saymrwulf/onnxruntime
1
0
Fork 0
mirror of https://github.com/saymrwulf/onnxruntime.git synced 2026-07-16 18:31:27 +00:00
Code Issues Projects Releases Packages Wiki Activity Actions
onnxruntime/index.html
Hariharan Seshadri e10d3f5202 Update docs based on 0.5.0 release (#1548)
2019-08-01 18:12:59 -07:00

141 lines
No EOL
13 KiB
HTML
Raw Blame History

This file contains ambiguous Unicode characters

This file contains Unicode characters that might be confused with other characters. If you think that this is intentional, you can safely ignore this warning. Use the Escape button to reveal them.

<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
<meta http-equiv="x-ua-compatible" content="ie=edge">
<title>Python Bindings for ONNX Runtime</title>
<link rel="stylesheet" href="_static/sphinx-modern-theme.css" type="text/css"/>
<link rel="stylesheet" href="_static/pygments.css" type="text/css" />
<link rel="stylesheet" href="_static/gallery.css" type="text/css" />
</head>
<body>
<div class="container">
<div class="row" style="margin-top: 1rem;">
<div id="sidebar" class="col-xs-12 col-sm-3">
<a href="#">
<img style="margin-bottom: 0.5rem;" class="img-fluid" src="_static/ONNX_Runtime_icon.png"/>
</a>
<div id="searchbox" style="display: none" role="search">
<form class="form-inline" action="search.html" method="get">
<div class="form-group">
<label class="sr-only" for="searchInput">Search</label>
<input type="text" class="form-control" name="q" id="searchInput" placeholder="Search">
</div>
<button type="submit" class="btn btn-secondary" style="display:none">Go</button>
<input type="hidden" name="check_keywords" value="yes"/>
<input type="hidden" name="area" value="default"/>
</form>
</div>
<hr>
<div id="toc">
<ul>
<li class="toctree-l1"><a class="reference internal" href="tutorial.html">Tutorial</a><ul>
<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>
<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>
<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>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="api_summary.html">API Summary</a><ul>
<li class="toctree-l2"><a class="reference internal" href="api_summary.html#device">Device</a></li>
<li class="toctree-l2"><a class="reference internal" href="api_summary.html#examples-and-datasets">Examples and datasets</a></li>
<li class="toctree-l2"><a class="reference internal" href="api_summary.html#load-and-run-a-model">Load and run a model</a></li>
<li class="toctree-l2"><a class="reference internal" href="api_summary.html#backend">Backend</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="auto_examples/index.html">Gallery of examples</a><ul>
<li class="toctree-l2"><a class="reference internal" href="auto_examples/plot_backend.html">ONNX Runtime Backend for ONNX</a></li>
<li class="toctree-l2"><a class="reference internal" href="auto_examples/plot_pipeline.html">Draw a pipeline</a></li>
<li class="toctree-l2"><a class="reference internal" href="auto_examples/plot_load_and_predict.html">Load and predict with ONNX Runtime and a very simple model</a></li>
<li class="toctree-l2"><a class="reference internal" href="auto_examples/plot_profiling.html">Profile the execution of a simple model</a></li>
<li class="toctree-l2"><a class="reference internal" href="auto_examples/plot_metadata.html">Metadata</a></li>
<li class="toctree-l2"><a class="reference internal" href="auto_examples/plot_dl_keras.html">ONNX Runtime for Keras</a></li>
<li class="toctree-l2"><a class="reference internal" href="auto_examples/plot_convert_pipeline_vectorizer.html">Train, convert and predict with ONNX Runtime</a></li>
<li class="toctree-l2"><a class="reference internal" href="auto_examples/plot_common_errors.html">Common errors with onnxruntime</a></li>
<li class="toctree-l2"><a class="reference internal" href="auto_examples/plot_train_convert_predict.html">Train, convert and predict with ONNX Runtime</a></li>
</ul>
</li>
</ul>
</div>
</div>
<div class="col-xs-12 col-sm-9">
<div class="section" id="python-bindings-for-onnx-runtime">
<h1>Python Bindings for ONNX Runtime<a class="headerlink" href="#python-bindings-for-onnx-runtime" title="Permalink to this headline"></a></h1>
<p>ONNX Runtime enables high-performance evaluation of trained machine learning (ML)
models while keeping resource usage low.
Building on Microsoft’s dedication to the
<a class="reference external" href="https://onnx.ai/">Open Neural Network Exchange (ONNX)</a>
community, it supports traditional ML models as well
as Deep Learning algorithms in the
<a class="reference external" href="https://github.com/onnx/onnx/blob/master/docs/IR.md">ONNX-ML format</a>.</p>
<div class="toctree-wrapper compound">
<ul>
<li class="toctree-l1"><a class="reference internal" href="tutorial.html">Tutorial</a></li>
<li class="toctree-l1"><a class="reference internal" href="api_summary.html">API Summary</a></li>
<li class="toctree-l1"><a class="reference internal" href="auto_examples/index.html">Gallery of examples</a></li>
</ul>
</div>
<p><a class="reference internal" href="genindex.html"><span class="std std-ref">Index</span></a></p>
<p>The core library is implemented in C++.
<em>ONNX Runtime</em> is available on
PyPi for Linux Ubuntu 16.04, Python 3.5+ for both
<a class="reference external" href="https://pypi.org/project/onnxruntime/">CPU</a> and
<a class="reference external" href="https://pypi.org/project/onnxruntime-gpu/">GPU</a>.
Please see <a class="reference external" href="https://github.com/Microsoft/onnxruntime#system-requirements">system requirements</a> before installating the packages.
This example demonstrates a simple prediction for an
<a class="reference external" href="https://github.com/onnx/onnx/blob/master/docs/IR.md">ONNX-ML format</a>
model. The following file <code class="docutils literal notranslate"><span class="pre">model.onnx</span></code> is taken from
github <a class="reference external" href="https://github.com/onnx/onnx/tree/master/onnx/backend/test/data/node/test_sigmoid">onnx…test_sigmoid</a>.</p>
<p>&lt;&lt;&lt;</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span>
<span class="kn">import</span> <span class="nn">onnxruntime</span> <span class="k">as</span> <span class="nn">rt</span>
<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="s2">&quot;model.onnx&quot;</span><span class="p">)</span>
<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>
<span class="n">X</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">random</span><span class="p">((</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">numpy</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
<span class="n">pred_onnx</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>
<span class="nb">print</span><span class="p">(</span><span class="n">pred_onnx</span><span class="p">)</span>
</pre></div>
</div>
<p>&gt;&gt;&gt;</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span> <span class="p">[</span><span class="n">array</span><span class="p">([[[</span><span class="mf">0.601</span><span class="p">,</span> <span class="mf">0.631</span><span class="p">,</span> <span class="mf">0.565</span><span class="p">,</span> <span class="mf">0.501</span><span class="p">,</span> <span class="mf">0.641</span><span class="p">],</span>
<span class="p">[</span><span class="mf">0.605</span><span class="p">,</span> <span class="mf">0.609</span><span class="p">,</span> <span class="mf">0.549</span><span class="p">,</span> <span class="mf">0.656</span><span class="p">,</span> <span class="mf">0.7</span> <span class="p">],</span>
<span class="p">[</span><span class="mf">0.674</span><span class="p">,</span> <span class="mf">0.531</span><span class="p">,</span> <span class="mf">0.652</span><span class="p">,</span> <span class="mf">0.674</span><span class="p">,</span> <span class="mf">0.582</span><span class="p">],</span>
<span class="p">[</span><span class="mf">0.556</span><span class="p">,</span> <span class="mf">0.603</span><span class="p">,</span> <span class="mf">0.521</span><span class="p">,</span> <span class="mf">0.708</span><span class="p">,</span> <span class="mf">0.63</span> <span class="p">]],</span>
<span class="p">[[</span><span class="mf">0.633</span><span class="p">,</span> <span class="mf">0.684</span><span class="p">,</span> <span class="mf">0.709</span><span class="p">,</span> <span class="mf">0.589</span><span class="p">,</span> <span class="mf">0.562</span><span class="p">],</span>
<span class="p">[</span><span class="mf">0.513</span><span class="p">,</span> <span class="mf">0.647</span><span class="p">,</span> <span class="mf">0.549</span><span class="p">,</span> <span class="mf">0.644</span><span class="p">,</span> <span class="mf">0.548</span><span class="p">],</span>
<span class="p">[</span><span class="mf">0.699</span><span class="p">,</span> <span class="mf">0.671</span><span class="p">,</span> <span class="mf">0.58</span> <span class="p">,</span> <span class="mf">0.622</span><span class="p">,</span> <span class="mf">0.727</span><span class="p">],</span>
<span class="p">[</span><span class="mf">0.633</span><span class="p">,</span> <span class="mf">0.686</span><span class="p">,</span> <span class="mf">0.508</span><span class="p">,</span> <span class="mf">0.675</span><span class="p">,</span> <span class="mf">0.689</span><span class="p">]],</span>
<span class="p">[[</span><span class="mf">0.705</span><span class="p">,</span> <span class="mf">0.706</span><span class="p">,</span> <span class="mf">0.551</span><span class="p">,</span> <span class="mf">0.689</span><span class="p">,</span> <span class="mf">0.582</span><span class="p">],</span>
<span class="p">[</span><span class="mf">0.605</span><span class="p">,</span> <span class="mf">0.631</span><span class="p">,</span> <span class="mf">0.551</span><span class="p">,</span> <span class="mf">0.697</span><span class="p">,</span> <span class="mf">0.622</span><span class="p">],</span>
<span class="p">[</span><span class="mf">0.612</span><span class="p">,</span> <span class="mf">0.716</span><span class="p">,</span> <span class="mf">0.586</span><span class="p">,</span> <span class="mf">0.647</span><span class="p">,</span> <span class="mf">0.707</span><span class="p">],</span>
<span class="p">[</span><span class="mf">0.63</span> <span class="p">,</span> <span class="mf">0.657</span><span class="p">,</span> <span class="mf">0.503</span><span class="p">,</span> <span class="mf">0.71</span> <span class="p">,</span> <span class="mf">0.727</span><span class="p">]]],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">float32</span><span class="p">)]</span>
</pre></div>
</div>
</div>
</div>
</div>
</div>
<script src="https://ajax.googleapis.com/ajax/libs/jquery/3.0.0/jquery.min.js"
integrity="sha384-THPy051/pYDQGanwU6poAc/hOdQxjnOEXzbT+OuUAFqNqFjL+4IGLBgCJC3ZOShY"
crossorigin="anonymous"></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/tether/1.2.0/js/tether.min.js"
integrity="sha384-Plbmg8JY28KFelvJVai01l8WyZzrYWG825m+cZ0eDDS1f7d/js6ikvy1+X+guPIB"
crossorigin="anonymous"></script>
<script src="https://maxcdn.bootstrapcdn.com/bootstrap/4.0.0-alpha.3/js/bootstrap.min.js"
integrity="sha384-ux8v3A6CPtOTqOzMKiuo3d/DomGaaClxFYdCu2HPMBEkf6x2xiDyJ7gkXU0MWwaD"
crossorigin="anonymous"></script>
<script src='https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML'></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/lunr.js/0.6.0/lunr.min.js"></script>
<script src="_static/searchtools.js"></script>
<script>$('#searchbox').show(0)</script>
</body>
</html>
Reference in a new issue View git blame Copy permalink