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<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20.71 7.04c.39-.39.39-1.04 0-1.41l-2.34-2.34c-.37-.39-1.02-.39-1.41 0l-1.84 1.83 3.75 3.75M3 17.25V21h3.75L17.81 9.93l-3.75-3.75L3 17.25Z"/></svg>
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<h1 id="how-to-perform-prediction-with-greatai">How to perform prediction with GreatAI<a class="headerlink" href="#how-to-perform-prediction-with-greatai" title="Permanent link">#</a></h1>
|
|
<p>After <a href="/how-to-guides/create-service">creating a GreatAI service</a> by wrapping your prediction function, and optionally <a href="/how-to-guides/configure-service">configuring it</a>, it's time to do some prediction.</p>
|
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<p>Let's take the following example:</p>
|
|
<div class="highlight"><span class="filename">greeter.py</span><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</span> <span class="kn">import</span> <span class="n">GreatAI</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="nd">@GreatAI</span><span class="o">.</span><span class="n">create</span>
|
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<a id="__codelineno-0-4" name="__codelineno-0-4" href="#__codelineno-0-4"></a><span class="k">def</span> <span class="nf">greeter</span><span class="p">(</span><span class="n">your_name</span><span class="p">:</span> <span class="nb">str</span><span class="p">)</span> <span class="o">-></span> <span class="nb">str</span><span class="p">:</span>
|
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<a id="__codelineno-0-5" name="__codelineno-0-5" href="#__codelineno-0-5"></a> <span class="k">return</span> <span class="sa">f</span><span class="s1">'Hi </span><span class="si">{</span><span class="n">your_name</span><span class="si">}</span><span class="s1">!'</span>
|
|
</code></pre></div>
|
|
<h2 id="one-off-prediction">One-off prediction<a class="headerlink" href="#one-off-prediction" title="Permanent link">#</a></h2>
|
|
<p>Even though <code>greeter</code> is now an instance of <a class="autorefs autorefs-internal" href="../../reference/#great_ai.GreatAI">GreatAI</a>, you can continue using it as a regular function.</p>
|
|
<div class="highlight"><pre><span></span><code><a id="__codelineno-1-1" name="__codelineno-1-1" href="#__codelineno-1-1"></a><span class="o">>>></span> <span class="n">greeter</span><span class="p">(</span><span class="s1">'Bob'</span><span class="p">)</span>
|
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<a id="__codelineno-1-2" name="__codelineno-1-2" href="#__codelineno-1-2"></a><span class="n">Trace</span><span class="p">[</span><span class="nb">str</span><span class="p">]({</span><span class="s1">'created'</span><span class="p">:</span> <span class="s1">'2022-07-11T14:31:46.183764'</span><span class="p">,</span>
|
|
<a id="__codelineno-1-3" name="__codelineno-1-3" href="#__codelineno-1-3"></a> <span class="s1">'exception'</span><span class="p">:</span> <span class="kc">None</span><span class="p">,</span>
|
|
<a id="__codelineno-1-4" name="__codelineno-1-4" href="#__codelineno-1-4"></a> <span class="s1">'feedback'</span><span class="p">:</span> <span class="kc">None</span><span class="p">,</span>
|
|
<a id="__codelineno-1-5" name="__codelineno-1-5" href="#__codelineno-1-5"></a> <span class="s1">'logged_values'</span><span class="p">:</span> <span class="p">{</span><span class="s1">'arg:your_name:length'</span><span class="p">:</span> <span class="mi">3</span><span class="p">,</span> <span class="s1">'arg:your_name:value'</span><span class="p">:</span> <span class="s1">'Bob'</span><span class="p">},</span>
|
|
<a id="__codelineno-1-6" name="__codelineno-1-6" href="#__codelineno-1-6"></a> <span class="s1">'models'</span><span class="p">:</span> <span class="p">[],</span>
|
|
<a id="__codelineno-1-7" name="__codelineno-1-7" href="#__codelineno-1-7"></a> <span class="s1">'original_execution_time_ms'</span><span class="p">:</span> <span class="mf">0.0381</span><span class="p">,</span>
|
|
<a id="__codelineno-1-8" name="__codelineno-1-8" href="#__codelineno-1-8"></a> <span class="s1">'output'</span><span class="p">:</span> <span class="s1">'Hi Bob!'</span><span class="p">,</span>
|
|
<a id="__codelineno-1-9" name="__codelineno-1-9" href="#__codelineno-1-9"></a> <span class="s1">'tags'</span><span class="p">:</span> <span class="p">[</span><span class="s1">'greeter'</span><span class="p">,</span> <span class="s1">'online'</span><span class="p">,</span> <span class="s1">'development'</span><span class="p">],</span>
|
|
<a id="__codelineno-1-10" name="__codelineno-1-10" href="#__codelineno-1-10"></a> <span class="s1">'trace_id'</span><span class="p">:</span> <span class="s1">'7c284fd7-7f0d-4464-b5f8-3ef126df34af'</span><span class="p">})</span>
|
|
</code></pre></div>
|
|
<p>As you can see, the original return value is wrapped in a <a class="autorefs autorefs-internal" href="../../reference/views/#great_ai.Trace">Trace</a> object (which is also persisted in your database of choice). You can access the original value under the <code>output</code> property.</p>
|
|
<h2 id="online-prediction">Online prediction<a class="headerlink" href="#online-prediction" title="Permanent link">#</a></h2>
|
|
<p>Likely, the main way you would like to expose your model is through an HTTP API. <a class="autorefs autorefs-internal" href="../../reference/#great_ai.deploy.great_ai.GreatAI.create">@GreatAI.create</a> scaffolds many REST API endpoints for your model and creates a <a href="https://fastapi.tiangolo.com/" target="_blank">FastAPI</a> app available under <a class="autorefs autorefs-internal" href="../../reference/#great_ai.GreatAI">GreatAI.app</a>. This can be served using <a href="https://www.uvicorn.org/" target="_blank">uvicorn</a> or any other <a href="https://asgi.readthedocs.io/en/latest/" target="_blank">ASGI server</a>.</p>
|
|
<p>Since most ML code lives in <a href="https://jupyter.org/" target="_blank">Jupyter</a> notebooks, therefore, deploying a notebook containing the inference function is supported. To achieve this, <code>uvicorn</code> is wrapped by the <code>great-ai</code> command-line utility, which — among others — takes care of feeding a notebook into <code>uvicorn</code>. It also supports auto-reloading.</p>
|
|
<h3 id="in-development">In development<a class="headerlink" href="#in-development" title="Permanent link">#</a></h3>
|
|
<div class="highlight"><pre><span></span><code><a id="__codelineno-2-1" name="__codelineno-2-1" href="#__codelineno-2-1"></a>great-ai greeter.py
|
|
</code></pre></div>
|
|
<div class="admonition success">
|
|
<p class="admonition-title">Success</p>
|
|
<p>Your model is accessible at <a href="http:/127.0.0.1:6060" target="_blank">localhost:6060</a>.</p>
|
|
</div>
|
|
<p>Some configuration options are also supported.</p>
|
|
<div class="highlight"><pre><span></span><code><a id="__codelineno-3-1" name="__codelineno-3-1" href="#__codelineno-3-1"></a>great-ai greeter.py --port <span class="m">8000</span> --host <span class="m">127</span>.0.0.1 --timeout_keep_alive <span class="m">10</span>
|
|
</code></pre></div>
|
|
<details class="note">
|
|
<summary>More options</summary>
|
|
<p>For more options (but no Notebook support), simply use <a href="https://www.uvicorn.org/" target="_blank">uvicorn</a> for starting your app (available at <code>greeter.app</code>).</p>
|
|
</details>
|
|
<h3 id="in-production">In production<a class="headerlink" href="#in-production" title="Permanent link">#</a></h3>
|
|
<p>There are three main approaches for deploying a GreatAI service.</p>
|
|
<h4 id="manual-deployment">Manual deployment<a class="headerlink" href="#manual-deployment" title="Permanent link">#</a></h4>
|
|
<p>The app is run in <em>production-mode</em> if the value of the <code>ENVIRONMENT</code> environment variable is set to <code>production</code>.</p>
|
|
<div class="highlight"><pre><span></span><code><a id="__codelineno-4-1" name="__codelineno-4-1" href="#__codelineno-4-1"></a><span class="nv">ENVIRONMENT</span><span class="o">=</span>production great-ai greeter.py
|
|
</code></pre></div>
|
|
<p>Simply run <code>ENVIRONMENT=production great-ai deploy.ipynb</code> in the command-line of a production machine.</p>
|
|
<blockquote>
|
|
<p>This is the crudest approach; however, it might be fitting for some contexts.</p>
|
|
</blockquote>
|
|
<h4 id="containerised-deployment">Containerised deployment<a class="headerlink" href="#containerised-deployment" title="Permanent link">#</a></h4>
|
|
<p>Run the notebook directly in a container or create a service for it using your favourite container orchestrator.</p>
|
|
<div class="highlight"><pre><span></span><code><a id="__codelineno-5-1" name="__codelineno-5-1" href="#__codelineno-5-1"></a>docker run -p <span class="m">6060</span>:6060 --volume <span class="sb">`</span><span class="nb">pwd</span><span class="sb">`</span>:/app --rm <span class="se">\</span>
|
|
<a id="__codelineno-5-2" name="__codelineno-5-2" href="#__codelineno-5-2"></a> schmelczera/great-ai deploy.ipynb
|
|
</code></pre></div>
|
|
<blockquote>
|
|
<p>You can replace <code>pwd</code> with the path to your code's folder.</p>
|
|
</blockquote>
|
|
<h4 id="use-a-platform-as-a-service">Use a Platform-as-a-Service<a class="headerlink" href="#use-a-platform-as-a-service" title="Permanent link">#</a></h4>
|
|
<p>Similar to the previous approach, your code will run in a container. However, instead of manually managing it, you can just choose from a plethora of PaaS providers (such as <a href="https://aws.amazon.com/ecs/" target="_blank">AWS ECS</a>, <a href="https://www.digitalocean.com/products/app-platform" target="_blank">DO App platform</a>, <a href="https://mlem.ai/" target="_blank">MLEM</a>, <a href="https://streamlit.io/" target="_blank">Streamlit</a>) that take a Docker image as a source and handle the rest of the deployment.</p>
|
|
<p>To this end, you can also create a custom Docker image. It is especially useful if you have third-party dependencies, such as <a href="https://pytorch.org/" target="_blank">PyTorch</a> or <a href="https://www.tensorflow.org/" target="_blank">TensorFlow</a>.</p>
|
|
<div class="highlight"><pre><span></span><code><a id="__codelineno-6-1" name="__codelineno-6-1" href="#__codelineno-6-1"></a><span class="k">FROM</span><span class="w"> </span><span class="s">schmelczera/great-ai:latest</span>
|
|
<a id="__codelineno-6-2" name="__codelineno-6-2" href="#__codelineno-6-2"></a>
|
|
<a id="__codelineno-6-3" name="__codelineno-6-3" href="#__codelineno-6-3"></a><span class="c"># Remove this block if you don't have a requirements.txt</span>
|
|
<a id="__codelineno-6-4" name="__codelineno-6-4" href="#__codelineno-6-4"></a><span class="k">COPY</span><span class="w"> </span>requirements.txt ./
|
|
<a id="__codelineno-6-5" name="__codelineno-6-5" href="#__codelineno-6-5"></a><span class="k">RUN</span><span class="w"> </span>pip install --no-cache-dir --requirement requirements.txt
|
|
<a id="__codelineno-6-6" name="__codelineno-6-6" href="#__codelineno-6-6"></a>
|
|
<a id="__codelineno-6-7" name="__codelineno-6-7" href="#__codelineno-6-7"></a><span class="c"># If you store your models in S3 or GridFS, it may be a </span>
|
|
<a id="__codelineno-6-8" name="__codelineno-6-8" href="#__codelineno-6-8"></a><span class="c"># good idea to cache them in the image so that you don't</span>
|
|
<a id="__codelineno-6-9" name="__codelineno-6-9" href="#__codelineno-6-9"></a><span class="c"># have to download it each time a container starts</span>
|
|
<a id="__codelineno-6-10" name="__codelineno-6-10" href="#__codelineno-6-10"></a><span class="k">RUN</span><span class="w"> </span>large-file --backend s3 --secrets s3.ini --cache my-domain-predictor
|
|
<a id="__codelineno-6-11" name="__codelineno-6-11" href="#__codelineno-6-11"></a>
|
|
<a id="__codelineno-6-12" name="__codelineno-6-12" href="#__codelineno-6-12"></a><span class="c"># Add your application code to the image</span>
|
|
<a id="__codelineno-6-13" name="__codelineno-6-13" href="#__codelineno-6-13"></a><span class="k">COPY</span><span class="w"> </span>. .
|
|
<a id="__codelineno-6-14" name="__codelineno-6-14" href="#__codelineno-6-14"></a>
|
|
<a id="__codelineno-6-15" name="__codelineno-6-15" href="#__codelineno-6-15"></a><span class="c"># The default ENTRYPOINT is great-ai; specify its argument using CMD</span>
|
|
<a id="__codelineno-6-16" name="__codelineno-6-16" href="#__codelineno-6-16"></a><span class="k">CMD</span><span class="w"> </span><span class="p">[</span><span class="s2">"deploy.ipynb"</span><span class="p">]</span>
|
|
</code></pre></div>
|
|
<h2 id="batch-prediction">Batch prediction<a class="headerlink" href="#batch-prediction" title="Permanent link">#</a></h2>
|
|
<p>Processing larger amounts of data on a single machine is made easy by the <a class="autorefs autorefs-internal" href="../../reference/#great_ai.GreatAI">GreatAI</a>'s <a class="autorefs autorefs-internal" href="../../reference/#great_ai.deploy.great_ai.GreatAI.process_batch">process_batch</a> method. This relies on multiprocessing (<a class="autorefs autorefs-internal" href="../../reference/utilities/#great_ai.utilities.parallel_map.parallel_map.parallel_map">parallel_map</a>) to take full advantage of all available CPU-cores.</p>
|
|
<div class="highlight"><pre><span></span><code><a id="__codelineno-7-1" name="__codelineno-7-1" href="#__codelineno-7-1"></a><span class="o">>>></span> <span class="n">greeter</span><span class="o">.</span><span class="n">process_batch</span><span class="p">([</span><span class="s1">'Alice'</span><span class="p">,</span> <span class="s1">'Bob'</span><span class="p">])</span>
|
|
<a id="__codelineno-7-2" name="__codelineno-7-2" href="#__codelineno-7-2"></a><span class="p">[</span><span class="n">Trace</span><span class="p">[</span><span class="nb">str</span><span class="p">]({</span><span class="s1">'created'</span><span class="p">:</span> <span class="s1">'2022-07-11T14:36:37.119183'</span><span class="p">,</span>
|
|
<a id="__codelineno-7-3" name="__codelineno-7-3" href="#__codelineno-7-3"></a> <span class="s1">'exception'</span><span class="p">:</span> <span class="kc">None</span><span class="p">,</span>
|
|
<a id="__codelineno-7-4" name="__codelineno-7-4" href="#__codelineno-7-4"></a> <span class="s1">'feedback'</span><span class="p">:</span> <span class="kc">None</span><span class="p">,</span>
|
|
<a id="__codelineno-7-5" name="__codelineno-7-5" href="#__codelineno-7-5"></a> <span class="s1">'logged_values'</span><span class="p">:</span> <span class="p">{</span><span class="s1">'arg:your_name:length'</span><span class="p">:</span> <span class="mi">5</span><span class="p">,</span> <span class="s1">'arg:your_name:value'</span><span class="p">:</span> <span class="s1">'Alice'</span><span class="p">},</span>
|
|
<a id="__codelineno-7-6" name="__codelineno-7-6" href="#__codelineno-7-6"></a> <span class="s1">'models'</span><span class="p">:</span> <span class="p">[],</span>
|
|
<a id="__codelineno-7-7" name="__codelineno-7-7" href="#__codelineno-7-7"></a> <span class="s1">'original_execution_time_ms'</span><span class="p">:</span> <span class="mf">0.1251</span><span class="p">,</span>
|
|
<a id="__codelineno-7-8" name="__codelineno-7-8" href="#__codelineno-7-8"></a> <span class="s1">'output'</span><span class="p">:</span> <span class="s1">'Hi Alice!'</span><span class="p">,</span>
|
|
<a id="__codelineno-7-9" name="__codelineno-7-9" href="#__codelineno-7-9"></a> <span class="s1">'tags'</span><span class="p">:</span> <span class="p">[</span><span class="s1">'greeter'</span><span class="p">,</span> <span class="s1">'online'</span><span class="p">,</span> <span class="s1">'development'</span><span class="p">],</span>
|
|
<a id="__codelineno-7-10" name="__codelineno-7-10" href="#__codelineno-7-10"></a> <span class="s1">'trace_id'</span><span class="p">:</span> <span class="s1">'90ffa15f-e839-41c4-8e7a-3211168bc138'</span><span class="p">}),</span>
|
|
<a id="__codelineno-7-11" name="__codelineno-7-11" href="#__codelineno-7-11"></a> <span class="n">Trace</span><span class="p">[</span><span class="nb">str</span><span class="p">]({</span><span class="s1">'created'</span><span class="p">:</span> <span class="s1">'2022-07-11T14:36:37.166659'</span><span class="p">,</span>
|
|
<a id="__codelineno-7-12" name="__codelineno-7-12" href="#__codelineno-7-12"></a> <span class="s1">'exception'</span><span class="p">:</span> <span class="kc">None</span><span class="p">,</span>
|
|
<a id="__codelineno-7-13" name="__codelineno-7-13" href="#__codelineno-7-13"></a> <span class="s1">'feedback'</span><span class="p">:</span> <span class="kc">None</span><span class="p">,</span>
|
|
<a id="__codelineno-7-14" name="__codelineno-7-14" href="#__codelineno-7-14"></a> <span class="s1">'logged_values'</span><span class="p">:</span> <span class="p">{</span><span class="s1">'arg:your_name:length'</span><span class="p">:</span> <span class="mi">3</span><span class="p">,</span> <span class="s1">'arg:your_name:value'</span><span class="p">:</span> <span class="s1">'Bob'</span><span class="p">},</span>
|
|
<a id="__codelineno-7-15" name="__codelineno-7-15" href="#__codelineno-7-15"></a> <span class="s1">'models'</span><span class="p">:</span> <span class="p">[],</span>
|
|
<a id="__codelineno-7-16" name="__codelineno-7-16" href="#__codelineno-7-16"></a> <span class="s1">'original_execution_time_ms'</span><span class="p">:</span> <span class="mf">0.0571</span><span class="p">,</span>
|
|
<a id="__codelineno-7-17" name="__codelineno-7-17" href="#__codelineno-7-17"></a> <span class="s1">'output'</span><span class="p">:</span> <span class="s1">'Hi Bob!'</span><span class="p">,</span>
|
|
<a id="__codelineno-7-18" name="__codelineno-7-18" href="#__codelineno-7-18"></a> <span class="s1">'tags'</span><span class="p">:</span> <span class="p">[</span><span class="s1">'greeter'</span><span class="p">,</span> <span class="s1">'online'</span><span class="p">,</span> <span class="s1">'development'</span><span class="p">],</span>
|
|
<a id="__codelineno-7-19" name="__codelineno-7-19" href="#__codelineno-7-19"></a> <span class="s1">'trace_id'</span><span class="p">:</span> <span class="s1">'f48e94c7-0815-48b3-a864-41349d3dae84'</span><span class="p">})]</span>
|
|
</code></pre></div>
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