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<li class="toctree-l2"><a class="reference internal" href="../tutorial.html#step-1-train-a-model-using-your-favorite-framework">Step 1: Train a model using your favorite framework</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../tutorial.html#step-2-convert-or-export-the-model-into-onnx-format">Step 2: Convert or export the model into ONNX format</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../tutorial.html#step-3-load-and-run-the-model-using-onnx-runtime">Step 3: Load and run the model using ONNX Runtime</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../api_summary.html#load-and-run-a-model">Load and run a model</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../api_summary.html#backend">Backend</a></li>
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<li class="toctree-l1 current"><a class="reference internal" href="index.html">Gallery of examples</a><ul class="current">
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<li class="toctree-l2"><a class="reference internal" href="plot_pipeline.html">Draw a pipeline</a></li>
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<li class="toctree-l2"><a class="reference internal" href="plot_load_and_predict.html">Load and predict with ONNX Runtime and a very simple model</a></li>
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<li class="toctree-l2 current"><a class="current reference internal" href="#">Profile the execution of a simple model</a></li>
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<li class="toctree-l2"><a class="reference internal" href="plot_backend.html">ONNX Runtime Backend for ONNX</a></li>
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<li class="toctree-l2"><a class="reference internal" href="plot_dl_keras.html">ONNX Runtime for Keras</a></li>
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<li class="toctree-l2"><a class="reference internal" href="plot_convert_pipeline_vectorizer.html">Train, convert and predict with ONNX Runtime</a></li>
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<li class="toctree-l2"><a class="reference internal" href="plot_common_errors.html">Common errors with onnxruntime</a></li>
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<li class="toctree-l2"><a class="reference internal" href="plot_train_convert_predict.html">Train, convert and predict with ONNX Runtime</a></li>
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<div class="sphx-glr-download-link-note admonition note">
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<p class="first admonition-title">Note</p>
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<p class="last">Click <a class="reference internal" href="#sphx-glr-download-auto-examples-plot-profiling-py"><span class="std std-ref">here</span></a> to download the full example code</p>
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</div>
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<div class="sphx-glr-example-title section" id="profile-the-execution-of-a-simple-model">
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<span id="l-example-profiling"></span><span id="sphx-glr-auto-examples-plot-profiling-py"></span><h1>Profile the execution of a simple model<a class="headerlink" href="#profile-the-execution-of-a-simple-model" title="Permalink to this headline">¶</a></h1>
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<p><em>ONNX Runtime</em> can profile the execution of the model.
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This example shows how to interpret the results.</p>
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<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">onnxruntime</span> <span class="kn">as</span> <span class="nn">rt</span>
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<span class="kn">import</span> <span class="nn">numpy</span>
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<span class="kn">from</span> <span class="nn">onnxruntime.datasets</span> <span class="kn">import</span> <span class="n">get_example</span>
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</pre></div>
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</div>
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<p>Let’s load a very simple model and compute some prediction.</p>
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<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">example1</span> <span class="o">=</span> <span class="n">get_example</span><span class="p">(</span><span class="s2">"mul_1.onnx"</span><span class="p">)</span>
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<span class="n">sess</span> <span class="o">=</span> <span class="n">rt</span><span class="o">.</span><span class="n">InferenceSession</span><span class="p">(</span><span class="n">example1</span><span class="p">)</span>
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<span class="n">input_name</span> <span class="o">=</span> <span class="n">sess</span><span class="o">.</span><span class="n">get_inputs</span><span class="p">()[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">name</span>
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<span class="n">x</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">],</span> <span class="p">[</span><span class="mf">3.0</span><span class="p">,</span> <span class="mf">4.0</span><span class="p">],</span> <span class="p">[</span><span class="mf">5.0</span><span class="p">,</span> <span class="mf">6.0</span><span class="p">]],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">numpy</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
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<span class="n">res</span> <span class="o">=</span> <span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="bp">None</span><span class="p">,</span> <span class="p">{</span><span class="n">input_name</span><span class="p">:</span> <span class="n">x</span><span class="p">})</span>
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<span class="k">print</span><span class="p">(</span><span class="n">res</span><span class="p">)</span>
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</pre></div>
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</div>
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<p class="sphx-glr-script-out">Out:</p>
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<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[array([[ 1., 4.],
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[ 9., 16.],
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[25., 36.]], dtype=float32)]
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</pre></div>
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</div>
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<p>We need to enable to profiling
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before running the predictions.</p>
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<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">options</span> <span class="o">=</span> <span class="n">rt</span><span class="o">.</span><span class="n">SessionOptions</span><span class="p">()</span>
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<span class="n">options</span><span class="o">.</span><span class="n">enable_profiling</span> <span class="o">=</span> <span class="bp">True</span>
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<span class="n">sess_profile</span> <span class="o">=</span> <span class="n">rt</span><span class="o">.</span><span class="n">InferenceSession</span><span class="p">(</span><span class="n">example1</span><span class="p">,</span> <span class="n">options</span><span class="p">)</span>
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<span class="n">input_name</span> <span class="o">=</span> <span class="n">sess</span><span class="o">.</span><span class="n">get_inputs</span><span class="p">()[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">name</span>
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<span class="n">x</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">],</span> <span class="p">[</span><span class="mf">3.0</span><span class="p">,</span> <span class="mf">4.0</span><span class="p">],</span> <span class="p">[</span><span class="mf">5.0</span><span class="p">,</span> <span class="mf">6.0</span><span class="p">]],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">numpy</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
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<span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="bp">None</span><span class="p">,</span> <span class="p">{</span><span class="n">input_name</span><span class="p">:</span> <span class="n">x</span><span class="p">})</span>
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<span class="n">prof_file</span> <span class="o">=</span> <span class="n">sess_profile</span><span class="o">.</span><span class="n">end_profiling</span><span class="p">()</span>
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<span class="k">print</span><span class="p">(</span><span class="n">prof_file</span><span class="p">)</span>
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</pre></div>
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</div>
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<p class="sphx-glr-script-out">Out:</p>
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<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>onnxruntime_profile__2019-12-19_16-47-06.json
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</pre></div>
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</div>
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<p>The results are stored un a file in JSON format.
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Let’s see what it contains.</p>
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<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">json</span>
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<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">prof_file</span><span class="p">,</span> <span class="s2">"r"</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
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<span class="n">sess_time</span> <span class="o">=</span> <span class="n">json</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">f</span><span class="p">)</span>
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<span class="kn">import</span> <span class="nn">pprint</span>
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<span class="n">pprint</span><span class="o">.</span><span class="n">pprint</span><span class="p">(</span><span class="n">sess_time</span><span class="p">)</span>
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</pre></div>
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</div>
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<p class="sphx-glr-script-out">Out:</p>
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<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[{'args': {},
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'cat': 'Session',
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'dur': 100,
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'name': 'model_loading_from_saved_proto',
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'ph': 'X',
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'pid': 27824,
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'tid': 13820,
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'ts': 10},
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{'args': {},
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'cat': 'Session',
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'dur': 200,
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'name': 'session_initialization',
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'ph': 'X',
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'pid': 27824,
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'tid': 13820,
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'ts': 123}]
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</pre></div>
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</div>
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<p><strong>Total running time of the script:</strong> ( 0 minutes 0.027 seconds)</p>
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<div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-plot-profiling-py">
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<div class="sphx-glr-download docutils container">
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<a class="reference download internal" href="../_downloads/plot_profiling.py" download=""><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_profiling.py</span></code></a></div>
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<div class="sphx-glr-download docutils container">
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<a class="reference download internal" href="../_downloads/plot_profiling.ipynb" download=""><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_profiling.ipynb</span></code></a></div>
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<p class="sphx-glr-signature"><a class="reference external" href="https://sphinx-gallery.readthedocs.io">Gallery generated by Sphinx-Gallery</a></p>
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