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<h1 id="train-and-deploy-a-sota-model">Train and deploy a SOTA model<a class="headerlink" href="#train-and-deploy-a-sota-model" title="Permanent link">#</a></h1>
<p>Let's see <code>great-ai</code> in action by going over the life-cycle of a simple service.</p>
<h2 id="objectives">Objectives<a class="headerlink" href="#objectives" title="Permanent link">#</a></h2>
<ol>
<li>You will see how the <a href="/reference/utilities">great_ai.utilities</a> can integrate into your Data Science workflow.</li>
<li>You will use <a href="/reference/large_file">great_ai.large_file</a> to version and store your trained model.</li>
<li>You will use <a class="autorefs autorefs-internal" href="../reference/#great_ai.GreatAI">GreatAI</a> to prepare your model for a robust and responsible deployment.</li>
</ol>
<h2 id="overview">Overview<a class="headerlink" href="#overview" title="Permanent link">#</a></h2>
<p>You are going to train a field of study (domain) classifier for scientific sentences. The exact task was proposed by the <a href="https://arxiv.org/abs/1903.10676">SciBERT paper</a> in which SciBERT <a href="https://paperswithcode.com/sota/sentence-classification-on-paper-field">achieved an F1-score of 0.6571</a>. We are going to outperform it using a trivial text classification model: a <a href="https://scikit-learn.org/stable/modules/generated/sklearn.svm.LinearSVC.html">Linear SVM</a>.</p>
<p>We use the same synthetic dataset derived from the <a href="https://www.microsoft.com/en-us/research/project/microsoft-academic-graph/">Microsoft Academic Graph</a>. The dataset is <a href="https://github.com/allenai/scibert/tree/master/data/text_classification/mag">available here</a>.</p>
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<p class="admonition-title">Success</p>
<p>You are ready to start the tutorial. Feel free to come back to the <a href="#summary">summary</a> section once you're finished.</p>
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<h2 id="summary">Summary<a class="headerlink" href="#summary" title="Permanent link">#</a></h2>
<h3 id="training-notebook"><a href="train/">Training notebook</a><a class="headerlink" href="#training-notebook" title="Permanent link">#</a></h3>
<p>We load and preprocess the dataset while relying on <a class="autorefs autorefs-internal" href="../reference/utilities/#great_ai.utilities.clean.clean">great_ai.utilities.clean</a> for doing the heavy-lifting. Additionally, the preprocessing is parallelised using <a class="autorefs autorefs-internal" href="../reference/utilities/#great_ai.utilities.simple_parallel_map">great_ai.utilities.simple_parallel_map</a></p>
<p>After training and evaluating a model, it is exported using <a class="autorefs autorefs-internal" href="../reference/#great_ai.save_model">great_ai.save_model</a>.</p>
<details class="tip">
<summary>Remote storage</summary>
<p>To store your model remotely, you need to set your credentials before calling <code>save_model</code>.</p>
<p>For example, to use <a href="https://aws.amazon.com/s3" target="_blank">AWS S3</a>:
<div class="highlight"><pre><span></span><code><a id="__codelineno-0-1" name="__codelineno-0-1" href="#__codelineno-0-1"></a><span class="kn">from</span> <span class="nn">great_ai.large_file</span> <span class="kn">import</span> <span class="n">LargeFileS3</span>
<a id="__codelineno-0-2" name="__codelineno-0-2" href="#__codelineno-0-2"></a>
<a id="__codelineno-0-3" name="__codelineno-0-3" href="#__codelineno-0-3"></a><span class="n">LargeFileS3</span><span class="o">.</span><span class="n">configure</span><span class="p">(</span>
<a id="__codelineno-0-4" name="__codelineno-0-4" href="#__codelineno-0-4"></a> <span class="n">aws_region_name</span><span class="o">=</span><span class="s1">&#39;eu-west-2&#39;</span><span class="p">,</span>
<a id="__codelineno-0-5" name="__codelineno-0-5" href="#__codelineno-0-5"></a> <span class="n">aws_access_key_id</span><span class="o">=</span><span class="s1">&#39;MY_AWS_ACCESS_KEY&#39;</span><span class="p">,</span>
<a id="__codelineno-0-6" name="__codelineno-0-6" href="#__codelineno-0-6"></a> <span class="n">aws_secret_access_key</span><span class="o">=</span><span class="s1">&#39;MY_AWS_SECRET_KEY&#39;</span><span class="p">,</span>
<a id="__codelineno-0-7" name="__codelineno-0-7" href="#__codelineno-0-7"></a> <span class="n">large_files_bucket_name</span><span class="o">=</span><span class="s1">&#39;my_bucket_for_models&#39;</span>
<a id="__codelineno-0-8" name="__codelineno-0-8" href="#__codelineno-0-8"></a><span class="p">)</span>
<a id="__codelineno-0-9" name="__codelineno-0-9" href="#__codelineno-0-9"></a>
<a id="__codelineno-0-10" name="__codelineno-0-10" href="#__codelineno-0-10"></a><span class="kn">from</span> <span class="nn">great_ai</span> <span class="kn">import</span> <span class="n">save_model</span>
<a id="__codelineno-0-11" name="__codelineno-0-11" href="#__codelineno-0-11"></a>
<a id="__codelineno-0-12" name="__codelineno-0-12" href="#__codelineno-0-12"></a><span class="n">save_model</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">key</span><span class="o">=</span><span class="s1">&#39;my-domain-predictor&#39;</span><span class="p">)</span>
</code></pre></div></p>
<p>For more info, checkout <a href="/how-to-guides/configure-service">the configuration how-to page</a>.</p>
</details>
<h3 id="deployment-notebook"><a href="deploy/">Deployment notebook</a><a class="headerlink" href="#deployment-notebook" title="Permanent link">#</a></h3>
<p>We create an inference function that can be hardened by wrapping it in a <a class="autorefs autorefs-internal" href="../reference/#great_ai.GreatAI">GreatAI</a> instance.</p>
<div class="highlight"><pre><span></span><code><a id="__codelineno-1-1" name="__codelineno-1-1" href="#__codelineno-1-1"></a><span class="kn">from</span> <span class="nn">great_ai</span> <span class="kn">import</span> <span class="n">GreatAI</span><span class="p">,</span> <span class="n">use_model</span>
<a id="__codelineno-1-2" name="__codelineno-1-2" href="#__codelineno-1-2"></a><span class="kn">from</span> <span class="nn">great_ai.utilities</span> <span class="kn">import</span> <span class="n">clean</span>
<a id="__codelineno-1-3" name="__codelineno-1-3" href="#__codelineno-1-3"></a>
<a id="__codelineno-1-4" name="__codelineno-1-4" href="#__codelineno-1-4"></a><span class="nd">@GreatAI</span><span class="o">.</span><span class="n">create</span>
<a id="__codelineno-1-5" name="__codelineno-1-5" href="#__codelineno-1-5"></a><span class="nd">@use_model</span><span class="p">(</span><span class="s1">&#39;my-domain-predictor&#39;</span><span class="p">)</span> <span class="c1">#(1)</span>
<a id="__codelineno-1-6" name="__codelineno-1-6" href="#__codelineno-1-6"></a><span class="k">def</span> <span class="nf">predict_domain</span><span class="p">(</span><span class="n">sentence</span><span class="p">,</span> <span class="n">model</span><span class="p">):</span>
<a id="__codelineno-1-7" name="__codelineno-1-7" href="#__codelineno-1-7"></a> <span class="n">inputs</span> <span class="o">=</span> <span class="p">[</span><span class="n">clean</span><span class="p">(</span><span class="n">sentence</span><span class="p">)]</span>
<a id="__codelineno-1-8" name="__codelineno-1-8" href="#__codelineno-1-8"></a> <span class="k">return</span> <span class="nb">str</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">inputs</span><span class="p">)[</span><span class="mi">0</span><span class="p">])</span>
</code></pre></div>
<ol>
<li><a class="autorefs autorefs-internal" href="../reference/#great_ai.use_model">@use_model</a> loads and injects your model into the <code>predict_domain</code> function's <code>model</code> argument.
You can freely reference it knowing that the function is always provided with it.</li>
</ol>
<p>Finally, we test the model's inference function through the GreatAI dashboard. <a href="/how-to-guides/use-service">The only thing left is to deploy the hardened-service properly.</a></p>
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<p><a class="md-button md-button--primary" href="/how-to-guides/create-service"><span class="twemoji"><svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M18 22a2 2 0 0 0 2-2V4a2 2 0 0 0-2-2h-6v7L9.5 7.5 7 9V2H6a2 2 0 0 0-2 2v16a2 2 0 0 0 2 2h12Z"/></svg></span> Learn about all the features</a></p>
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