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<div class="section" id="distributed-latent-dirichlet-allocation">
<span id="dist-lda"></span><h1>Distributed Latent Dirichlet Allocation<a class="headerlink" href="#distributed-latent-dirichlet-allocation" title="Permalink to this headline">¶</a></h1>
<div class="admonition note">
<p class="first admonition-title">Note</p>
<p class="last">See <a class="reference internal" href="distributed.html"><em>Distributed Computing</em></a> for an introduction to distributed computing in <cite>gensim</cite>.</p>
</div>
<div class="section" id="setting-up-the-cluster">
<h2>Setting up the cluster<a class="headerlink" href="#setting-up-the-cluster" title="Permalink to this headline">¶</a></h2>
<p>See the tutorial on <a class="reference internal" href="dist_lsi.html"><em>Distributed Latent Semantic Analysis</em></a>; setting up a cluster for LDA is completely
analogous, except you want to run <cite>lda_worker</cite> and <cite>lda_dispatcher</cite> scripts instead
of <cite>lsi_worker</cite> and <cite>lsi_dispatcher</cite>.</p>
</div>
<div class="section" id="running-lda">
<h2>Running LDA<a class="headerlink" href="#running-lda" title="Permalink to this headline">¶</a></h2>
<p>Run LDA like you normally would, but turn on the <cite>distributed=True</cite> constructor
parameter:</p>
<div class="highlight-python"><div class="highlight"><pre><span class="gp">>>> </span><span class="c"># extract 100 LDA topics, using default parameters</span>
<span class="gp">>>> </span><span class="n">lda</span> <span class="o">=</span> <span class="n">LdaModel</span><span class="p">(</span><span class="n">corpus</span><span class="o">=</span><span class="n">mm</span><span class="p">,</span> <span class="n">id2word</span><span class="o">=</span><span class="n">id2word</span><span class="p">,</span> <span class="n">num_topics</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">distributed</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>
<span class="go">using distributed version with 4 workers</span>
<span class="go">running online LDA training, 100 topics, 1 passes over the supplied corpus of 3199665 documets, updating model once every 40000 documents</span>
<span class="go">..</span>
</pre></div>
</div>
<p>In serial mode (no distribution), creating this online LDA <a class="reference internal" href="wiki.html"><em>model of Wikipedia</em></a>
takes 10h56m on my laptop (OS X, C2D 2.53GHz, 4GB RAM with <cite>libVec</cite>).
In distributed mode with four workers (Linux, Xeons of 2Ghz, 4GB RAM
with <a class="reference external" href="http://math-atlas.sourceforge.net/">ATLAS</a>), the wallclock time taken drops to 3h20m.</p>
<p>To run standard batch LDA (no online updates of mini-batches) instead, you would similarly
call:</p>
<div class="highlight-python"><div class="highlight"><pre><span class="gp">>>> </span><span class="n">lda</span> <span class="o">=</span> <span class="n">LdaModel</span><span class="p">(</span><span class="n">corpus</span><span class="o">=</span><span class="n">mm</span><span class="p">,</span> <span class="n">id2word</span><span class="o">=</span><span class="n">id2token</span><span class="p">,</span> <span class="n">num_topics</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">update_every</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">passes</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span> <span class="n">distributed</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>
<span class="go">using distributed version with 4 workers</span>
<span class="go">running batch LDA training, 100 topics, 20 passes over the supplied corpus of 3199665 documets, updating model once every 3199665 documents</span>
<span class="go">initializing workers</span>
<span class="go">iteration 0, dispatching documents up to #10000/3199665</span>
<span class="go">iteration 0, dispatching documents up to #20000/3199665</span>
<span class="gp">...</span>
</pre></div>
</div>
<p>and then, some two days later:</p>
<div class="highlight-python"><pre>iteration 19, dispatching documents up to #3190000/3199665
iteration 19, dispatching documents up to #3199665/3199665
reached the end of input; now waiting for all remaining jobs to finish</pre>
</div>
<div class="highlight-python"><div class="highlight"><pre><span class="gp">>>> </span><span class="n">lda</span><span class="o">.</span><span class="n">print_topics</span><span class="p">(</span><span class="mi">20</span><span class="p">)</span>
<span class="go">topic #0: 0.007*disease + 0.006*medical + 0.005*treatment + 0.005*cells + 0.005*cell + 0.005*cancer + 0.005*health + 0.005*blood + 0.004*patients + 0.004*drug</span>
<span class="go">topic #1: 0.024*king + 0.013*ii + 0.013*prince + 0.013*emperor + 0.008*duke + 0.008*empire + 0.007*son + 0.007*china + 0.007*dynasty + 0.007*iii</span>
<span class="go">topic #2: 0.031*film + 0.017*films + 0.005*movie + 0.005*directed + 0.004*man + 0.004*episode + 0.003*character + 0.003*cast + 0.003*father + 0.003*mother</span>
<span class="go">topic #3: 0.022*user + 0.012*edit + 0.009*wikipedia + 0.007*block + 0.007*my + 0.007*here + 0.007*edits + 0.007*blocked + 0.006*revert + 0.006*me</span>
<span class="go">topic #4: 0.045*air + 0.026*aircraft + 0.021*force + 0.018*airport + 0.011*squadron + 0.010*flight + 0.010*military + 0.008*wing + 0.007*aviation + 0.007*f</span>
<span class="go">topic #5: 0.025*sun + 0.022*star + 0.018*moon + 0.015*light + 0.013*stars + 0.012*planet + 0.011*camera + 0.010*mm + 0.009*earth + 0.008*lens</span>
<span class="go">topic #6: 0.037*radio + 0.026*station + 0.022*fm + 0.014*news + 0.014*stations + 0.014*channel + 0.013*am + 0.013*racing + 0.011*tv + 0.010*broadcasting</span>
<span class="go">topic #7: 0.122*image + 0.099*jpg + 0.046*file + 0.038*uploaded + 0.024*png + 0.014*contribs + 0.013*notify + 0.013*logs + 0.013*picture + 0.013*flag</span>
<span class="go">topic #8: 0.036*russian + 0.030*soviet + 0.028*polish + 0.024*poland + 0.022*russia + 0.013*union + 0.012*czech + 0.011*republic + 0.011*moscow + 0.010*finland</span>
<span class="go">topic #9: 0.031*language + 0.014*word + 0.013*languages + 0.009*term + 0.009*words + 0.008*example + 0.007*names + 0.007*meaning + 0.006*latin + 0.006*form</span>
<span class="go">topic #10: 0.029*w + 0.029*toronto + 0.023*l + 0.020*hockey + 0.019*nhl + 0.014*ontario + 0.012*calgary + 0.011*edmonton + 0.011*hamilton + 0.010*season</span>
<span class="go">topic #11: 0.110*wikipedia + 0.110*articles + 0.030*library + 0.029*wikiproject + 0.028*project + 0.019*data + 0.016*archives + 0.012*needing + 0.009*reference + 0.009*statements</span>
<span class="go">topic #12: 0.032*http + 0.030*your + 0.022*request + 0.017*sources + 0.016*archived + 0.016*modify + 0.015*changes + 0.015*creation + 0.014*www + 0.013*try</span>
<span class="go">topic #13: 0.011*your + 0.010*my + 0.009*we + 0.008*don + 0.008*get + 0.008*know + 0.007*me + 0.006*think + 0.006*question + 0.005*find</span>
<span class="go">topic #14: 0.073*r + 0.066*japanese + 0.062*japan + 0.018*tokyo + 0.008*prefecture + 0.005*osaka + 0.004*j + 0.004*sf + 0.003*kyoto + 0.003*manga</span>
<span class="go">topic #15: 0.045*da + 0.045*fr + 0.027*kategori + 0.026*pl + 0.024*nl + 0.021*pt + 0.017*en + 0.015*categoria + 0.014*es + 0.012*kategorie</span>
<span class="go">topic #16: 0.010*death + 0.005*died + 0.005*father + 0.004*said + 0.004*himself + 0.004*took + 0.004*son + 0.004*killed + 0.003*murder + 0.003*wife</span>
<span class="go">topic #17: 0.027*book + 0.021*published + 0.020*books + 0.014*isbn + 0.010*author + 0.010*magazine + 0.009*press + 0.009*novel + 0.009*writers + 0.008*story</span>
<span class="go">topic #18: 0.027*football + 0.024*players + 0.023*cup + 0.019*club + 0.017*fc + 0.017*footballers + 0.017*league + 0.011*season + 0.007*teams + 0.007*goals</span>
<span class="go">topic #19: 0.032*band + 0.024*album + 0.014*albums + 0.013*guitar + 0.013*rock + 0.011*records + 0.011*vocals + 0.009*live + 0.008*bass + 0.008*track</span>
</pre></div>
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<p>If you used the distributed LDA implementation in <cite>gensim</cite>, please let me know (my
email is at the bottom of this page). I would like to hear about your application and
the possible (inevitable?) issues that you encountered, to improve <cite>gensim</cite> in the future.</p>
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