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<title>Profile the execution of a simple model — ONNX Runtime 1.14.0 documentation</title>
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<section class="sphx-glr-example-title" 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 heading">¶</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-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span>
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<span class="kn">import</span> <span class="nn">onnx</span>
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<span class="kn">import</span> <span class="nn">onnxruntime</span> <span class="k">as</span> <span class="nn">rt</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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<span class="k">def</span> <span class="nf">change_ir_version</span><span class="p">(</span><span class="n">filename</span><span class="p">,</span> <span class="n">ir_version</span><span class="o">=</span><span class="mi">6</span><span class="p">):</span>
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<span class="s2">"onnxruntime==1.2.0 does not support opset <= 7 and ir_version > 6"</span>
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<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">filename</span><span class="p">,</span> <span class="s2">"rb"</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">model</span> <span class="o">=</span> <span class="n">onnx</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="n">model</span><span class="o">.</span><span class="n">ir_version</span> <span class="o">=</span> <span class="mi">6</span>
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<span class="k">if</span> <span class="n">model</span><span class="o">.</span><span class="n">opset_import</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">version</span> <span class="o"><=</span> <span class="mi">7</span><span class="p">:</span>
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<span class="n">model</span><span class="o">.</span><span class="n">opset_import</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">version</span> <span class="o">=</span> <span class="mi">11</span>
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<span class="k">return</span> <span class="n">model</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-default 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">onnx_model</span> <span class="o">=</span> <span class="n">change_ir_version</span><span class="p">(</span><span class="n">example1</span><span class="p">)</span>
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<span class="n">onnx_model_str</span> <span class="o">=</span> <span class="n">onnx_model</span><span class="o">.</span><span class="n">SerializeToString</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">onnx_model_str</span><span class="p">,</span> <span class="n">providers</span><span class="o">=</span><span class="n">rt</span><span class="o">.</span><span class="n">get_available_providers</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="kc">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="nb">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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<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-default 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="kc">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">onnx_model_str</span><span class="p">,</span> <span class="n">options</span><span class="p">,</span> <span class="n">providers</span><span class="o">=</span><span class="n">rt</span><span class="o">.</span><span class="n">get_available_providers</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="kc">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="nb">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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<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>onnxruntime_profile__2023-01-26_22-44-30.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-default 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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<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': 59,
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'name': 'model_loading_array',
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'ph': 'X',
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'pid': 2745,
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'tid': 2745,
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'ts': 1},
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{'args': {},
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'cat': 'Session',
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'dur': 308,
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'name': 'session_initialization',
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'ph': 'X',
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'pid': 2745,
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'tid': 2745,
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'ts': 73}]
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</pre></div>
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<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.005 seconds)</p>
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<p><a class="reference download internal" download="" href="../downloads/cfe61aca1f0a89486c7024466ea500fd/plot_profiling.py"><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></p>
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