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<section id="scaling-on-distributed-memory-multiprocessing">
<span id="distributed"></span><h1>Scaling on Distributed Memory (Multiprocessing)<a class="headerlink" href="#scaling-on-distributed-memory-multiprocessing" title="Permalink to this heading"></a></h1>
<div class="admonition note" id="note">
<p class="admonition-title">Note</p>
<p>Scikit-learn patching functionality in daal4py was deprecated and moved to a separate package, <a class="reference external" href="https://github.com/intel/scikit-learn-intelex">Intel(R) Extension for Scikit-learn*</a>.
All future patches will be available only in Intel(R) Extension for Scikit-learn*. Use the scikit-learn-intelex package instead of daal4py for the scikit-learn acceleration.</p>
</div>
<section id="it-s-easy">
<h2>It’s Easy<a class="headerlink" href="#it-s-easy" title="Permalink to this heading"></a></h2>
<p>daal4py operates in SPMD style (Single Program Multiple Data), which means your
program is executed on several processes (e.g. similar to MPI). The use of MPI is
not required for daal4py’s SPMD-mode to work, all necessary communication and
synchronization happens under the hood of daal4py. It is possible to use daal4py and
mpi4py in the same program, though.</p>
<p>Only very minimal changes are needed to your daal4py code to allow daal4py to
run on a cluster of workstations. Initialize the distribution engine:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">daalinit</span><span class="p">()</span>
</pre></div>
</div>
<p>Add the distribution parameter to the algorithm construction:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">kmi</span> <span class="o">=</span> <span class="n">kmeans_init</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s2">"plusPlusDense"</span><span class="p">,</span> <span class="n">distributed</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
</pre></div>
</div>
<p>When calling the actual computation each process expects an input file or input
array/DataFrame. Your program needs to tell each process which
file/array/DataFrame it should operate on. Like with other SPMD programs this is
usually done conditionally on the process id/rank (‘daal4py.my_procid()’). Assume
we have one file for each process, all having the same prefix ‘file’ and being
suffixed by a number. The code could then look like this:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">result</span> <span class="o">=</span> <span class="n">kmi</span><span class="o">.</span><span class="n">compute</span><span class="p">(</span><span class="s2">"file</span><span class="si">{}</span><span class="s2">.csv"</span><span class="p">,</span> <span class="n">daal4py</span><span class="o">.</span><span class="n">my_procid</span><span class="p">())</span>
</pre></div>
</div>
<p>The result of the computation will now be available on all processes.</p>
<p>Finally stop the distribution engine:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">daalfini</span><span class="p">()</span>
</pre></div>
</div>
<p>That’s all for the python code:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">daal4py</span> <span class="kn">import</span> <span class="n">daalinit</span><span class="p">,</span> <span class="n">daalfini</span><span class="p">,</span> <span class="n">kmeans_init</span>
<span class="n">daalinit</span><span class="p">()</span>
<span class="n">kmi</span> <span class="o">=</span> <span class="n">kmeans_init</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s2">"plusPlusDense"</span><span class="p">,</span> <span class="n">distributed</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">result</span> <span class="o">=</span> <span class="n">kmi</span><span class="o">.</span><span class="n">compute</span><span class="p">(</span><span class="s2">"file</span><span class="si">{}</span><span class="s2">.csv"</span><span class="p">,</span> <span class="n">daal4py</span><span class="o">.</span><span class="n">my_procid</span><span class="p">())</span>
<span class="n">daalfini</span><span class="p">()</span>
</pre></div>
</div>
<p>To actually get it executed on several processes use standard MPI mechanics,
like:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">mpirun</span> <span class="o">-</span><span class="n">n</span> <span class="mi">4</span> <span class="n">python</span> <span class="o">./</span><span class="n">kmeans</span><span class="o">.</span><span class="n">py</span>
</pre></div>
</div>
<p>The binaries provided by Intel use the Intel® MPI library, but
daal4py can also be compiled for any other MPI implementation.</p>
</section>
<section id="supported-algorithms-and-examples">
<h2>Supported Algorithms and Examples<a class="headerlink" href="#supported-algorithms-and-examples" title="Permalink to this heading"></a></h2>
<p>The following algorithms support distribution:</p>
<ul class="simple">
<li><p>PCA (pca)</p>
<ul>
<li><p><a class="reference external" href="https://github.com/intel/scikit-learn-intelex/tree/main/examples/daal4py/pca_spmd.py">PCA</a></p></li>
</ul>
</li>
<li><p>SVD (svd)</p>
<ul>
<li><p><a class="reference external" href="https://github.com/intel/scikit-learn-intelex/tree/main/examples/daal4py/svd_spmd.py">SVD</a></p></li>
</ul>
</li>
<li><p>Linear Regression Training (linear_regression_training)</p>
<ul>
<li><p><a class="reference external" href="https://github.com/intel/scikit-learn-intelex/tree/main/examples/daal4py/linear_regression_spmd.py">Linear Regression</a></p></li>
</ul>
</li>
<li><p>Ridge Regression Training (ridge_regression_training)</p>
<ul>
<li><p><a class="reference external" href="https://github.com/intel/scikit-learn-intelex/tree/main/examples/daal4py/ridge_regression_spmd.py">Ridge Regression</a></p></li>
</ul>
</li>
<li><p>Multinomial Naive Bayes Training (multinomial_naive_bayes_training)</p>
<ul>
<li><p><a class="reference external" href="https://github.com/intel/scikit-learn-intelex/tree/main/examples/daal4py/naive_bayes_spmd.py">Naive Bayes</a></p></li>
</ul>
</li>
<li><p>K-Means (kmeans_init and kmeans)</p>
<ul>
<li><p><a class="reference external" href="https://github.com/intel/scikit-learn-intelex/tree/main/examples/daal4py/kmeans_spmd.py">K-Means</a></p></li>
</ul>
</li>
<li><p>Correlation and Variance-Covariance Matrices (covariance)</p>
<ul>
<li><p><a class="reference external" href="https://github.com/intel/scikit-learn-intelex/tree/main/examples/daal4py/covariance_spmd.py">Covariance</a></p></li>
</ul>
</li>
<li><p>Moments of Low Order (low_order_moments)</p>
<ul>
<li><p><a class="reference external" href="https://github.com/intel/scikit-learn-intelex/tree/main/examples/daal4py/low_order_moms_spmd.py">Low Order Moments</a></p></li>
</ul>
</li>
<li><p>QR Decomposition (qr)</p>
<ul>
<li><p><a class="reference external" href="https://github.com/intel/scikit-learn-intelex/tree/main/examples/daal4py/qr_spmd.py">QR</a></p></li>
</ul>
</li>
</ul>
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