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<li class="toctree-l1 has-children"><a class="reference internal" href="supervised_learning.html">1. Supervised learning</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
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<li class="toctree-l2"><a class="reference internal" href="modules/model_evaluation.html">3.4. Metrics and scoring: quantifying the quality of predictions</a></li>
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<li class="toctree-l2"><a class="reference internal" href="modules/unsupervised_reduction.html">6.5. Unsupervised dimensionality reduction</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/random_projection.html">6.6. Random Projection</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/kernel_approximation.html">6.7. Kernel Approximation</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/metrics.html">6.8. Pairwise metrics, Affinities and Kernels</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/preprocessing_targets.html">6.9. Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</a></li>
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<li class="toctree-l2"><a class="reference internal" href="datasets/toy_dataset.html">7.1. Toy datasets</a></li>
<li class="toctree-l2"><a class="reference internal" href="datasets/real_world.html">7.2. Real world datasets</a></li>
<li class="toctree-l2"><a class="reference internal" href="datasets/sample_generators.html">7.3. Generated datasets</a></li>
<li class="toctree-l2"><a class="reference internal" href="datasets/loading_other_datasets.html">7.4. Loading other datasets</a></li>
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<li class="toctree-l2"><a class="reference internal" href="computing/scaling_strategies.html">8.1. Strategies to scale computationally: bigger data</a></li>
<li class="toctree-l2"><a class="reference internal" href="computing/computational_performance.html">8.2. Computational Performance</a></li>
<li class="toctree-l2"><a class="reference internal" href="computing/parallelism.html">8.3. Parallelism, resource management, and configuration</a></li>
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<li class="toctree-l1 current active"><a class="current reference internal" href="#">9. Model persistence</a></li>
<li class="toctree-l1"><a class="reference internal" href="common_pitfalls.html">10. Common pitfalls and recommended practices</a></li>
<li class="toctree-l1 has-children"><a class="reference internal" href="dispatching.html">11. Dispatching</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l2"><a class="reference internal" href="modules/array_api.html">11.1. Array API support (experimental)</a></li>
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<li class="toctree-l1"><a class="reference internal" href="machine_learning_map.html">12. Choosing the right estimator</a></li>
<li class="toctree-l1"><a class="reference internal" href="presentations.html">13. External Resources, Videos and Talks</a></li>
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<section id="model-persistence">
<span id="id1"></span><h1><span class="section-number">9. </span>Model persistence<a class="headerlink" href="#model-persistence" title="Link to this heading">#</a></h1>
<div class="pst-scrollable-table-container"><table class="table" id="id2">
<caption><span class="caption-text">Summary of model persistence methods</span><a class="headerlink" href="#id2" title="Link to this table">#</a></caption>
<colgroup>
<col style="width: 20.0%" />
<col style="width: 40.0%" />
<col style="width: 40.0%" />
</colgroup>
<thead>
<tr class="row-odd"><th class="head"><p>Persistence method</p></th>
<th class="head"><p>Pros</p></th>
<th class="head"><p>Risks / Cons</p></th>
</tr>
</thead>
<tbody>
<tr class="row-even"><td><p><a class="reference internal" href="#onnx-persistence"><span class="std std-ref">ONNX</span></a></p></td>
<td><ul class="simple">
<li><p>Serve models without a Python environment</p></li>
<li><p>Serving and training environments independent of one another</p></li>
<li><p>Most secure option</p></li>
</ul>
</td>
<td><ul class="simple">
<li><p>Not all scikit-learn models are supported</p></li>
<li><p>Custom estimators require more work to support</p></li>
<li><p>Original Python object is lost and cannot be reconstructed</p></li>
</ul>
</td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#skops-persistence"><span class="std std-ref">skops.io</span></a></p></td>
<td><ul class="simple">
<li><p>More secure than <code class="docutils literal notranslate"><span class="pre">pickle</span></code> based formats</p></li>
<li><p>Contents can be partly validated without loading</p></li>
</ul>
</td>
<td><ul class="simple">
<li><p>Not as fast as <code class="docutils literal notranslate"><span class="pre">pickle</span></code> based formats</p></li>
<li><p>Supports less types than <code class="docutils literal notranslate"><span class="pre">pickle</span></code> based formats</p></li>
<li><p>Requires the same environment as the training environment</p></li>
</ul>
</td>
</tr>
<tr class="row-even"><td><p><a class="reference external" href="https://docs.python.org/3/library/pickle.html#module-pickle" title="(in Python v3.13)"><code class="xref py py-mod docutils literal notranslate"><span class="pre">pickle</span></code></a></p></td>
<td><ul class="simple">
<li><p>Native to Python</p></li>
<li><p>Can serialize most Python objects</p></li>
<li><p>Efficient memory usage with <code class="docutils literal notranslate"><span class="pre">protocol=5</span></code></p></li>
</ul>
</td>
<td><ul class="simple">
<li><p>Loading can execute arbitrary code</p></li>
<li><p>Requires the same environment as the training environment</p></li>
</ul>
</td>
</tr>
<tr class="row-odd"><td><p><a class="reference external" href="https://joblib.readthedocs.io/en/latest/index.html#module-joblib" title="(in joblib v1.5.dev0)"><code class="xref py py-mod docutils literal notranslate"><span class="pre">joblib</span></code></a></p></td>
<td><ul class="simple">
<li><p>Efficient memory usage</p></li>
<li><p>Supports memory mapping</p></li>
<li><p>Easy shortcuts for compression and decompression</p></li>
</ul>
</td>
<td><ul class="simple">
<li><p>Pickle based format</p></li>
<li><p>Loading can execute arbitrary code</p></li>
<li><p>Requires the same environment as the training environment</p></li>
</ul>
</td>
</tr>
<tr class="row-even"><td><p><a class="reference external" href="https://github.com/cloudpipe/cloudpickle">cloudpickle</a></p></td>
<td><ul class="simple">
<li><p>Can serialize non-packaged, custom Python code</p></li>
<li><p>Comparable loading efficiency as <a class="reference external" href="https://docs.python.org/3/library/pickle.html#module-pickle" title="(in Python v3.13)"><code class="xref py py-mod docutils literal notranslate"><span class="pre">pickle</span></code></a> with <code class="docutils literal notranslate"><span class="pre">protocol=5</span></code></p></li>
</ul>
</td>
<td><ul class="simple">
<li><p>Pickle based format</p></li>
<li><p>Loading can execute arbitrary code</p></li>
<li><p>No forward compatibility guarantees</p></li>
<li><p>Requires the same environment as the training environment</p></li>
</ul>
</td>
</tr>
</tbody>
</table>
</div>
<p>After training a scikit-learn model, it is desirable to have a way to persist
the model for future use without having to retrain. Based on your use-case,
there are a few different ways to persist a scikit-learn model, and here we
help you decide which one suits you best. In order to make a decision, you need
to answer the following questions:</p>
<ol class="arabic simple">
<li><p>Do you need the Python object after persistence, or do you only need to
persist in order to serve the model and get predictions out of it?</p></li>
</ol>
<p>If you only need to serve the model and no further investigation on the Python
object itself is required, then <a class="reference internal" href="#onnx-persistence"><span class="std std-ref">ONNX</span></a> might be the
best fit for you. Note that not all models are supported by ONNX.</p>
<p>In case ONNX is not suitable for your use-case, the next question is:</p>
<ol class="arabic simple" start="2">
<li><p>Do you absolutely trust the source of the model, or are there any security
concerns regarding where the persisted model comes from?</p></li>
</ol>
<p>If you have security concerns, then you should consider using <a class="reference internal" href="#skops-persistence"><span class="std std-ref">skops.io</span></a> which gives you back the Python object, but unlike
<code class="docutils literal notranslate"><span class="pre">pickle</span></code> based persistence solutions, loading the persisted model doesn’t
automatically allow arbitrary code execution. Note that this requires manual
investigation of the persisted file, which <a class="reference external" href="https://skops.readthedocs.io/en/stable/modules/classes.html#module-skops.io" title="(in skops)"><code class="xref py py-mod docutils literal notranslate"><span class="pre">skops.io</span></code></a> allows you to do.</p>
<p>The other solutions assume you absolutely trust the source of the file to be
loaded, as they are all susceptible to arbitrary code execution upon loading
the persisted file since they all use the pickle protocol under the hood.</p>
<ol class="arabic simple" start="3">
<li><p>Do you care about the performance of loading the model, and sharing it
between processes where a memory mapped object on disk is beneficial?</p></li>
</ol>
<p>If yes, then you can consider using <a class="reference internal" href="#pickle-persistence"><span class="std std-ref">joblib</span></a>. If this
is not a major concern for you, then you can use the built-in <a class="reference external" href="https://docs.python.org/3/library/pickle.html#module-pickle" title="(in Python v3.13)"><code class="xref py py-mod docutils literal notranslate"><span class="pre">pickle</span></code></a>
module.</p>
<ol class="arabic simple" start="4">
<li><p>Did you try <a class="reference external" href="https://docs.python.org/3/library/pickle.html#module-pickle" title="(in Python v3.13)"><code class="xref py py-mod docutils literal notranslate"><span class="pre">pickle</span></code></a> or <a class="reference external" href="https://joblib.readthedocs.io/en/latest/index.html#module-joblib" title="(in joblib v1.5.dev0)"><code class="xref py py-mod docutils literal notranslate"><span class="pre">joblib</span></code></a> and found that the model cannot
be persisted? It can happen for instance when you have user defined
functions in your model.</p></li>
</ol>
<p>If yes, then you can use <a class="reference external" href="https://github.com/cloudpipe/cloudpickle">cloudpickle</a> which can serialize certain objects
which cannot be serialized by <a class="reference external" href="https://docs.python.org/3/library/pickle.html#module-pickle" title="(in Python v3.13)"><code class="xref py py-mod docutils literal notranslate"><span class="pre">pickle</span></code></a> or <a class="reference external" href="https://joblib.readthedocs.io/en/latest/index.html#module-joblib" title="(in joblib v1.5.dev0)"><code class="xref py py-mod docutils literal notranslate"><span class="pre">joblib</span></code></a>.</p>
<section id="workflow-overview">
<h2><span class="section-number">9.1. </span>Workflow Overview<a class="headerlink" href="#workflow-overview" title="Link to this heading">#</a></h2>
<p>In a typical workflow, the first step is to train the model using scikit-learn
and scikit-learn compatible libraries. Note that support for scikit-learn and
third party estimators varies across the different persistence methods.</p>
<section id="train-and-persist-the-model">
<h3><span class="section-number">9.1.1. </span>Train and Persist the Model<a class="headerlink" href="#train-and-persist-the-model" title="Link to this heading">#</a></h3>
<p>Creating an appropriate model depends on your use-case. As an example, here we
train a <a class="reference internal" href="modules/generated/sklearn.ensemble.HistGradientBoostingClassifier.html#sklearn.ensemble.HistGradientBoostingClassifier" title="sklearn.ensemble.HistGradientBoostingClassifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.ensemble.HistGradientBoostingClassifier</span></code></a> on the iris
dataset:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">ensemble</span>
<span class="gp">>>> </span><span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">datasets</span>
<span class="gp">>>> </span><span class="n">clf</span> <span class="o">=</span> <span class="n">ensemble</span><span class="o">.</span><span class="n">HistGradientBoostingClassifier</span><span class="p">()</span>
<span class="gp">>>> </span><span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">datasets</span><span class="o">.</span><span class="n">load_iris</span><span class="p">(</span><span class="n">return_X_y</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">clf</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
<span class="go">HistGradientBoostingClassifier()</span>
</pre></div>
</div>
<p>Once the model is trained, you can persist it using your desired method, and
then you can load the model in a separate environment and get predictions from
it given input data. Here there are two major paths depending on how you
persist and plan to serve the model:</p>
<ul class="simple">
<li><p><a class="reference internal" href="#onnx-persistence"><span class="std std-ref">ONNX</span></a>: You need an <code class="docutils literal notranslate"><span class="pre">ONNX</span></code> runtime and an environment
with appropriate dependencies installed to load the model and use the runtime
to get predictions. This environment can be minimal and does not necessarily
even require Python to be installed to load the model and compute
predictions. Also note that <code class="docutils literal notranslate"><span class="pre">onnxruntime</span></code> typically requires much less RAM
than Python to compute predictions from small models.</p></li>
<li><p><a class="reference external" href="https://skops.readthedocs.io/en/stable/modules/classes.html#module-skops.io" title="(in skops)"><code class="xref py py-mod docutils literal notranslate"><span class="pre">skops.io</span></code></a>, <a class="reference external" href="https://docs.python.org/3/library/pickle.html#module-pickle" title="(in Python v3.13)"><code class="xref py py-mod docutils literal notranslate"><span class="pre">pickle</span></code></a>, <a class="reference external" href="https://joblib.readthedocs.io/en/latest/index.html#module-joblib" title="(in joblib v1.5.dev0)"><code class="xref py py-mod docutils literal notranslate"><span class="pre">joblib</span></code></a>, <a class="reference external" href="https://github.com/cloudpipe/cloudpickle">cloudpickle</a>: You need a
Python environment with the appropriate dependencies installed to load the
model and get predictions from it. This environment should have the same
<strong>packages</strong> and the same <strong>versions</strong> as the environment where the model was
trained. Note that none of these methods support loading a model trained with
a different version of scikit-learn, and possibly different versions of other
dependencies such as <code class="docutils literal notranslate"><span class="pre">numpy</span></code> and <code class="docutils literal notranslate"><span class="pre">scipy</span></code>. Another concern would be running
the persisted model on a different hardware, and in most cases you should be
able to load your persisted model on a different hardware.</p></li>
</ul>
</section>
</section>
<section id="onnx">
<span id="onnx-persistence"></span><h2><span class="section-number">9.2. </span>ONNX<a class="headerlink" href="#onnx" title="Link to this heading">#</a></h2>
<p><code class="docutils literal notranslate"><span class="pre">ONNX</span></code>, or <a class="reference external" href="https://onnx.ai/">Open Neural Network Exchange</a> format is best
suitable in use-cases where one needs to persist the model and then use the
persisted artifact to get predictions without the need to load the Python
object itself. It is also useful in cases where the serving environment needs
to be lean and minimal, since the <code class="docutils literal notranslate"><span class="pre">ONNX</span></code> runtime does not require <code class="docutils literal notranslate"><span class="pre">python</span></code>.</p>
<p><code class="docutils literal notranslate"><span class="pre">ONNX</span></code> is a binary serialization of the model. It has been developed to improve
the usability of the interoperable representation of data models. It aims to
facilitate the conversion of the data models between different machine learning
frameworks, and to improve their portability on different computing
architectures. More details are available from the <a class="reference external" href="https://onnx.ai/get-started.html">ONNX tutorial</a>. To convert scikit-learn model to <code class="docutils literal notranslate"><span class="pre">ONNX</span></code>
<a class="reference external" href="http://onnx.ai/sklearn-onnx/">sklearn-onnx</a> has been developed. However,
not all scikit-learn models are supported, and it is limited to the core
scikit-learn and does not support most third party estimators. One can write a
custom converter for third party or custom estimators, but the documentation to
do that is sparse and it might be challenging to do so.</p>
<details class="sd-sphinx-override sd-dropdown sd-card sd-mb-3" id="using-onnx">
<summary class="sd-summary-title sd-card-header">
<span class="sd-summary-text">Using ONNX<a class="headerlink" href="#using-onnx" title="Link to this dropdown">#</a></span><span class="sd-summary-state-marker sd-summary-chevron-right"><svg version="1.1" width="1.5em" height="1.5em" class="sd-octicon sd-octicon-chevron-right" viewBox="0 0 24 24" aria-hidden="true"><path d="M8.72 18.78a.75.75 0 0 1 0-1.06L14.44 12 8.72 6.28a.751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018l6.25 6.25a.75.75 0 0 1 0 1.06l-6.25 6.25a.75.75 0 0 1-1.06 0Z"></path></svg></span></summary><div class="sd-summary-content sd-card-body docutils">
<p class="sd-card-text">To convert the model to <code class="docutils literal notranslate"><span class="pre">ONNX</span></code> format, you need to give the converter some
information about the input as well, about which you can read more <a class="reference external" href="http://onnx.ai/sklearn-onnx/index.html">here</a>:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">skl2onnx</span> <span class="kn">import</span> <span class="n">to_onnx</span>
<span class="n">onx</span> <span class="o">=</span> <span class="n">to_onnx</span><span class="p">(</span><span class="n">clf</span><span class="p">,</span> <span class="n">X</span><span class="p">[:</span><span class="mi">1</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">target_opset</span><span class="o">=</span><span class="mi">12</span><span class="p">)</span>
<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="s2">"filename.onnx"</span><span class="p">,</span> <span class="s2">"wb"</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
<span class="n">f</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="n">onx</span><span class="o">.</span><span class="n">SerializeToString</span><span class="p">())</span>
</pre></div>
</div>
<p class="sd-card-text">You can load the model in Python and use the <code class="docutils literal notranslate"><span class="pre">ONNX</span></code> runtime to get
predictions:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">onnxruntime</span> <span class="kn">import</span> <span class="n">InferenceSession</span>
<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="s2">"filename.onnx"</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>
<span class="n">onx</span> <span class="o">=</span> <span class="n">f</span><span class="o">.</span><span class="n">read</span><span class="p">()</span>
<span class="n">sess</span> <span class="o">=</span> <span class="n">InferenceSession</span><span class="p">(</span><span class="n">onx</span><span class="p">,</span> <span class="n">providers</span><span class="o">=</span><span class="p">[</span><span class="s2">"CPUExecutionProvider"</span><span class="p">])</span>
<span class="n">pred_ort</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="s2">"X"</span><span class="p">:</span> <span class="n">X_test</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="mi">0</span><span class="p">]</span>
</pre></div>
</div>
</div>
</details></section>
<section id="skops-io">
<span id="skops-persistence"></span><h2><span class="section-number">9.3. </span><code class="docutils literal notranslate"><span class="pre">skops.io</span></code><a class="headerlink" href="#skops-io" title="Link to this heading">#</a></h2>
<p><a class="reference external" href="https://skops.readthedocs.io/en/stable/modules/classes.html#module-skops.io" title="(in skops)"><code class="xref py py-mod docutils literal notranslate"><span class="pre">skops.io</span></code></a> avoids using <a class="reference external" href="https://docs.python.org/3/library/pickle.html#module-pickle" title="(in Python v3.13)"><code class="xref py py-mod docutils literal notranslate"><span class="pre">pickle</span></code></a> and only loads files which have types
and references to functions which are trusted either by default or by the user.
Therefore it provides a more secure format than <a class="reference external" href="https://docs.python.org/3/library/pickle.html#module-pickle" title="(in Python v3.13)"><code class="xref py py-mod docutils literal notranslate"><span class="pre">pickle</span></code></a>, <a class="reference external" href="https://joblib.readthedocs.io/en/latest/index.html#module-joblib" title="(in joblib v1.5.dev0)"><code class="xref py py-mod docutils literal notranslate"><span class="pre">joblib</span></code></a>,
and <a class="reference external" href="https://github.com/cloudpipe/cloudpickle">cloudpickle</a>.</p>
<details class="sd-sphinx-override sd-dropdown sd-card sd-mb-3" id="using-skops">
<summary class="sd-summary-title sd-card-header">
<span class="sd-summary-text">Using skops<a class="headerlink" href="#using-skops" title="Link to this dropdown">#</a></span><span class="sd-summary-state-marker sd-summary-chevron-right"><svg version="1.1" width="1.5em" height="1.5em" class="sd-octicon sd-octicon-chevron-right" viewBox="0 0 24 24" aria-hidden="true"><path d="M8.72 18.78a.75.75 0 0 1 0-1.06L14.44 12 8.72 6.28a.751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018l6.25 6.25a.75.75 0 0 1 0 1.06l-6.25 6.25a.75.75 0 0 1-1.06 0Z"></path></svg></span></summary><div class="sd-summary-content sd-card-body docutils">
<p class="sd-card-text">The API is very similar to <a class="reference external" href="https://docs.python.org/3/library/pickle.html#module-pickle" title="(in Python v3.13)"><code class="xref py py-mod docutils literal notranslate"><span class="pre">pickle</span></code></a>, and you can persist your models as
explained in the <a class="reference external" href="https://skops.readthedocs.io/en/stable/persistence.html">documentation</a> using
<a class="reference external" href="https://skops.readthedocs.io/en/stable/modules/classes.html#skops.io.dump" title="(in skops)"><code class="xref py py-func docutils literal notranslate"><span class="pre">skops.io.dump</span></code></a> and <a class="reference external" href="https://skops.readthedocs.io/en/stable/modules/classes.html#skops.io.dumps" title="(in skops)"><code class="xref py py-func docutils literal notranslate"><span class="pre">skops.io.dumps</span></code></a>:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">skops.io</span> <span class="k">as</span> <span class="nn">sio</span>
<span class="n">obj</span> <span class="o">=</span> <span class="n">sio</span><span class="o">.</span><span class="n">dump</span><span class="p">(</span><span class="n">clf</span><span class="p">,</span> <span class="s2">"filename.skops"</span><span class="p">)</span>
</pre></div>
</div>
<p class="sd-card-text">And you can load them back using <a class="reference external" href="https://skops.readthedocs.io/en/stable/modules/classes.html#skops.io.load" title="(in skops)"><code class="xref py py-func docutils literal notranslate"><span class="pre">skops.io.load</span></code></a> and
<a class="reference external" href="https://skops.readthedocs.io/en/stable/modules/classes.html#skops.io.loads" title="(in skops)"><code class="xref py py-func docutils literal notranslate"><span class="pre">skops.io.loads</span></code></a>. However, you need to specify the types which are
trusted by you. You can get existing unknown types in a dumped object / file
using <a class="reference external" href="https://skops.readthedocs.io/en/stable/modules/classes.html#skops.io.get_untrusted_types" title="(in skops)"><code class="xref py py-func docutils literal notranslate"><span class="pre">skops.io.get_untrusted_types</span></code></a>, and after checking its contents,
pass it to the load function:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">unknown_types</span> <span class="o">=</span> <span class="n">sio</span><span class="o">.</span><span class="n">get_untrusted_types</span><span class="p">(</span><span class="n">file</span><span class="o">=</span><span class="s2">"filename.skops"</span><span class="p">)</span>
<span class="c1"># investigate the contents of unknown_types, and only load if you trust</span>
<span class="c1"># everything you see.</span>
<span class="n">clf</span> <span class="o">=</span> <span class="n">sio</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s2">"filename.skops"</span><span class="p">,</span> <span class="n">trusted</span><span class="o">=</span><span class="n">unknown_types</span><span class="p">)</span>
</pre></div>
</div>
<p class="sd-card-text">Please report issues and feature requests related to this format on the <a class="reference external" href="https://github.com/skops-dev/skops/issues">skops
issue tracker</a>.</p>
</div>
</details></section>
<section id="pickle-joblib-and-cloudpickle">
<span id="pickle-persistence"></span><h2><span class="section-number">9.4. </span><code class="docutils literal notranslate"><span class="pre">pickle</span></code>, <code class="docutils literal notranslate"><span class="pre">joblib</span></code>, and <code class="docutils literal notranslate"><span class="pre">cloudpickle</span></code><a class="headerlink" href="#pickle-joblib-and-cloudpickle" title="Link to this heading">#</a></h2>
<p>These three modules / packages, use the <code class="docutils literal notranslate"><span class="pre">pickle</span></code> protocol under the hood, but
come with slight variations:</p>
<ul class="simple">
<li><p><a class="reference external" href="https://docs.python.org/3/library/pickle.html#module-pickle" title="(in Python v3.13)"><code class="xref py py-mod docutils literal notranslate"><span class="pre">pickle</span></code></a> is a module from the Python Standard Library. It can serialize
and deserialize any Python object, including custom Python classes and
objects.</p></li>
<li><p><a class="reference external" href="https://joblib.readthedocs.io/en/latest/index.html#module-joblib" title="(in joblib v1.5.dev0)"><code class="xref py py-mod docutils literal notranslate"><span class="pre">joblib</span></code></a> is more efficient than <code class="docutils literal notranslate"><span class="pre">pickle</span></code> when working with large machine
learning models or large numpy arrays.</p></li>
<li><p><a class="reference external" href="https://github.com/cloudpipe/cloudpickle">cloudpickle</a> can serialize certain objects which cannot be serialized by
<a class="reference external" href="https://docs.python.org/3/library/pickle.html#module-pickle" title="(in Python v3.13)"><code class="xref py py-mod docutils literal notranslate"><span class="pre">pickle</span></code></a> or <a class="reference external" href="https://joblib.readthedocs.io/en/latest/index.html#module-joblib" title="(in joblib v1.5.dev0)"><code class="xref py py-mod docutils literal notranslate"><span class="pre">joblib</span></code></a>, such as user defined functions and lambda
functions. This can happen for instance, when using a
<a class="reference internal" href="modules/generated/sklearn.preprocessing.FunctionTransformer.html#sklearn.preprocessing.FunctionTransformer" title="sklearn.preprocessing.FunctionTransformer"><code class="xref py py-class docutils literal notranslate"><span class="pre">FunctionTransformer</span></code></a> and using a custom
function to transform the data.</p></li>
</ul>
<details class="sd-sphinx-override sd-dropdown sd-card sd-mb-3" id="using-pickle,-joblib,-or-cloudpickle">
<summary class="sd-summary-title sd-card-header">
<span class="sd-summary-text">Using <code class="docutils literal notranslate"><span class="pre">pickle</span></code>, <code class="docutils literal notranslate"><span class="pre">joblib</span></code>, or <code class="docutils literal notranslate"><span class="pre">cloudpickle</span></code><a class="headerlink" href="#using-pickle,-joblib,-or-cloudpickle" title="Link to this dropdown">#</a></span><span class="sd-summary-state-marker sd-summary-chevron-right"><svg version="1.1" width="1.5em" height="1.5em" class="sd-octicon sd-octicon-chevron-right" viewBox="0 0 24 24" aria-hidden="true"><path d="M8.72 18.78a.75.75 0 0 1 0-1.06L14.44 12 8.72 6.28a.751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018l6.25 6.25a.75.75 0 0 1 0 1.06l-6.25 6.25a.75.75 0 0 1-1.06 0Z"></path></svg></span></summary><div class="sd-summary-content sd-card-body docutils">
<p class="sd-card-text">Depending on your use-case, you can choose one of these three methods to
persist and load your scikit-learn model, and they all follow the same API:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Here you can replace pickle with joblib or cloudpickle</span>
<span class="kn">from</span> <span class="nn">pickle</span> <span class="kn">import</span> <span class="n">dump</span>
<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="s2">"filename.pkl"</span><span class="p">,</span> <span class="s2">"wb"</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
<span class="n">dump</span><span class="p">(</span><span class="n">clf</span><span class="p">,</span> <span class="n">f</span><span class="p">,</span> <span class="n">protocol</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
</pre></div>
</div>
<p class="sd-card-text">Using <code class="docutils literal notranslate"><span class="pre">protocol=5</span></code> is recommended to reduce memory usage and make it faster to
store and load any large NumPy array stored as a fitted attribute in the model.
You can alternatively pass <code class="docutils literal notranslate"><span class="pre">protocol=pickle.HIGHEST_PROTOCOL</span></code> which is
equivalent to <code class="docutils literal notranslate"><span class="pre">protocol=5</span></code> in Python 3.8 and later (at the time of writing).</p>
<p class="sd-card-text">And later when needed, you can load the same object from the persisted file:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Here you can replace pickle with joblib or cloudpickle</span>
<span class="kn">from</span> <span class="nn">pickle</span> <span class="kn">import</span> <span class="n">load</span>
<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="s2">"filename.pkl"</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>
<span class="n">clf</span> <span class="o">=</span> <span class="n">load</span><span class="p">(</span><span class="n">f</span><span class="p">)</span>
</pre></div>
</div>
</div>
</details></section>
<section id="security-maintainability-limitations">
<span id="persistence-limitations"></span><h2><span class="section-number">9.5. </span>Security & Maintainability Limitations<a class="headerlink" href="#security-maintainability-limitations" title="Link to this heading">#</a></h2>
<p><a class="reference external" href="https://docs.python.org/3/library/pickle.html#module-pickle" title="(in Python v3.13)"><code class="xref py py-mod docutils literal notranslate"><span class="pre">pickle</span></code></a> (and <a class="reference external" href="https://joblib.readthedocs.io/en/latest/index.html#module-joblib" title="(in joblib v1.5.dev0)"><code class="xref py py-mod docutils literal notranslate"><span class="pre">joblib</span></code></a> and <code class="xref py py-mod docutils literal notranslate"><span class="pre">clouldpickle</span></code> by extension), has
many documented security vulnerabilities by design and should only be used if
the artifact, i.e. the pickle-file, is coming from a trusted and verified
source. You should never load a pickle file from an untrusted source, similarly