class="o">}</span><span class="s">/deploy.cpp</span>
<span class="w"> </span><span class="o">${</span><span class="nv">DEPLOY_DIR</span><span class="o">}</span><span class="s">/loader.cpp</span>
<span class="w"> </span><span class="o">${</span><span class="nv">DEPLOY_DIR</span><span class="o">}</span><span class="s">/path_environment.cpp</span>
<span class="w"> </span><span class="o">${</span><span class="nv">DEPLOY_DIR</span><span class="o">}</span><span class="s">/elf_file.cpp</span><span class="p">)</span>
<span class="c"># for python builtins</span>
<span class="nb">target_link_libraries</span><span class="p">(</span><span class="s">torch_deploy_internal</span><span class="w"> </span><span class="s">PRIVATE</span>
<span class="w"> </span><span class="s">crypt</span><span class="w"> </span><span class="s">pthread</span><span class="w"> </span><span class="s">dl</span><span class="w"> </span><span class="s">util</span><span class="w"> </span><span class="s">m</span><span class="w"> </span><span class="s">z</span><span class="w"> </span><span class="s">ffi</span><span class="w"> </span><span class="s">lzma</span><span class="w"> </span><span class="s">readline</span><span class="w"> </span><span class="s">nsl</span><span class="w"> </span><span class="s">ncursesw</span><span class="w"> </span><span class="s">panelw</span><span class="p">)</span>
<span class="nb">target_link_libraries</span><span class="p">(</span><span class="s">torch_deploy_internal</span><span class="w"> </span><span class="s">PUBLIC</span>
<span class="w"> </span><span class="s">shm</span><span class="w"> </span><span class="s">torch</span><span class="w"> </span><span class="s">fmt::fmt-header-only</span><span class="p">)</span>
<span class="nb">caffe2_interface_library</span><span class="p">(</span><span class="s">torch_deploy_internal</span><span class="w"> </span><span class="s">torch_deploy</span><span class="p">)</span>
<span class="nb">add_executable</span><span class="p">(</span><span class="s">example-app</span><span class="w"> </span><span class="s">example.cpp</span><span class="p">)</span>
<span class="nb">target_link_libraries</span><span class="p">(</span><span class="s">example-app</span><span class="w"> </span><span class="s">PUBLIC</span>
<span class="w"> </span><span class="s2">"-Wl,--no-as-needed -rdynamic"</span><span class="w"> </span><span class="s">dl</span><span class="w"> </span><span class="s">torch_deploy</span><span class="w"> </span><span class="s2">"${TORCH_LIBRARIES}"</span><span class="p">)</span>
</pre></div>
</div>
<p>Currently, it is necessary to build <code class="docutils literal notranslate"><span class="pre">torch::deploy</span></code> as a static library.
In order to correctly link to a static library, the utility <code class="docutils literal notranslate"><span class="pre">caffe2_interface_library</span></code>
is used to appropriately set and unset <code class="docutils literal notranslate"><span class="pre">--whole-archive</span></code> flag.</p>
<p>Furthermore, the <code class="docutils literal notranslate"><span class="pre">-rdynamic</span></code> flag is needed when linking to the executable
to ensure that symbols are exported to the dynamic table, making them accessible
to the deploy interpreters (which are dynamically loaded).</p>
<p>The last step is configuring and building the project. Assuming that our code
directory is laid out like this:</p>
<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>example-app/
CMakeLists.txt
example-app.cpp
</pre></div>
</div>
<p>We can now run the following commands to build the application from within the
<code class="docutils literal notranslate"><span class="pre">example-app/</span></code> folder:</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>mkdir build
<span class="nb">cd</span> build
<span class="c1"># Point CMake at the built version of PyTorch we just installed.</span>
cmake -DCMAKE_PREFIX_PATH<span class="o">=</span><span class="s2">"</span><span class="k">$(</span>python -c <span class="s1">'import torch.utils; print(torch.utils.cmake_prefix_path)'</span><span class="k">)</span><span class="s2">"</span> .. <span class="se">\</span>
-DDEPLOY_INTERPRETER_PATH<span class="o">=</span><span class="s2">"</span><span class="nv">$DEPLOY_INTERPRETER_PATH</span><span class="s2">"</span> <span class="se">\</span>
-DDEPLOY_DIR<span class="o">=</span><span class="s2">"</span><span class="nv">$DEPLOY_DIR</span><span class="s2">"</span>
cmake --build . --config Release
</pre></div>
</div>
<p>Now we can run our app:</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>./example-app /path/to/my_package.pt
</pre></div>
</div>
</div>
<div class="section" id="executing-forward-in-c">
<h3>Executing <code class="docutils literal notranslate"><span class="pre">forward</span></code> in C++<a class="headerlink" href="#executing-forward-in-c" title="Permalink to this headline">¶</a></h3>
<p>One you have your model loaded in C++, it is easy to execute it:</p>
<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="c1">// Create a vector of inputs.</span>
<span class="n">std</span><span class="o">::</span><span class="n">vector</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">IValue</span><span class="o">></span><span class="w"> </span><span class="n">inputs</span><span class="p">;</span><span class="w"></span>
<span class="n">inputs</span><span class="p">.</span><span class="n">push_back</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">1</span><span class="p">,</span><span class="w"> </span><span class="mi">3</span><span class="p">,</span><span class="w"> </span><span class="mi">224</span><span class="p">,</span><span class="w"> </span><span class="mi">224</span><span class="p">}));</span><span class="w"></span>
<span class="c1">// Execute the model and turn its output into a tensor.</span>
<span class="n">at</span><span class="o">::</span><span class="n">Tensor</span><span class="w"> </span><span class="n">output</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">model</span><span class="p">(</span><span class="n">inputs</span><span class="p">).</span><span class="n">toTensor</span><span class="p">();</span><span class="w"></span>
<span class="n">std</span><span class="o">::</span><span class="n">cout</span><span class="w"> </span><span class="o"><<</span><span class="w"> </span><span class="n">output</span><span class="p">.</span><span class="n">slice</span><span class="p">(</span><span class="cm">/*dim=*/</span><span class="mi">1</span><span class="p">,</span><span class="w"> </span><span class="cm">/*start=*/</span><span class="mi">0</span><span class="p">,</span><span class="w"> </span><span class="cm">/*end=*/</span><span class="mi">5</span><span class="p">)</span><span class="w"> </span><span class="o"><<</span><span class="w"> </span><span class="sc">'\n'</span><span class="p">;</span><span class="w"></span>
</pre></div>
</div>
<p>Notably, the model’s forward function is executing in Python, in an embedded
CPython interpreter. Note that the model is a <code class="docutils literal notranslate"><span class="pre">ReplicatedObj</span></code>, which means
that you can call <code class="docutils literal notranslate"><span class="pre">model()</span></code> from multiple threads and the forward method will
be executed on multiple independent interpreters, with no global interpreter
lock.</p>
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