TRAINS - AutoKeras

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TextRegressor </span> </a> </li> <li class="md-nav__item"> <a href="../../auto_model/" class="md-nav__link"> <span class="md-ellipsis"> AutoModel </span> </a> </li> <li class="md-nav__item"> <a href="../../base/" class="md-nav__link"> <span class="md-ellipsis"> Base Class </span> </a> </li> <li class="md-nav__item"> <a href="../../node/" class="md-nav__link"> <span class="md-ellipsis"> Node </span> </a> </li> <li class="md-nav__item"> <a href="../../block/" class="md-nav__link"> <span class="md-ellipsis"> Block </span> </a> </li> <li class="md-nav__item"> <a href="../../utils/" class="md-nav__link"> <span class="md-ellipsis"> Utils </span> </a> </li> </ul> </nav> </li> <li class="md-nav__item"> <a href="../../benchmarks/" class="md-nav__link"> <span class="md-ellipsis"> Benchmarks </span> </a> </li> <li class="md-nav__item"> <a href="../../about/" class="md-nav__link"> <span class="md-ellipsis"> About </span> </a> </li> </ul> </nav> </div> </div> </div> <div class="md-sidebar md-sidebar--secondary" data-md-component="sidebar" data-md-type="toc" > <div class="md-sidebar__scrollwrap"> <div class="md-sidebar__inner"> <nav class="md-nav md-nav--secondary" aria-label="Table of contents"> <label class="md-nav__title" for="__toc"> <span class="md-nav__icon md-icon"></span> Table of contents </label> <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix> <li class="md-nav__item"> <a href="#setting-up-trains" class="md-nav__link"> <span class="md-ellipsis"> Setting up Trains </span> </a> </li> <li class="md-nav__item"> <a href="#tracking-your-autokeras-tasks" class="md-nav__link"> <span class="md-ellipsis"> Tracking your AutoKeras tasks </span> </a> <nav class="md-nav" aria-label="Tracking your AutoKeras tasks"> <ul class="md-nav__list"> <li class="md-nav__item"> <a href="#visualizing-task-results" class="md-nav__link"> <span class="md-ellipsis"> Visualizing Task Results </span> </a> </li> <li class="md-nav__item"> <a href="#task-models" class="md-nav__link"> <span class="md-ellipsis"> Task Models </span> </a> </li> <li class="md-nav__item"> <a href="#tracking-model-performance" class="md-nav__link"> <span class="md-ellipsis"> Tracking Model Performance </span> </a> </li> <li class="md-nav__item"> <a href="#model-development-insights" class="md-nav__link"> <span class="md-ellipsis"> Model Development Insights </span> </a> </li> </ul> </nav> </li> </ul> </nav> </div> </div> </div> <div class="md-content" data-md-component="content"> <article class="md-content__inner md-typeset"> <h1 id="trains-integration">Trains Integration</h1> <p>Allegro Trains is a full system open source ML / DL experiment manager and ML-Ops solution. It enables data scientists and data engineers to effortlessly track, manage, compare and collaborate on their experiments as well as easily manage their training workloads on remote machines.</p> <p><strong>Trains</strong> is a suite of open source Python packages and plugins, including:</p> <ul> <li><a href="https://github.com/allegroai/trains"><strong>Trains</strong></a> Python Client package - Integrate <strong>Trains</strong> into your AutoKeras tasks with just two lines of code, and get all of <strong>Trains</strong> robust features. </li> <li><a href="https://github.com/allegroai/trains-server"><strong>Trains Server</strong></a> - The <strong>Trains</strong> backend infrastructure and web UI. Use the public <a href="https://demoapp.trains.allegro.ai"><strong>Trains</strong> demo server</a>, or deploy your own.</li> <li><a href="https://github.com/allegroai/trains-agent"><strong>Trains Agent</strong></a> - The <strong>Trains</strong> DevOps component for experiment execution, resource control, and autoML..</li> <li>Additional integrations - Integrate <strong>Trains</strong> with <a href="https://github.com/allegroai/trains-pycharm-plugin">PyCharm</a> and <a href="https://github.com/allegroai/trains-jupyter-plugin">Jupyter Notebook</a>. </li> </ul> <p><img src="https://allegro.ai/docs/img/trains/gif/webapp_screenshots.gif"></p> <h2 id="setting-up-trains">Setting up Trains</h2> <p>To integrate <strong>Trains</strong> into your AutoKeras project, do the following:</p> <ol> <li> <p>Install the <strong>Trains</strong> Python Client package.</p> <div class="codehilite"><pre><span></span><code>pip install trains </code></pre></div> </li> <li> <p>Add the short <strong>Trains</strong> initialization code to your task.</p> <div class="codehilite"><pre><span></span><code><span class="kn">from</span> <span class="nn">trains</span> <span class="kn">import</span> <span class="n">Task</span> <span class="n">task</span> <span class="o">=</span> <span class="n">Task</span><span class="o">.</span><span class="n">init</span><span class="p">(</span><span class="n">project_name</span><span class="o">=</span><span class="s2">"autokeras"</span><span class="p">,</span> <span class="n">task_name</span><span class="o">=</span><span class="s2">"autokeras imdb example with scalars"</span><span class="p">)</span> </code></pre></div> </li> <li> <p>Run your task. The console output will include the URL of the task's <strong>RESULTS</strong> page.</p> <div class="codehilite"><pre><span></span><code>TRAINS<span class="w"> </span>Task:<span class="w"> </span>overwriting<span class="w"> </span>(reusing)<span class="w"> </span>task<span class="w"> </span>id=60763e04c0ba45ea9fe3cfe79f3f06a3 TRAINS<span class="w"> </span>results<span class="w"> </span>page:<span class="w"> </span>https://demoapp.trains.allegro.ai/projects/21643e0f1c4a4c99953302fc88a1a84c/experiments/60763e04c0ba45ea9fe3cfe79f3f06a3/output/log<span class="nt"></code></pre></span> </code></pre></div> </li> </ol> <p>See an example script <a href="https://github.com/allegroai/trains/blob/master/examples/autokeras/autokeras_imdb_example.py">here</a>.</p> <h2 id="tracking-your-autokeras-tasks">Tracking your AutoKeras tasks</h2> <h3 id="visualizing-task-results">Visualizing Task Results</h3> <p><strong>Trains</strong> automatically logs comprehensive information about your AutoKeras task: code source control, execution environment, hyperparameters and more.<br /> It also automatically records any scalars, histograms and images reported to Tensorboard/Matplotlib or Seaborn.</p> <p>For example, making use of Tensorboard in your task will make all recorded information available in <strong>Trains</strong> as well:</p> <div class="highlight"><pre><span></span><code><span class="kn">from</span> <span class="nn">tensorflow</span> <span class="kn">import</span> <span class="n">keras</span> <span class="n">tensorboard_callback_train</span> <span class="o">=</span> <span class="n">keras</span><span class="o">.</span><span class="n">callbacks</span><span class="o">.</span><span class="n">TensorBoard</span><span class="p">(</span><span class="n">log_dir</span><span class="o">=</span><span class="s1">'log'</span><span class="p">)</span> <span class="n">tensorboard_callback_test</span> <span class="o">=</span> <span class="n">keras</span><span class="o">.</span><span class="n">callbacks</span><span class="o">.</span><span class="n">TensorBoard</span><span class="p">(</span><span class="n">log_dir</span><span class="o">=</span><span class="s1">'log'</span><span class="p">)</span> <span class="n">clf</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">2</span><span class="p">,</span> <span class="n">callbacks</span><span class="o">=</span><span class="p">[</span><span class="n">tensorboard_callback_train</span><span class="p">])</span> <span class="n">clf</span><span class="o">.</span><span class="n">fit</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="n">epochs</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">callbacks</span><span class="o">=</span><span class="p">[</span><span class="n">tensorboard_callback_test</span><span class="p">])</span> </code></pre></div> <p>When your task runs, you can follow its results, including any collected metrics through the <strong>Trains</strong> web UI.</p> <p>View your task results in the <strong>Trains</strong> web UI, by clicking on it in the <strong>EXPERIMENTS</strong> table.<br /> Find the <strong>EXPERIMENT</strong> table under the specified project listed in the <strong>HOME</strong> or <strong>PROJECTS</strong> page: </p> <p><img src="https://allegro-datasets.s3.amazonaws.com/erez/Selection_028.png" style="border: 1px solid black; border-radius:3px"></p> <p>Detailed description <strong>Trains</strong> Web UI experiment information can be obtained <a href="https://allegro.ai/docs/webapp/webapp_exp_details/">here</a>.<br /> Additional information on <strong>Trains</strong> logging capabilities can be obtained in the <a href="https://allegro.ai/docs/concepts_arch/concepts_arch/#logging">relevant <strong>Trains</strong> Documentation</a></p> <h3 id="task-models">Task Models</h3> <p><strong>Trains</strong> automatically tracks models produced by your AutoKeras tasks.</p> <p>To upload models, specify the <code>output_uri</code> parameter when calling <code>Task.init</code> to provide the upload destination:</p> <div class="codehilite"><pre><span></span><code> task = Task.init(project_name="autokeras", task_name="autokeras imdb example with scalars", output_uri="http://localhost:8081/") </code></pre></div> <p>View models information in the experiment details panel, <strong>ARTIFACTS</strong> tab: </p> <p><img id="myImg_01" class="modalImg" src="https://allegro-datasets.s3.amazonaws.com/erez/Selection_029.png" style="border: 1px solid black; border-radius:3px"></p> <h3 id="tracking-model-performance">Tracking Model Performance</h3> <p>Use the <strong>Trains</strong> web UI to easily create experiment leaderboards and quickly identify best performing models.<br /> Customize your board adding any valuable metric or hyperparameter.</p> <p><img id="myImg_03" class="modalImg" src="https://allegro-datasets.s3.amazonaws.com/erez/Selection_031.png" style="border: 1px solid black; border-radius:3px"> </p> <p>Additional information on customizing <strong>Trains</strong> experiment and model tables can be obtained in the <a href="https://allegro.ai/docs/webapp/webapp_exp_table/#customize-the-experiments-table">relevant <strong>Trains</strong> Documentation</a></p> <h3 id="model-development-insights">Model Development Insights</h3> <p>Use the <strong>Trains</strong> web UI to view side-by-side comparison of experiments: Easily locate the differences and impact of experiment configuration parameters, metrics, scalars etc.</p> <p>Compare multiple experiments, by selecting two or more experiments in the <strong>EXPERIMENTS</strong> table, and clicking <strong>COMPARE</strong>.</p> <p>The following image shows how two experiments compare in their epoch_accuracy and epoch_loss behaviour:</p> <p><img id="myImg_02" class="modalImg" src="https://allegro-datasets.s3.amazonaws.com/erez/Selection_030.png" style="border: 1px solid black; 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TRAINS - AutoKeras