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   <div class="sphx-glr-download-link-note admonition note">
 <p class="admonition-title">Note</p>
 <p>This tutorial can be used interactively with Google Colab! You can also click
 <a class="reference internal" href="#sphx-glr-download-how-to-deploy-models-deploy-model-on-android-py"><span class="std std-ref">here</span></a> to run the Jupyter notebook locally.</p>
 <a class="reference external image-reference" href="https://colab.research.google.com/github/apache/tvm-site/blob/asf-site/docs/_downloads/eed2658f15243bab719b2de7769fa45a/deploy_model_on_android.ipynb"><img alt="https://raw.githubusercontent.com/tlc-pack/web-data/main/images/utilities/colab_button.svg" class="align-center" src="https://raw.githubusercontent.com/tlc-pack/web-data/main/images/utilities/colab_button.svg" width="300px" /></a>
 </div>
 <div class="sphx-glr-example-title section" id="deploy-the-pretrained-model-on-android">
 <span id="tutorial-deploy-model-on-android"></span><span id="sphx-glr-how-to-deploy-models-deploy-model-on-android-py"></span><h1>Deploy the Pretrained Model on Android<a class="headerlink" href="#deploy-the-pretrained-model-on-android" title="Permalink to this headline">¶</a></h1>
 <p><strong>Author</strong>: <a class="reference external" href="https://tkat0.github.io/">Tomohiro Kato</a></p>
 <p>This is an example of using Relay to compile a keras model and deploy it on Android device.</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">os</span>
 <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
 <span class="kn">from</span> <span class="nn">PIL</span> <span class="kn">import</span> <span class="n">Image</span>
 <span class="kn">import</span> <span class="nn">keras</span>
 <span class="kn">from</span> <span class="nn">keras.applications.mobilenet_v2</span> <span class="kn">import</span> <span class="n">MobileNetV2</span>
 <span class="kn">import</span> <span class="nn">tvm</span>
 <span class="kn">from</span> <span class="nn">tvm</span> <span class="kn">import</span> <span class="n">te</span>
 <span class="kn">import</span> <span class="nn">tvm.relay</span> <span class="k">as</span> <span class="nn">relay</span>
 <span class="kn">from</span> <span class="nn">tvm</span> <span class="kn">import</span> <span class="n">rpc</span>
 <span class="kn">from</span> <span class="nn">tvm.contrib</span> <span class="kn">import</span> <span class="n">utils</span><span class="p">,</span> <span class="n">ndk</span><span class="p">,</span> <span class="n">graph_executor</span> <span class="k">as</span> <span class="n">runtime</span>
 <span class="kn">from</span> <span class="nn">tvm.contrib.download</span> <span class="kn">import</span> <span class="n">download_testdata</span>
 </pre></div>
 </div>
 <div class="section" id="setup-environment">
 <h2>Setup Environment<a class="headerlink" href="#setup-environment" title="Permalink to this headline">¶</a></h2>
 <p>Since there are many required packages for Android, it is recommended to use the official Docker Image.</p>
 <p>First, to build and run Docker Image, we can run the following command.</p>
 <div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>git<span class="w"> </span>clone<span class="w"> </span>--recursive<span class="w"> </span>https://github.com/apache/tvm<span class="w"> </span>tvm
 <span class="nb">cd</span><span class="w"> </span>tvm
 docker<span class="w"> </span>build<span class="w"> </span>-t<span class="w"> </span>tvm.demo_android<span class="w"> </span>-f<span class="w"> </span>docker/Dockerfile.demo_android<span class="w"> </span>./docker
 docker<span class="w"> </span>run<span class="w"> </span>--pid<span class="o">=</span>host<span class="w"> </span>-h<span class="w"> </span>tvm<span class="w"> </span>-v<span class="w"> </span><span class="nv">$PWD</span>:/workspace<span class="w"> </span><span class="se">\</span>
 <span class="w">       </span>-w<span class="w"> </span>/workspace<span class="w"> </span>-p<span class="w"> </span><span class="m">9190</span>:9190<span class="w"> </span>--name<span class="w"> </span>tvm<span class="w"> </span>-it<span class="w"> </span>tvm.demo_android<span class="w"> </span>bash
 </pre></div>
 </div>
 <p>You are now inside the container. The cloned TVM directory is mounted on /workspace.
 At this time, mount the 9190 port used by RPC described later.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
 <p>Please execute the following steps in the container.
 We can execute <code class="code docutils literal notranslate"><span class="pre">docker</span> <span class="pre">exec</span> <span class="pre">-it</span> <span class="pre">tvm</span> <span class="pre">bash</span></code> to open a new terminal in the container.</p>
 </div>
 <p>Next we build the TVM.</p>
 <div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>mkdir<span class="w"> </span>build
 <span class="nb">cd</span><span class="w"> </span>build
 cmake<span class="w"> </span>-DUSE_LLVM<span class="o">=</span>llvm-config-8<span class="w"> </span><span class="se">\</span>
 <span class="w">      </span>-DUSE_RPC<span class="o">=</span>ON<span class="w"> </span><span class="se">\</span>
 <span class="w">      </span>-DUSE_SORT<span class="o">=</span>ON<span class="w"> </span><span class="se">\</span>
 <span class="w">      </span>-DUSE_VULKAN<span class="o">=</span>ON<span class="w"> </span><span class="se">\</span>
 <span class="w">      </span>-DUSE_GRAPH_EXECUTOR<span class="o">=</span>ON<span class="w"> </span><span class="se">\</span>
 <span class="w">      </span>..
 make<span class="w"> </span>-j10
 </pre></div>
 </div>
 <p>After building TVM successfully, Please set PYTHONPATH.</p>
 <div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="nb">echo</span><span class="w"> </span><span class="s1">&#39;export PYTHONPATH=/workspace/python:/workspace/vta/python:${PYTHONPATH}&#39;</span><span class="w"> </span>&gt;&gt;<span class="w"> </span>~/.bashrc
 <span class="nb">source</span><span class="w"> </span>~/.bashrc
 </pre></div>
 </div>
 </div>
 <div class="section" id="start-rpc-tracker">
 <h2>Start RPC Tracker<a class="headerlink" href="#start-rpc-tracker" title="Permalink to this headline">¶</a></h2>
 <p>TVM uses RPC session to communicate with Android device.</p>
 <p>To start an RPC tracker, run this command in the container. The tracker is
 required during the whole tuning process, so we need to open a new terminal for
 this command:</p>
 <div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>python3<span class="w"> </span>-m<span class="w"> </span>tvm.exec.rpc_tracker<span class="w"> </span>--host<span class="o">=</span><span class="m">0</span>.0.0.0<span class="w"> </span>--port<span class="o">=</span><span class="m">9190</span>
 </pre></div>
 </div>
 <p>The expected output is</p>
 <div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>INFO:RPCTracker:bind<span class="w"> </span>to<span class="w"> </span><span class="m">0</span>.0.0.0:9190
 </pre></div>
 </div>
 </div>
 <div class="section" id="register-android-device-to-rpc-tracker">
 <h2>Register Android device to RPC Tracker<a class="headerlink" href="#register-android-device-to-rpc-tracker" title="Permalink to this headline">¶</a></h2>
 <p>Now we can register our Android device to the tracker.</p>
 <p>Follow this <a class="reference external" href="https://github.com/apache/tvm/tree/main/apps/android_rpc">readme page</a> to
 install TVM RPC APK on the android device.</p>
 <p>Here is an example of config.mk. I enabled OpenCL and Vulkan.</p>
 <div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="nv">APP_ABI</span><span class="w"> </span><span class="o">=</span><span class="w"> </span>arm64-v8a

 <span class="nv">APP_PLATFORM</span><span class="w"> </span><span class="o">=</span><span class="w"> </span>android-24

 <span class="c1"># whether enable OpenCL during compile</span>
 <span class="nv">USE_OPENCL</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="m">1</span>

 <span class="c1"># whether to enable Vulkan during compile</span>
 <span class="nv">USE_VULKAN</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="m">1</span>

 ifeq<span class="w"> </span><span class="o">(</span><span class="k">$(</span>USE_VULKAN<span class="k">)</span>,<span class="w"> </span><span class="m">1</span><span class="o">)</span>
 <span class="w">  </span><span class="c1"># Statically linking vulkan requires API Level 24 or higher</span>
 <span class="w">  </span><span class="nv">APP_PLATFORM</span><span class="w"> </span><span class="o">=</span><span class="w"> </span>android-24
 endif

 <span class="c1"># the additional include headers you want to add, e.g., SDK_PATH/adrenosdk/Development/Inc</span>
 <span class="nv">ADD_C_INCLUDES</span><span class="w"> </span><span class="o">+=</span><span class="w"> </span>/work/adrenosdk-linux-5_0/Development/Inc
 <span class="nv">ADD_C_INCLUDES</span><span class="w"> </span><span class="o">=</span>

 <span class="c1"># the additional link libs you want to add, e.g., ANDROID_LIB_PATH/libOpenCL.so</span>
 <span class="nv">ADD_LDLIBS</span><span class="w"> </span><span class="o">=</span>
 </pre></div>
 </div>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
 <p>At this time, don’t forget to <a class="reference external" href="https://github.com/apache/tvm/tree/main/apps/android_rpc#architecture-and-android-standalone-toolchain">create a standalone toolchain</a> .</p>
 <p>for example</p>
 <div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="nv">$ANDROID_NDK_HOME</span>/build/tools/make-standalone-toolchain.sh<span class="w"> </span><span class="se">\</span>
 <span class="w">   </span>--platform<span class="o">=</span>android-24<span class="w"> </span>--use-llvm<span class="w"> </span>--arch<span class="o">=</span>arm64<span class="w"> </span>--install-dir<span class="o">=</span>/opt/android-toolchain-arm64
 <span class="nb">export</span><span class="w"> </span><span class="nv">TVM_NDK_CC</span><span class="o">=</span>/opt/android-toolchain-arm64/bin/aarch64-linux-android-g++
 </pre></div>
 </div>
 </div>
 <p>Next, start the Android application and enter the IP address and port of RPC Tracker.
 Then you have already registered your device.</p>
 <p>After registering devices, we can confirm it by querying rpc_tracker</p>
 <div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>python3<span class="w"> </span>-m<span class="w"> </span>tvm.exec.query_rpc_tracker<span class="w"> </span>--host<span class="o">=</span><span class="m">0</span>.0.0.0<span class="w"> </span>--port<span class="o">=</span><span class="m">9190</span>
 </pre></div>
 </div>
 <p>For example, if we have 1 Android device.
 the output can be</p>
 <div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>Queue<span class="w"> </span>Status
 ----------------------------------
 key<span class="w">          </span>total<span class="w">  </span>free<span class="w">  </span>pending
 ----------------------------------
 android<span class="w">      </span><span class="m">1</span><span class="w">      </span><span class="m">1</span><span class="w">     </span><span class="m">0</span>
 ----------------------------------
 </pre></div>
 </div>
 <p>To confirm that you can communicate with Android, we can run following test script.
 If you use OpenCL and Vulkan, please set <code class="code docutils literal notranslate"><span class="pre">test_opencl</span></code> and <code class="code docutils literal notranslate"><span class="pre">test_vulkan</span></code> in the script.</p>
 <div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="nb">export</span><span class="w"> </span><span class="nv">TVM_TRACKER_HOST</span><span class="o">=</span><span class="m">0</span>.0.0.0
 <span class="nb">export</span><span class="w"> </span><span class="nv">TVM_TRACKER_PORT</span><span class="o">=</span><span class="m">9190</span>
 </pre></div>
 </div>
 <div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="nb">cd</span><span class="w"> </span>/workspace/apps/android_rpc
 python3<span class="w"> </span>tests/android_rpc_test.py
 </pre></div>
 </div>
 </div>
 <div class="section" id="load-pretrained-keras-model">
 <h2>Load pretrained keras model<a class="headerlink" href="#load-pretrained-keras-model" title="Permalink to this headline">¶</a></h2>
 <p>We load a pretrained MobileNetV2(alpha=0.5) classification model provided by keras.</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">keras</span><span class="o">.</span><span class="n">backend</span><span class="o">.</span><span class="n">clear_session</span><span class="p">()</span>  <span class="c1"># Destroys the current TF graph and creates a new one.</span>
 <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">weights_url</span></a> <span class="o">=</span> <span class="s2">&quot;&quot;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span>
     <span class="p">[</span>
         <span class="s2">&quot;https://github.com/JonathanCMitchell/&quot;</span><span class="p">,</span>
         <span class="s2">&quot;mobilenet_v2_keras/releases/download/v1.1/&quot;</span><span class="p">,</span>
         <span class="s2">&quot;mobilenet_v2_weights_tf_dim_ordering_tf_kernels_0.5_224.h5&quot;</span><span class="p">,</span>
     <span class="p">]</span>
 <span class="p">)</span>
 <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">weights_file</span></a> <span class="o">=</span> <span class="s2">&quot;mobilenet_v2_weights.h5&quot;</span>
 <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">weights_path</span></a> <span class="o">=</span> <span class="n">download_testdata</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">weights_url</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">weights_file</span></a><span class="p">,</span> <a href="../../reference/api/python/graph_executor.html#tvm.contrib.graph_executor.GraphModule" title="tvm.contrib.graph_executor.GraphModule" class="sphx-glr-backref-module-tvm-contrib-graph_executor sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">module</span></a><span class="o">=</span><span class="s2">&quot;keras&quot;</span><span class="p">)</span>
 <span class="n">keras_mobilenet_v2</span> <span class="o">=</span> <span class="n">MobileNetV2</span><span class="p">(</span>
     <span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">include_top</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">weights</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">input_shape</span><span class="o">=</span><span class="p">(</span><span class="mi">224</span><span class="p">,</span> <span class="mi">224</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="n">classes</span><span class="o">=</span><span class="mi">1000</span>
 <span class="p">)</span>
 <span class="n">keras_mobilenet_v2</span><span class="o">.</span><span class="n">load_weights</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">weights_path</span></a><span class="p">)</span>
 </pre></div>
 </div>
 <p>In order to test our model, here we download an image of cat and
 transform its format.</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">img_url</span></a> <span class="o">=</span> <span class="s2">&quot;https://github.com/dmlc/mxnet.js/blob/main/data/cat.png?raw=true&quot;</span>
 <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">img_name</span></a> <span class="o">=</span> <span class="s2">&quot;cat.png&quot;</span>
 <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">img_path</span></a> <span class="o">=</span> <span class="n">download_testdata</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">img_url</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">img_name</span></a><span class="p">,</span> <a href="../../reference/api/python/graph_executor.html#tvm.contrib.graph_executor.GraphModule" title="tvm.contrib.graph_executor.GraphModule" class="sphx-glr-backref-module-tvm-contrib-graph_executor sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">module</span></a><span class="o">=</span><span class="s2">&quot;data&quot;</span><span class="p">)</span>
 <span class="n">image</span> <span class="o">=</span> <span class="n">Image</span><span class="o">.</span><span class="n">open</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">img_path</span></a><span class="p">)</span><span class="o">.</span><span class="n">resize</span><span class="p">((</span><span class="mi">224</span><span class="p">,</span> <span class="mi">224</span><span class="p">))</span>
 <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dtype</span></a> <span class="o">=</span> <span class="s2">&quot;float32&quot;</span>


 <span class="k">def</span> <span class="nf">transform_image</span><span class="p">(</span><span class="n">image</span><span class="p">):</span>
     <span class="n">image</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">image</span><span class="p">)</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mf">123.0</span><span class="p">,</span> <span class="mf">117.0</span><span class="p">,</span> <span class="mf">104.0</span><span class="p">])</span>
     <span class="n">image</span> <span class="o">/=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mf">58.395</span><span class="p">,</span> <span class="mf">57.12</span><span class="p">,</span> <span class="mf">57.375</span><span class="p">])</span>
     <span class="n">image</span> <span class="o">=</span> <span class="n">image</span><span class="o">.</span><span class="n">transpose</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
     <span class="n">image</span> <span class="o">=</span> <span class="n">image</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">,</span> <span class="p">:]</span>
     <span class="k">return</span> <span class="n">image</span>


 <span class="n">x</span> <span class="o">=</span> <span class="n">transform_image</span><span class="p">(</span><span class="n">image</span><span class="p">)</span>
 </pre></div>
 </div>
 <p>synset is used to transform the label from number of ImageNet class to
 the word human can understand.</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">synset_url</span></a> <span class="o">=</span> <span class="s2">&quot;&quot;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span>
     <span class="p">[</span>
         <span class="s2">&quot;https://gist.githubusercontent.com/zhreshold/&quot;</span><span class="p">,</span>
         <span class="s2">&quot;4d0b62f3d01426887599d4f7ede23ee5/raw/&quot;</span><span class="p">,</span>
         <span class="s2">&quot;596b27d23537e5a1b5751d2b0481ef172f58b539/&quot;</span><span class="p">,</span>
         <span class="s2">&quot;imagenet1000_clsid_to_human.txt&quot;</span><span class="p">,</span>
     <span class="p">]</span>
 <span class="p">)</span>
 <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">synset_name</span></a> <span class="o">=</span> <span class="s2">&quot;imagenet1000_clsid_to_human.txt&quot;</span>
 <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">synset_path</span></a> <span class="o">=</span> <span class="n">download_testdata</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">synset_url</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">synset_name</span></a><span class="p">,</span> <a href="../../reference/api/python/graph_executor.html#tvm.contrib.graph_executor.GraphModule" title="tvm.contrib.graph_executor.GraphModule" class="sphx-glr-backref-module-tvm-contrib-graph_executor sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">module</span></a><span class="o">=</span><span class="s2">&quot;data&quot;</span><span class="p">)</span>
 <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">synset_path</span></a><span class="p">)</span> <span class="k">as</span> <a href="https://docs.python.org/3/library/io.html#io.TextIOWrapper" title="io.TextIOWrapper" class="sphx-glr-backref-module-io sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">f</span></a><span class="p">:</span>
     <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">synset</span></a> <span class="o">=</span> <span class="nb">eval</span><span class="p">(</span><a href="https://docs.python.org/3/library/io.html#io.TextIOWrapper" title="io.TextIOWrapper" class="sphx-glr-backref-module-io sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">f</span></a><span class="o">.</span><span class="n">read</span><span class="p">())</span>
 </pre></div>
 </div>
 </div>
 <div class="section" id="compile-the-model-with-relay">
 <h2>Compile the model with relay<a class="headerlink" href="#compile-the-model-with-relay" title="Permalink to this headline">¶</a></h2>
 <p>If we run the example on our x86 server for demonstration, we can simply
 set it as <code class="code docutils literal notranslate"><span class="pre">llvm</span></code>. If running it on the Android device, we need to
 specify its instruction set. Set <code class="code docutils literal notranslate"><span class="pre">local_demo</span></code> to False if you want
 to run this tutorial with a real device.</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">local_demo</span></a> <span class="o">=</span> <span class="kc">True</span>

 <span class="c1"># by default on CPU target will execute.</span>
 <span class="c1"># select &#39;cpu&#39;, &#39;opencl&#39; and &#39;vulkan&#39;</span>
 <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_target</span></a> <span class="o">=</span> <span class="s2">&quot;cpu&quot;</span>

 <span class="c1"># Change target configuration.</span>
 <span class="c1"># Run `adb shell cat /proc/cpuinfo` to find the arch.</span>
 <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">arch</span></a> <span class="o">=</span> <span class="s2">&quot;arm64&quot;</span>
 <a href="../../reference/api/python/target.html#tvm.target.Target" title="tvm.target.Target" class="sphx-glr-backref-module-tvm-target sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">target</span></a> <span class="o">=</span> <a href="../../reference/api/python/target.html#tvm.target.Target" title="tvm.target.Target" class="sphx-glr-backref-module-tvm-target sphx-glr-backref-type-py-class"><span class="n">tvm</span><span class="o">.</span><span class="n">target</span><span class="o">.</span><span class="n">Target</span></a><span class="p">(</span><span class="s2">&quot;llvm -mtriple=</span><span class="si">%s</span><span class="s2">-linux-android&quot;</span> <span class="o">%</span> <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">arch</span></a><span class="p">)</span>

 <span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">local_demo</span></a><span class="p">:</span>
     <a href="../../reference/api/python/target.html#tvm.target.Target" title="tvm.target.Target" class="sphx-glr-backref-module-tvm-target sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">target</span></a> <span class="o">=</span> <a href="../../reference/api/python/target.html#tvm.target.Target" title="tvm.target.Target" class="sphx-glr-backref-module-tvm-target sphx-glr-backref-type-py-class"><span class="n">tvm</span><span class="o">.</span><span class="n">target</span><span class="o">.</span><span class="n">Target</span></a><span class="p">(</span><span class="s2">&quot;llvm&quot;</span><span class="p">)</span>
 <span class="k">elif</span> <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_target</span></a> <span class="o">==</span> <span class="s2">&quot;opencl&quot;</span><span class="p">:</span>
     <a href="../../reference/api/python/target.html#tvm.target.Target" title="tvm.target.Target" class="sphx-glr-backref-module-tvm-target sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">target</span></a> <span class="o">=</span> <a href="../../reference/api/python/target.html#tvm.target.Target" title="tvm.target.Target" class="sphx-glr-backref-module-tvm-target sphx-glr-backref-type-py-class"><span class="n">tvm</span><span class="o">.</span><span class="n">target</span><span class="o">.</span><span class="n">Target</span></a><span class="p">(</span><span class="s2">&quot;opencl&quot;</span><span class="p">,</span> <span class="n">host</span><span class="o">=</span><a href="../../reference/api/python/target.html#tvm.target.Target" title="tvm.target.Target" class="sphx-glr-backref-module-tvm-target sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">target</span></a><span class="p">)</span>
 <span class="k">elif</span> <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_target</span></a> <span class="o">==</span> <span class="s2">&quot;vulkan&quot;</span><span class="p">:</span>
     <a href="../../reference/api/python/target.html#tvm.target.Target" title="tvm.target.Target" class="sphx-glr-backref-module-tvm-target sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">target</span></a> <span class="o">=</span> <a href="../../reference/api/python/target.html#tvm.target.Target" title="tvm.target.Target" class="sphx-glr-backref-module-tvm-target sphx-glr-backref-type-py-class"><span class="n">tvm</span><span class="o">.</span><span class="n">target</span><span class="o">.</span><span class="n">Target</span></a><span class="p">(</span><span class="s2">&quot;vulkan&quot;</span><span class="p">,</span> <span class="n">host</span><span class="o">=</span><a href="../../reference/api/python/target.html#tvm.target.Target" title="tvm.target.Target" class="sphx-glr-backref-module-tvm-target sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">target</span></a><span class="p">)</span>

 <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_name</span></a> <span class="o">=</span> <span class="s2">&quot;input_1&quot;</span>
 <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">shape_dict</span></a> <span class="o">=</span> <span class="p">{</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_name</span></a><span class="p">:</span> <a href="https://docs.python.org/3/library/stdtypes.html#tuple" title="builtins.tuple" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">x</span><span class="o">.</span><span class="n">shape</span></a><span class="p">}</span>
 <span class="n">mod</span><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">params</span></a> <span class="o">=</span> <a href="../../reference/api/python/relay/frontend.html#tvm.relay.frontend.from_keras" title="tvm.relay.frontend.from_keras" class="sphx-glr-backref-module-tvm-relay-frontend sphx-glr-backref-type-py-function"><span class="n">relay</span><span class="o">.</span><span class="n">frontend</span><span class="o">.</span><span class="n">from_keras</span></a><span class="p">(</span><span class="n">keras_mobilenet_v2</span><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">shape_dict</span></a><span class="p">)</span>

 <span class="k">with</span> <a href="../../reference/api/python/ir.html#tvm.transform.PassContext" title="tvm.transform.PassContext" class="sphx-glr-backref-module-tvm-transform sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tvm</span><span class="o">.</span><span class="n">transform</span><span class="o">.</span><span class="n">PassContext</span></a><span class="p">(</span><span class="n">opt_level</span><span class="o">=</span><span class="mi">3</span><span class="p">):</span>
     <span class="n">lib</span> <span class="o">=</span> <span class="n">relay</span><span class="o">.</span><span class="n">build</span><span class="p">(</span><span class="n">mod</span><span class="p">,</span> <a href="../../reference/api/python/target.html#tvm.target.Target" title="tvm.target.Target" class="sphx-glr-backref-module-tvm-target sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">target</span></a><span class="o">=</span><a href="../../reference/api/python/target.html#tvm.target.Target" title="tvm.target.Target" class="sphx-glr-backref-module-tvm-target sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">target</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">params</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">params</span></a><span class="p">)</span>

 <span class="c1"># After `relay.build`, you will get three return values: graph,</span>
 <span class="c1"># library and the new parameter, since we do some optimization that will</span>
 <span class="c1"># change the parameters but keep the result of model as the same.</span>

 <span class="c1"># Save the library at local temporary directory.</span>
 <a href="../../reference/api/python/contrib.html#tvm.contrib.utils.TempDirectory" title="tvm.contrib.utils.TempDirectory" class="sphx-glr-backref-module-tvm-contrib-utils sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tmp</span></a> <span class="o">=</span> <a href="../../reference/api/python/contrib.html#tvm.contrib.utils.tempdir" title="tvm.contrib.utils.tempdir" class="sphx-glr-backref-module-tvm-contrib-utils sphx-glr-backref-type-py-function"><span class="n">utils</span><span class="o">.</span><span class="n">tempdir</span></a><span class="p">()</span>
 <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">lib_fname</span></a> <span class="o">=</span> <a href="../../reference/api/python/contrib.html#tvm.contrib.utils.TempDirectory.relpath" title="tvm.contrib.utils.TempDirectory.relpath" class="sphx-glr-backref-module-tvm-contrib-utils sphx-glr-backref-type-py-method"><span class="n">tmp</span><span class="o">.</span><span class="n">relpath</span></a><span class="p">(</span><span class="s2">&quot;net.so&quot;</span><span class="p">)</span>
 <span class="n">fcompile</span> <span class="o">=</span> <a href="../../reference/api/python/contrib.html#tvm.contrib.ndk.create_shared" title="tvm.contrib.ndk.create_shared" class="sphx-glr-backref-module-tvm-contrib-ndk sphx-glr-backref-type-py-function"><span class="n">ndk</span><span class="o">.</span><span class="n">create_shared</span></a> <span class="k">if</span> <span class="ow">not</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">local_demo</span></a> <span class="k">else</span> <span class="kc">None</span>
 <span class="n">lib</span><span class="o">.</span><span class="n">export_library</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">lib_fname</span></a><span class="p">,</span> <span class="n">fcompile</span><span class="o">=</span><span class="n">fcompile</span><span class="p">)</span>
 </pre></div>
 </div>
 </div>
 <div class="section" id="deploy-the-model-remotely-by-rpc">
 <h2>Deploy the Model Remotely by RPC<a class="headerlink" href="#deploy-the-model-remotely-by-rpc" title="Permalink to this headline">¶</a></h2>
 <p>With RPC, you can deploy the model remotely from your host machine
 to the remote android device.</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tracker_host</span></a> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;TVM_TRACKER_HOST&quot;</span><span class="p">,</span> <span class="s2">&quot;127.0.0.1&quot;</span><span class="p">)</span>
 <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tracker_port</span></a> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;TVM_TRACKER_PORT&quot;</span><span class="p">,</span> <span class="mi">9190</span><span class="p">))</span>
 <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">key</span></a> <span class="o">=</span> <span class="s2">&quot;android&quot;</span>

 <span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">local_demo</span></a><span class="p">:</span>
     <a href="../../reference/api/python/rpc.html#tvm.rpc.LocalSession" title="tvm.rpc.LocalSession" class="sphx-glr-backref-module-tvm-rpc sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">remote</span></a> <span class="o">=</span> <a href="../../reference/api/python/rpc.html#tvm.rpc.LocalSession" title="tvm.rpc.LocalSession" class="sphx-glr-backref-module-tvm-rpc sphx-glr-backref-type-py-class"><span class="n">rpc</span><span class="o">.</span><span class="n">LocalSession</span></a><span class="p">()</span>
 <span class="k">else</span><span class="p">:</span>
     <span class="n">tracker</span> <span class="o">=</span> <a href="../../reference/api/python/rpc.html#tvm.rpc.connect_tracker" title="tvm.rpc.connect_tracker" class="sphx-glr-backref-module-tvm-rpc sphx-glr-backref-type-py-function"><span class="n">rpc</span><span class="o">.</span><span class="n">connect_tracker</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tracker_host</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tracker_port</span></a><span class="p">)</span>
     <span class="c1"># When running a heavy model, we should increase the `session_timeout`</span>
     <a href="../../reference/api/python/rpc.html#tvm.rpc.LocalSession" title="tvm.rpc.LocalSession" class="sphx-glr-backref-module-tvm-rpc sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">remote</span></a> <span class="o">=</span> <span class="n">tracker</span><span class="o">.</span><span class="n">request</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">key</span></a><span class="p">,</span> <span class="n">priority</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">session_timeout</span><span class="o">=</span><span class="mi">60</span><span class="p">)</span>

 <span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">local_demo</span></a><span class="p">:</span>
     <span class="n">dev</span> <span class="o">=</span> <a href="../../reference/api/python/rpc.html#tvm.rpc.RPCSession.cpu" title="tvm.rpc.RPCSession.cpu" class="sphx-glr-backref-module-tvm-rpc sphx-glr-backref-type-py-method"><span class="n">remote</span><span class="o">.</span><span class="n">cpu</span></a><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
 <span class="k">elif</span> <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_target</span></a> <span class="o">==</span> <span class="s2">&quot;opencl&quot;</span><span class="p">:</span>
     <span class="n">dev</span> <span class="o">=</span> <a href="../../reference/api/python/rpc.html#tvm.rpc.RPCSession.cl" title="tvm.rpc.RPCSession.cl" class="sphx-glr-backref-module-tvm-rpc sphx-glr-backref-type-py-method"><span class="n">remote</span><span class="o">.</span><span class="n">cl</span></a><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
 <span class="k">elif</span> <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_target</span></a> <span class="o">==</span> <span class="s2">&quot;vulkan&quot;</span><span class="p">:</span>
     <span class="n">dev</span> <span class="o">=</span> <a href="../../reference/api/python/rpc.html#tvm.rpc.RPCSession.vulkan" title="tvm.rpc.RPCSession.vulkan" class="sphx-glr-backref-module-tvm-rpc sphx-glr-backref-type-py-method"><span class="n">remote</span><span class="o">.</span><span class="n">vulkan</span></a><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
 <span class="k">else</span><span class="p">:</span>
     <span class="n">dev</span> <span class="o">=</span> <a href="../../reference/api/python/rpc.html#tvm.rpc.RPCSession.cpu" title="tvm.rpc.RPCSession.cpu" class="sphx-glr-backref-module-tvm-rpc sphx-glr-backref-type-py-method"><span class="n">remote</span><span class="o">.</span><span class="n">cpu</span></a><span class="p">(</span><span class="mi">0</span><span class="p">)</span>

 <span class="c1"># upload the library to remote device and load it</span>
 <a href="../../reference/api/python/rpc.html#tvm.rpc.RPCSession.upload" title="tvm.rpc.RPCSession.upload" class="sphx-glr-backref-module-tvm-rpc sphx-glr-backref-type-py-method"><span class="n">remote</span><span class="o">.</span><span class="n">upload</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">lib_fname</span></a><span class="p">)</span>
 <a href="../../reference/api/python/runtime.html#tvm.runtime.Module" title="tvm.runtime.Module" class="sphx-glr-backref-module-tvm-runtime sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">rlib</span></a> <span class="o">=</span> <a href="../../reference/api/python/rpc.html#tvm.rpc.RPCSession.load_module" title="tvm.rpc.RPCSession.load_module" class="sphx-glr-backref-module-tvm-rpc sphx-glr-backref-type-py-method"><span class="n">remote</span><span class="o">.</span><span class="n">load_module</span></a><span class="p">(</span><span class="s2">&quot;net.so&quot;</span><span class="p">)</span>

 <span class="c1"># create the remote runtime module</span>
 <a href="../../reference/api/python/graph_executor.html#tvm.contrib.graph_executor.GraphModule" title="tvm.contrib.graph_executor.GraphModule" class="sphx-glr-backref-module-tvm-contrib-graph_executor sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">module</span></a> <span class="o">=</span> <a href="../../reference/api/python/graph_executor.html#tvm.contrib.graph_executor.GraphModule" title="tvm.contrib.graph_executor.GraphModule" class="sphx-glr-backref-module-tvm-contrib-graph_executor sphx-glr-backref-type-py-class"><span class="n">runtime</span><span class="o">.</span><span class="n">GraphModule</span></a><span class="p">(</span><a href="../../reference/api/python/runtime.html#tvm.runtime.Module" title="tvm.runtime.Module" class="sphx-glr-backref-module-tvm-runtime sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">rlib</span></a><span class="p">[</span><span class="s2">&quot;default&quot;</span><span class="p">](</span><span class="n">dev</span><span class="p">))</span>
 </pre></div>
 </div>
 </div>
 <div class="section" id="execute-on-tvm">
 <h2>Execute on TVM<a class="headerlink" href="#execute-on-tvm" title="Permalink to this headline">¶</a></h2>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># set input data</span>
 <a href="../../reference/api/python/graph_executor.html#tvm.contrib.graph_executor.GraphModule.set_input" title="tvm.contrib.graph_executor.GraphModule.set_input" class="sphx-glr-backref-module-tvm-contrib-graph_executor sphx-glr-backref-type-py-method"><span class="n">module</span><span class="o">.</span><span class="n">set_input</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_name</span></a><span class="p">,</span> <a href="../../reference/api/python/ndarray.html#tvm.nd.array" title="tvm.nd.array" class="sphx-glr-backref-module-tvm-nd sphx-glr-backref-type-py-function"><span class="n">tvm</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span></a><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dtype</span></a><span class="p">)))</span>
 <span class="c1"># run</span>
 <a href="../../reference/api/python/graph_executor.html#tvm.contrib.graph_executor.GraphModule.run" title="tvm.contrib.graph_executor.GraphModule.run" class="sphx-glr-backref-module-tvm-contrib-graph_executor sphx-glr-backref-type-py-method"><span class="n">module</span><span class="o">.</span><span class="n">run</span></a><span class="p">()</span>
 <span class="c1"># get output</span>
 <span class="n">out</span> <span class="o">=</span> <a href="../../reference/api/python/graph_executor.html#tvm.contrib.graph_executor.GraphModule.get_output" title="tvm.contrib.graph_executor.GraphModule.get_output" class="sphx-glr-backref-module-tvm-contrib-graph_executor sphx-glr-backref-type-py-method"><span class="n">module</span><span class="o">.</span><span class="n">get_output</span></a><span class="p">(</span><span class="mi">0</span><span class="p">)</span>

 <span class="c1"># get top1 result</span>
 <span class="n">top1</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">out</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span>
 <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;TVM prediction top-1: </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">synset</span></a><span class="p">[</span><span class="n">top1</span><span class="p">]))</span>

 <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Evaluate inference time cost...&quot;</span><span class="p">)</span>
 <span class="nb">print</span><span class="p">(</span><a href="../../reference/api/python/graph_executor.html#tvm.contrib.graph_executor.GraphModule.benchmark" title="tvm.contrib.graph_executor.GraphModule.benchmark" class="sphx-glr-backref-module-tvm-contrib-graph_executor sphx-glr-backref-type-py-method"><span class="n">module</span><span class="o">.</span><span class="n">benchmark</span></a><span class="p">(</span><span class="n">dev</span><span class="p">,</span> <span class="n">number</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">repeat</span><span class="o">=</span><span class="mi">10</span><span class="p">))</span>
 </pre></div>
 </div>
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>TVM prediction top-1: tiger cat
 Evaluate inference time cost...
 Execution time summary:
  mean (ms)   median (ms)    max (ms)     min (ms)     std (ms)
   16.4625      16.3645      16.9735      16.2344       0.2272
 </pre></div>
 </div>
 </div>
 <div class="section" id="sample-output">
 <h2>Sample Output<a class="headerlink" href="#sample-output" title="Permalink to this headline">¶</a></h2>
 <p>The following is the result of ‘cpu’, ‘opencl’ and ‘vulkan’ using Adreno 530 on Snapdragon 820</p>
 <p>Although we can run on a GPU, it is slower than CPU.
 To speed up, we need to write and optimize the schedule according to the GPU architecture.</p>
 <div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="c1"># cpu</span>
 TVM<span class="w"> </span>prediction<span class="w"> </span>top-1:<span class="w"> </span>tiger<span class="w"> </span>cat
 Evaluate<span class="w"> </span>inference<span class="w"> </span><span class="nb">time</span><span class="w"> </span>cost...
 Mean<span class="w"> </span>inference<span class="w"> </span><span class="nb">time</span><span class="w"> </span><span class="o">(</span>std<span class="w"> </span>dev<span class="o">)</span>:<span class="w"> </span><span class="m">37</span>.92<span class="w"> </span>ms<span class="w"> </span><span class="o">(</span><span class="m">19</span>.67<span class="w"> </span>ms<span class="o">)</span>

 <span class="c1"># opencl</span>
 TVM<span class="w"> </span>prediction<span class="w"> </span>top-1:<span class="w"> </span>tiger<span class="w"> </span>cat
 Evaluate<span class="w"> </span>inference<span class="w"> </span><span class="nb">time</span><span class="w"> </span>cost...
 Mean<span class="w"> </span>inference<span class="w"> </span><span class="nb">time</span><span class="w"> </span><span class="o">(</span>std<span class="w"> </span>dev<span class="o">)</span>:<span class="w"> </span><span class="m">419</span>.83<span class="w"> </span>ms<span class="w"> </span><span class="o">(</span><span class="m">7</span>.49<span class="w"> </span>ms<span class="o">)</span>

 <span class="c1"># vulkan</span>
 TVM<span class="w"> </span>prediction<span class="w"> </span>top-1:<span class="w"> </span>tiger<span class="w"> </span>cat
 Evaluate<span class="w"> </span>inference<span class="w"> </span><span class="nb">time</span><span class="w"> </span>cost...
 Mean<span class="w"> </span>inference<span class="w"> </span><span class="nb">time</span><span class="w"> </span><span class="o">(</span>std<span class="w"> </span>dev<span class="o">)</span>:<span class="w"> </span><span class="m">465</span>.80<span class="w"> </span>ms<span class="w"> </span><span class="o">(</span><span class="m">4</span>.52<span class="w"> </span>ms<span class="o">)</span>
 </pre></div>
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