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<!DOCTYPE html>
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<title>Examples — CodeCarbon 3.0.1 documentation</title>
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<section id="examples">
<span id="id1"></span><h1>Examples<a class="headerlink" href="#examples" title="Link to this heading"></a></h1>
<p>Following are examples to train a Deep Learning model on MNIST Data to recognize digits in images using TensorFlow.</p>
<section id="using-the-decorator">
<h2>Using the Decorator<a class="headerlink" href="#using-the-decorator" title="Link to this heading"></a></h2>
<p>This is the simplest way to use the CodeCarbon tracker with two lines of code. You just need to copy-paste <cite>from codecarbon import track_emissions</cite> and add the <cite>@track_emissions</cite> decorator to your training function. The emissions will be tracked automatically and printed at the end of the training.</p>
<p>But you can’t get them in your code, see the Context Manager section below for that.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">tensorflow</span> <span class="k">as</span> <span class="nn">tf</span>
<span class="kn">from</span> <span class="nn">codecarbon</span> <span class="kn">import</span> <span class="n">track_emissions</span>
<span class="nd">@track_emissions</span><span class="p">(</span><span class="n">project_name</span><span class="o">=</span><span class="s2">"mnist"</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">train_model</span><span class="p">():</span>
<span class="n">mnist</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">datasets</span><span class="o">.</span><span class="n">mnist</span>
<span class="p">(</span><span class="n">x_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">),</span> <span class="p">(</span><span class="n">x_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">)</span> <span class="o">=</span> <span class="n">mnist</span><span class="o">.</span><span class="n">load_data</span><span class="p">()</span>
<span class="n">x_train</span><span class="p">,</span> <span class="n">x_test</span> <span class="o">=</span> <span class="n">x_train</span> <span class="o">/</span> <span class="mf">255.0</span><span class="p">,</span> <span class="n">x_test</span> <span class="o">/</span> <span class="mf">255.0</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">models</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span>
<span class="p">[</span>
<span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">Flatten</span><span class="p">(</span><span class="n">input_shape</span><span class="o">=</span><span class="p">(</span><span class="mi">28</span><span class="p">,</span> <span class="mi">28</span><span class="p">)),</span>
<span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">128</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s2">"relu"</span><span class="p">),</span>
<span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">Dropout</span><span class="p">(</span><span class="mf">0.2</span><span class="p">),</span>
<span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">10</span><span class="p">),</span>
<span class="p">]</span>
<span class="p">)</span>
<span class="n">loss_fn</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">losses</span><span class="o">.</span><span class="n">SparseCategoricalCrossentropy</span><span class="p">(</span><span class="n">from_logits</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">model</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">optimizer</span><span class="o">=</span><span class="s2">"adam"</span><span class="p">,</span> <span class="n">loss</span><span class="o">=</span><span class="n">loss_fn</span><span class="p">,</span> <span class="n">metrics</span><span class="o">=</span><span class="p">[</span><span class="s2">"accuracy"</span><span class="p">])</span>
<span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">epochs</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
<span class="k">return</span> <span class="n">model</span>
<span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s2">"__main__"</span><span class="p">:</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">train_model</span><span class="p">()</span>
</pre></div>
</div>
</section>
<section id="using-the-context-manager">
<h2>Using the Context Manager<a class="headerlink" href="#using-the-context-manager" title="Link to this heading"></a></h2>
<p>We think this is the best way to use CodeCarbon. Still only two lines of code, and you can get the emissions in your code.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">tensorflow</span> <span class="k">as</span> <span class="nn">tf</span>
<span class="kn">from</span> <span class="nn">codecarbon</span> <span class="kn">import</span> <span class="n">EmissionsTracker</span>
<span class="n">mnist</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">datasets</span><span class="o">.</span><span class="n">mnist</span>
<span class="p">(</span><span class="n">x_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">),</span> <span class="p">(</span><span class="n">x_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">)</span> <span class="o">=</span> <span class="n">mnist</span><span class="o">.</span><span class="n">load_data</span><span class="p">()</span>
<span class="n">x_train</span><span class="p">,</span> <span class="n">x_test</span> <span class="o">=</span> <span class="n">x_train</span> <span class="o">/</span> <span class="mf">255.0</span><span class="p">,</span> <span class="n">x_test</span> <span class="o">/</span> <span class="mf">255.0</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">models</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span>
<span class="p">[</span>
<span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">Flatten</span><span class="p">(</span><span class="n">input_shape</span><span class="o">=</span><span class="p">(</span><span class="mi">28</span><span class="p">,</span> <span class="mi">28</span><span class="p">)),</span>
<span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">128</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s2">"relu"</span><span class="p">),</span>
<span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">Dropout</span><span class="p">(</span><span class="mf">0.2</span><span class="p">),</span>
<span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">10</span><span class="p">),</span>
<span class="p">]</span>
<span class="p">)</span>
<span class="n">loss_fn</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">losses</span><span class="o">.</span><span class="n">SparseCategoricalCrossentropy</span><span class="p">(</span><span class="n">from_logits</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="k">with</span> <span class="n">EmissionsTracker</span><span class="p">()</span> <span class="k">as</span> <span class="n">tracker</span><span class="p">:</span>
<span class="n">model</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">optimizer</span><span class="o">=</span><span class="s2">"adam"</span><span class="p">,</span> <span class="n">loss</span><span class="o">=</span><span class="n">loss_fn</span><span class="p">,</span> <span class="n">metrics</span><span class="o">=</span><span class="p">[</span><span class="s2">"accuracy"</span><span class="p">])</span>
<span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">epochs</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
<span class="c1"># Display the emissions data</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"</span><span class="se">\n</span><span class="s2">Carbon emissions from computation: </span><span class="si">{</span><span class="n">tracker</span><span class="o">.</span><span class="n">final_emissions</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="mi">1000</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="s2"> g CO2eq"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"</span><span class="se">\n</span><span class="s2">Detailed emissions data:"</span><span class="p">,</span> <span class="n">tracker</span><span class="o">.</span><span class="n">final_emissions_data</span><span class="p">)</span>
</pre></div>
</div>
</section>
<section id="using-the-explicit-object">
<h2>Using the Explicit Object<a class="headerlink" href="#using-the-explicit-object" title="Link to this heading"></a></h2>
<p>This is the recommended way to use the CodeCarbon tracker in a Notebook : you instantiate the tracker and call the <cite>start()</cite> method at the beginning of the Notebook. You call the stop() method at the end of the Notebook to stop the tracker and get the emissions.</p>
<p>If not in an interactive Notebook, always use a <cite>try…finally</cite> block to ensure that the tracker is stopped even if an error occurs during training.
This is important to ensure the CodeCarbon scheduler is stopped. If you don’t use <cite>try…finally</cite>, the scheduler will continue running in the background after your computation code has crashed, so your program will never finish.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">tensorflow</span> <span class="k">as</span> <span class="nn">tf</span>
<span class="kn">from</span> <span class="nn">codecarbon</span> <span class="kn">import</span> <span class="n">EmissionsTracker</span>
<span class="n">mnist</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">datasets</span><span class="o">.</span><span class="n">mnist</span>
<span class="p">(</span><span class="n">x_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">),</span> <span class="p">(</span><span class="n">x_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">)</span> <span class="o">=</span> <span class="n">mnist</span><span class="o">.</span><span class="n">load_data</span><span class="p">()</span>
<span class="n">x_train</span><span class="p">,</span> <span class="n">x_test</span> <span class="o">=</span> <span class="n">x_train</span> <span class="o">/</span> <span class="mf">255.0</span><span class="p">,</span> <span class="n">x_test</span> <span class="o">/</span> <span class="mf">255.0</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">models</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span>
<span class="p">[</span>
<span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">Flatten</span><span class="p">(</span><span class="n">input_shape</span><span class="o">=</span><span class="p">(</span><span class="mi">28</span><span class="p">,</span> <span class="mi">28</span><span class="p">)),</span>
<span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">128</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s2">"relu"</span><span class="p">),</span>
<span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">Dropout</span><span class="p">(</span><span class="mf">0.2</span><span class="p">),</span>
<span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">10</span><span class="p">),</span>
<span class="p">]</span>
<span class="p">)</span>
<span class="n">loss_fn</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">losses</span><span class="o">.</span><span class="n">SparseCategoricalCrossentropy</span><span class="p">(</span><span class="n">from_logits</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">model</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">optimizer</span><span class="o">=</span><span class="s2">"adam"</span><span class="p">,</span> <span class="n">loss</span><span class="o">=</span><span class="n">loss_fn</span><span class="p">,</span> <span class="n">metrics</span><span class="o">=</span><span class="p">[</span><span class="s2">"accuracy"</span><span class="p">])</span>
<span class="n">tracker</span> <span class="o">=</span> <span class="n">EmissionsTracker</span><span class="p">()</span>
<span class="n">tracker</span><span class="o">.</span><span class="n">start</span><span class="p">()</span>
<span class="k">try</span><span class="p">:</span>
<span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">epochs</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
<span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"An error occurred: </span><span class="si">{</span><span class="n">e</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="k">finally</span><span class="p">:</span>
<span class="n">emissions</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="n">tracker</span><span class="o">.</span><span class="n">stop</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="n">emissions</span><span class="p">)</span>
</pre></div>
</div>
<p>Other examples are available in the <a class="reference external" href="https://github.com/mlco2/codecarbon/tree/master/examples">project GitHub repository</a>.</p>
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