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@ -12,19 +12,19 @@
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@ -109,19 +109,19 @@
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@ -230,7 +230,7 @@
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@ -271,8 +271,8 @@
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@ -299,7 +299,7 @@
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@ -483,13 +497,13 @@
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<h1>Index</h1> <h1>Call remote</h1>
@ -498,7 +512,7 @@
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Last update: Last update:
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# How to instantiate a GreatAI service
The core value of the `great-ai` library lies in its [great_ai.deploy.GreatAI][] class. In order to take advantage of it, you need to create an instance wrapping your code.
Let's say that you have the following greeter function:
```python title="greeter.py"
def my_greeter_function(your_name):
return f'Hi {your_name}!'
```
You can simply decorate (wrap) this function with the `GreatAI.create` factory.
```python title="greeter.py"
from great_ai import GreatAI
@GreatAI.create
def greeter(your_name):
return f'Hi {your_name}!'
```
??? info "Why not simply use `@GreatAI?`"
The purpose of the `GreatAI.create` is simply to provide you with type-checking through MyPy, Pylance, and similar libraries. However, the overloading support for `__new__` is lacking in MyPy, thus, a static factory method is used instead.
## With types
[Type annotating your codebase](https://realpython.com/python-type-checking/){ target=_blank } can save you from lots of trivial mistakes, that's why it's highly advised. Simply add the expected types to your function's signature.
```python title="type_safe_greeter.py"
from great_ai import GreatAI
@GreatAI.create
def type_safe_greeter(your_name: str) -> str:
return f'Hi {your_name}!'
```
This not only allows you to statically typecheck your code, but by default, GreatAI will check it during runtime as well using [typeguard](https://github.com/agronholm/typeguard){ target=_blank }.
## With async
Asynchronous code can result in immense performance gains in certain cases. For example, you might rely on a third-party service, do database access, or [call a remote GreatAI instance](/how-to-guides/call-remote). In these cases, you can simply make your function `async` without any other changes.
```python title="async_greeter.py"
from great_ai import GreatAI
from asyncio import sleep
@GreatAI.create
async def async_greeter(your_name: str) -> str:
await sleep(2) # simulate IO-heavy operation
return f'Hi {your_name}!'
```
## With decorators
GreatAI can decorate already decorated functions. The only restriction is that `@GreatAI.create` always have to come last. There are two built-in decorators that you can use to customise your function.
### Using `use_model`
If you have previously saved a model with `save_model`, you can inject it into your function by calling `use_model`.
```python title="greeter_with_model.py"
from great_ai import GreatAI, use_model
@GreatAI.create
@use_model('name_of_my_model', version='latest') #(1)
def type_safe_greeter(your_name: str, model) -> str:
return f'Hi {your_name}!'
assert type_safe_greeter('Andras').output == 'Hi Andras'
```
1. By default, the parameter named `model` will be replaced by the loaded model. This behaviour can be customised by setting the `model_kwarg_name`. This way, even multiple models can be injected into a single function.
!!! important
You must call `@use_model` before `GreatAI.create`. Feel free to use `@use_model` in other places of the code base, it works equally well outside of GreatAI services.
### Using `parameter`
If you wish to turn of logging or specify custom validation for your parameters, you can use the `@parameter` decorator.
!!! note
By default, all parameters that are not affected by an explicit `@parameter` or `@use_model` decorator, are automatically decorated with `@parameter` when `GreatAI.create` is called.
```python "greeter_with_validation.py"
from great_ai import GreatAI, use_model
@GreatAI.create
@use_model('name_of_my_model', version='latest')
def type_safe_greeter(your_name: str, model) -> str:
return f'Hi {your_name}!'
assert type_safe_greeter('Andras').output == 'Hi Andras'
```
!!! important
You must call `@parameter` before `GreatAI.create`. Feel free to use `@parameter` in other places of the code base, it works equally well outside of GreatAI services.
## Complex example
Refer to the following example summarising the options you have when instantiating a GreatAI service.
```python title="complex.py"
from great_ai import save_model, GreatAI, parameter, use_model
save_model(4, 'secret-number') #(2)
@GreatAI.create
@parameter('positive_number', validator=lambda n: n > 0)
@use_model('secret-number', version='latest', model_kwarg_name='secret')
def add_number(positive_number: int, secret: int) -> int:
return positive_number + secret
assert add_number(1).output == 5
```
2. Refer to [storing models](/how-to-guides/store-models) for specifying where to store your models.

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<h1 id="how-to-instantiate-a-greatai-service">How to instantiate a GreatAI service<a class="headerlink" href="#how-to-instantiate-a-greatai-service" title="Permanent link">#</a></h1>
<p>The core value of the <code>great-ai</code> library lies in its <a class="autorefs autorefs-internal" href="../../reference/#great_ai.GreatAI">great_ai.deploy.GreatAI</a> class. In order to take advantage of it, you need to create an instance wrapping your code.</p>
<p>Let's say that you have the following greeter function:</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="k">def</span> <span class="nf">my_greeter_function</span><span class="p">(</span><span class="n">your_name</span><span class="p">):</span>
<a id="__codelineno-0-2" name="__codelineno-0-2" href="#__codelineno-0-2"></a> <span class="k">return</span> <span class="sa">f</span><span class="s1">&#39;Hi </span><span class="si">{</span><span class="n">your_name</span><span class="si">}</span><span class="s1">!&#39;</span>
</code></pre></div>
<p>You can simply decorate (wrap) this function with the <code>GreatAI.create</code> factory.</p>
<div class="highlight"><span class="filename">greeter.py</span><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>
<a id="__codelineno-1-2" name="__codelineno-1-2" href="#__codelineno-1-2"></a>
<a id="__codelineno-1-3" name="__codelineno-1-3" href="#__codelineno-1-3"></a><span class="nd">@GreatAI</span><span class="o">.</span><span class="n">create</span>
<a id="__codelineno-1-4" name="__codelineno-1-4" href="#__codelineno-1-4"></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>
<a id="__codelineno-1-5" name="__codelineno-1-5" href="#__codelineno-1-5"></a> <span class="k">return</span> <span class="sa">f</span><span class="s1">&#39;Hi </span><span class="si">{</span><span class="n">your_name</span><span class="si">}</span><span class="s1">!&#39;</span>
</code></pre></div>
<details class="info">
<summary>Why not simply use <code>@GreatAI?</code></summary>
<p>The purpose of the <code>GreatAI.create</code> is simply to provide you with type-checking through MyPy, Pylance, and similar libraries. However, the overloading support for <code>__new__</code> is lacking in MyPy, thus, a static factory method is used instead.</p>
</details>
<h2 id="with-types">With types<a class="headerlink" href="#with-types" title="Permanent link">#</a></h2>
<p><a href="https://realpython.com/python-type-checking/" target="_blank">Type annotating your codebase</a> can save you from lots of trivial mistakes, that's why it's highly advised. Simply add the expected types to your function's signature.</p>
<div class="highlight"><span class="filename">type_safe_greeter.py</span><pre><span></span><code><a id="__codelineno-2-1" name="__codelineno-2-1" href="#__codelineno-2-1"></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-2-2" name="__codelineno-2-2" href="#__codelineno-2-2"></a>
<a id="__codelineno-2-3" name="__codelineno-2-3" href="#__codelineno-2-3"></a><span class="nd">@GreatAI</span><span class="o">.</span><span class="n">create</span>
<a id="__codelineno-2-4" name="__codelineno-2-4" href="#__codelineno-2-4"></a><span class="k">def</span> <span class="nf">type_safe_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">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
<a id="__codelineno-2-5" name="__codelineno-2-5" href="#__codelineno-2-5"></a> <span class="k">return</span> <span class="sa">f</span><span class="s1">&#39;Hi </span><span class="si">{</span><span class="n">your_name</span><span class="si">}</span><span class="s1">!&#39;</span>
</code></pre></div>
<p>This not only allows you to statically typecheck your code, but by default, GreatAI will check it during runtime as well using <a href="https://github.com/agronholm/typeguard" target="_blank">typeguard</a>.</p>
<h2 id="with-async">With async<a class="headerlink" href="#with-async" title="Permanent link">#</a></h2>
<p>Asynchronous code can result in immense performance gains in certain cases. For example, you might rely on a third-party service, do database access, or <a href="/how-to-guides/call-remote">call a remote GreatAI instance</a>. In these cases, you can simply make your function <code>async</code> without any other changes.</p>
<div class="highlight"><span class="filename">async_greeter.py</span><pre><span></span><code><a id="__codelineno-3-1" name="__codelineno-3-1" href="#__codelineno-3-1"></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-3-2" name="__codelineno-3-2" href="#__codelineno-3-2"></a><span class="kn">from</span> <span class="nn">asyncio</span> <span class="kn">import</span> <span class="n">sleep</span>
<a id="__codelineno-3-3" name="__codelineno-3-3" href="#__codelineno-3-3"></a>
<a id="__codelineno-3-4" name="__codelineno-3-4" href="#__codelineno-3-4"></a><span class="nd">@GreatAI</span><span class="o">.</span><span class="n">create</span>
<a id="__codelineno-3-5" name="__codelineno-3-5" href="#__codelineno-3-5"></a><span class="k">async</span> <span class="k">def</span> <span class="nf">async_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">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
<a id="__codelineno-3-6" name="__codelineno-3-6" href="#__codelineno-3-6"></a> <span class="k">await</span> <span class="n">sleep</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span> <span class="c1"># simulate IO-heavy operation</span>
<a id="__codelineno-3-7" name="__codelineno-3-7" href="#__codelineno-3-7"></a> <span class="k">return</span> <span class="sa">f</span><span class="s1">&#39;Hi </span><span class="si">{</span><span class="n">your_name</span><span class="si">}</span><span class="s1">!&#39;</span>
</code></pre></div>
<h2 id="with-decorators">With decorators<a class="headerlink" href="#with-decorators" title="Permanent link">#</a></h2>
<p>GreatAI can decorate already decorated functions. The only restriction is that <code>@GreatAI.create</code> always have to come last. There are two built-in decorators that you can use to customise your function.</p>
<h3 id="using-use_model">Using <code>use_model</code><a class="headerlink" href="#using-use_model" title="Permanent link">#</a></h3>
<p>If you have previously saved a model with <code>save_model</code>, you can inject it into your function by calling <code>use_model</code>.</p>
<div class="highlight"><span class="filename">greeter_with_model.py</span><pre><span></span><code><a id="__codelineno-4-1" name="__codelineno-4-1" href="#__codelineno-4-1"></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-4-2" name="__codelineno-4-2" href="#__codelineno-4-2"></a>
<a id="__codelineno-4-3" name="__codelineno-4-3" href="#__codelineno-4-3"></a><span class="nd">@GreatAI</span><span class="o">.</span><span class="n">create</span>
<a id="__codelineno-4-4" name="__codelineno-4-4" href="#__codelineno-4-4"></a><span class="nd">@use_model</span><span class="p">(</span><span class="s1">&#39;name_of_my_model&#39;</span><span class="p">,</span> <span class="n">version</span><span class="o">=</span><span class="s1">&#39;latest&#39;</span><span class="p">)</span> <span class="c1">#(1)</span>
<a id="__codelineno-4-5" name="__codelineno-4-5" href="#__codelineno-4-5"></a><span class="k">def</span> <span class="nf">type_safe_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="n">model</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
<a id="__codelineno-4-6" name="__codelineno-4-6" href="#__codelineno-4-6"></a> <span class="k">return</span> <span class="sa">f</span><span class="s1">&#39;Hi </span><span class="si">{</span><span class="n">your_name</span><span class="si">}</span><span class="s1">!&#39;</span>
<a id="__codelineno-4-7" name="__codelineno-4-7" href="#__codelineno-4-7"></a>
<a id="__codelineno-4-8" name="__codelineno-4-8" href="#__codelineno-4-8"></a><span class="k">assert</span> <span class="n">type_safe_greeter</span><span class="p">(</span><span class="s1">&#39;Andras&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">output</span> <span class="o">==</span> <span class="s1">&#39;Hi Andras&#39;</span>
</code></pre></div>
<ol>
<li>By default, the parameter named <code>model</code> will be replaced by the loaded model. This behaviour can be customised by setting the <code>model_kwarg_name</code>. This way, even multiple models can be injected into a single function.</li>
</ol>
<div class="admonition important">
<p class="admonition-title">Important</p>
<p>You must call <code>@use_model</code> before <code>GreatAI.create</code>. Feel free to use <code>@use_model</code> in other places of the code base, it works equally well outside of GreatAI services. </p>
</div>
<h3 id="using-parameter">Using <code>parameter</code><a class="headerlink" href="#using-parameter" title="Permanent link">#</a></h3>
<p>If you wish to turn of logging or specify custom validation for your parameters, you can use the <code>@parameter</code> decorator.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>By default, all parameters that are not affected by an explicit <code>@parameter</code> or <code>@use_model</code> decorator, are automatically decorated with <code>@parameter</code> when <code>GreatAI.create</code> is called.</p>
</div>
<p>```python "greeter_with_validation.py"
from great_ai import GreatAI, use_model</p>
<p>@GreatAI.create
@use_model('name_of_my_model', version='latest')
def type_safe_greeter(your_name: str, model) -&gt; str:
return f'Hi {your_name}!'</p>
<p>assert type_safe_greeter('Andras').output == 'Hi Andras'
<div class="highlight"><pre><span></span><code><a id="__codelineno-5-1" name="__codelineno-5-1" href="#__codelineno-5-1"></a>!!! important
<a id="__codelineno-5-2" name="__codelineno-5-2" href="#__codelineno-5-2"></a> You must call `@parameter` before `GreatAI.create`. Feel free to use `@parameter` in other places of the code base, it works equally well outside of GreatAI services.
<a id="__codelineno-5-3" name="__codelineno-5-3" href="#__codelineno-5-3"></a>
<a id="__codelineno-5-4" name="__codelineno-5-4" href="#__codelineno-5-4"></a>
<a id="__codelineno-5-5" name="__codelineno-5-5" href="#__codelineno-5-5"></a>## Complex example
<a id="__codelineno-5-6" name="__codelineno-5-6" href="#__codelineno-5-6"></a>
<a id="__codelineno-5-7" name="__codelineno-5-7" href="#__codelineno-5-7"></a>Refer to the following example summarising the options you have when instantiating a GreatAI service.
<a id="__codelineno-5-8" name="__codelineno-5-8" href="#__codelineno-5-8"></a>
<a id="__codelineno-5-9" name="__codelineno-5-9" href="#__codelineno-5-9"></a>```python title=&quot;complex.py&quot;
<a id="__codelineno-5-10" name="__codelineno-5-10" href="#__codelineno-5-10"></a>from great_ai import save_model, GreatAI, parameter, use_model
<a id="__codelineno-5-11" name="__codelineno-5-11" href="#__codelineno-5-11"></a>
<a id="__codelineno-5-12" name="__codelineno-5-12" href="#__codelineno-5-12"></a>save_model(4, &#39;secret-number&#39;) #(2)
<a id="__codelineno-5-13" name="__codelineno-5-13" href="#__codelineno-5-13"></a>
<a id="__codelineno-5-14" name="__codelineno-5-14" href="#__codelineno-5-14"></a>@GreatAI.create
<a id="__codelineno-5-15" name="__codelineno-5-15" href="#__codelineno-5-15"></a>@parameter(&#39;positive_number&#39;, validator=lambda n: n &gt; 0)
<a id="__codelineno-5-16" name="__codelineno-5-16" href="#__codelineno-5-16"></a>@use_model(&#39;secret-number&#39;, version=&#39;latest&#39;, model_kwarg_name=&#39;secret&#39;)
<a id="__codelineno-5-17" name="__codelineno-5-17" href="#__codelineno-5-17"></a>def add_number(positive_number: int, secret: int) -&gt; int:
<a id="__codelineno-5-18" name="__codelineno-5-18" href="#__codelineno-5-18"></a> return positive_number + secret
<a id="__codelineno-5-19" name="__codelineno-5-19" href="#__codelineno-5-19"></a>
<a id="__codelineno-5-20" name="__codelineno-5-20" href="#__codelineno-5-20"></a>assert add_number(1).output == 5
</code></pre></div></p>
<ol>
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<h1 id="how-to-use-a-greatai-service">How to use a GreatAI service<a class="headerlink" href="#how-to-use-a-greatai-service" title="Permanent link">#</a></h1>
<p>After <a href="/how-to-guides/cerate-service">creating a GreatAI service</a> by wrapping your prediction function, it's time to do some prediction.</p>
<p>Let's use the following example:</p>
<p>```python "type_safe_greeter.py"
from great_ai import GreatAI</p>
<p>@GreatAI.create
def type_safe_greeter(your_name: str) -&gt; str:
return f'Hi {your_name}'
```</p>
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how-to-guides/using-service/using-service.md Normal file
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@ -0,0 +1,14 @@
# How to use a GreatAI service
After [creating a GreatAI service](/how-to-guides/cerate-service) by wrapping your prediction function, it's time to do some prediction.
Let's use the following example:
```python "type_safe_greeter.py"
from great_ai import GreatAI
@GreatAI.create
def type_safe_greeter(your_name: str) -> str:
return f'Hi {your_name}'
```

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View file

@ -58,7 +58,7 @@
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@ -416,24 +416,46 @@
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<p>Applying AI is becoming increasingly easier but many case studies have shown that these applications are often deployed poorly. This may lead to suboptimal performance and to introducing <a href="https://en.wikipedia.org/wiki/Weapons_of_Math_Destruction" target="_blank">unintended biases</a>. To extend the list of available solutions, <mark>GreatAI helps you easily transform your prototype AI code into production-ready software.</mark></p> <p>Applying AI is becoming increasingly easier but many case studies have shown that these applications are often deployed poorly. This may lead to suboptimal performance and to introducing <a href="https://en.wikipedia.org/wiki/Weapons_of_Math_Destruction" target="_blank">unintended biases</a>. GreatAI helps fixing this by allowing you to <mark>easily transform your prototype AI code into production-ready software</mark>.</p>
<details class="quote"> <details class="quote">
<summary>Case studies</summary> <summary>Case studies</summary>
<p>"There is a need to consider and adapt well established SE practices which have been ignored or had a very narrow focus in ML literature." <p>"There is a need to consider and adapt well established SE practices which have been ignored or had a very narrow focus in ML literature."
@ -636,9 +652,10 @@
<li class="task-list-item"><label class="task-list-control"><input type="checkbox" disabled checked/><span class="task-list-indicator"></span></label> A simple, unified configuration interface</li> <li class="task-list-item"><label class="task-list-control"><input type="checkbox" disabled checked/><span class="task-list-indicator"></span></label> A simple, unified configuration interface</li>
<li class="task-list-item"><label class="task-list-control"><input type="checkbox" disabled checked/><span class="task-list-indicator"></span></label> Fully-typed API for <a href="https://github.com/microsoft/pylance-release" target="_blank">Pylance</a> and <a href="http://mypy-lang.org" target="_blank">MyPy</a> support</li> <li class="task-list-item"><label class="task-list-control"><input type="checkbox" disabled checked/><span class="task-list-indicator"></span></label> Fully-typed API for <a href="https://github.com/microsoft/pylance-release" target="_blank">Pylance</a> and <a href="http://mypy-lang.org" target="_blank">MyPy</a> support</li>
<li class="task-list-item"><label class="task-list-control"><input type="checkbox" disabled checked/><span class="task-list-indicator"></span></label> Auto-reload for development</li> <li class="task-list-item"><label class="task-list-control"><input type="checkbox" disabled checked/><span class="task-list-indicator"></span></label> Auto-reload for development</li>
<li class="task-list-item"><label class="task-list-control"><input type="checkbox" disabled checked/><span class="task-list-indicator"></span></label> Deployable Jupyter Notebooks</li>
<li class="task-list-item"><label class="task-list-control"><input type="checkbox" disabled checked/><span class="task-list-indicator"></span></label> Docker support for deployment</li> <li class="task-list-item"><label class="task-list-control"><input type="checkbox" disabled checked/><span class="task-list-indicator"></span></label> Docker support for deployment</li>
<li class="task-list-item"><label class="task-list-control"><input type="checkbox" disabled checked/><span class="task-list-indicator"></span></label> Dashboard for high-level overview and searching traces</li> <li class="task-list-item"><label class="task-list-control"><input type="checkbox" disabled checked/><span class="task-list-indicator"></span></label> Deployable Jupyter Notebooks</li>
<li class="task-list-item"><label class="task-list-control"><input type="checkbox" disabled checked/><span class="task-list-indicator"></span></label> Dashboard for high-level overview and analysing traces</li>
<li class="task-list-item"><label class="task-list-control"><input type="checkbox" disabled/><span class="task-list-indicator"></span></label> Support for direct file input</li>
</ul> </ul>
<h2 id="hello-world">Hello world<a class="headerlink" href="#hello-world" title="Permanent link">#</a></h2> <h2 id="hello-world">Hello world<a class="headerlink" href="#hello-world" title="Permanent link">#</a></h2>
<div class="highlight"><pre><span></span><code><a id="__codelineno-0-1" name="__codelineno-0-1" href="#__codelineno-0-1"></a>pip install great-ai <div class="highlight"><pre><span></span><code><a id="__codelineno-0-1" name="__codelineno-0-1" href="#__codelineno-0-1"></a>pip install great-ai
@ -659,7 +676,7 @@
</ol> </ol>
</li> </li>
<li> <li>
<p><a href="https://docs.python.org/3/library/typing.html" target="_blank">Typing functions</a> is recommended in general, however, not necessary for GreatAI to work.</p> <p><a href="https://docs.python.org/3/library/typing.html" target="_blank">Typing functions</a> is recommended in general, however, not required for GreatAI to work.</p>
</li> </li>
</ol> </ol>
<details class="note"> <details class="note">
@ -691,7 +708,7 @@
</ul> </ul>
<h2 id="why-greatai">Why GreatAI?<a class="headerlink" href="#why-greatai" title="Permanent link">#</a></h2> <h2 id="why-greatai">Why GreatAI?<a class="headerlink" href="#why-greatai" title="Permanent link">#</a></h2>
<p>There are other, existing solutions aiming to facilitate this phase. <a href="https://aws.amazon.com/sagemaker" target="_blank">Amazon SageMaker</a> and <a href="https://www.seldon.io/solutions/open-source-projects/core" target="_blank">Seldon Core</a> provide the most comprehensive suite of features. If you have the opportunity use those, do that because they're great.</p> <p>There are other, existing solutions aiming to facilitate this phase. <a href="https://aws.amazon.com/sagemaker" target="_blank">Amazon SageMaker</a> and <a href="https://www.seldon.io/solutions/open-source-projects/core" target="_blank">Seldon Core</a> provide the most comprehensive suite of features. If you have the opportunity use those, do that because they're great.</p>
<p>However, research indicates that professionals rarely use them. This may be due to their inherent setup and operating complexity. GreatAI is designed to be as simple to use as possible. Its clear, high-level API and sensible default configuration makes it extremely easy to start using. Despite its relative simplicity over Seldon Core, it still implements many <a href="https://se-ml.github.io" target="_blank">best-practices</a>, and thus, can meaningfully improve your deployment without requiring prohibitively large effort.</p> <p>However, research indicates that professionals rarely use them. This may be due to their inherent setup and operating complexity. GreatAI is designed to be as simple to use as possible. Its clear, high-level API and sensible default configuration makes it extremely easy to start using. Despite its relative simplicity over Seldon Core, it still implements many of the <a href="https://se-ml.github.io" target="_blank">SE4ML best-practices</a>, and thus, can meaningfully improve your deployment without requiring prohibitively large effort.</p>
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@ -706,7 +723,7 @@
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<span class="git-revision-date-localized-plugin git-revision-date-localized-plugin-date">July 9, 2022</span> <span class="git-revision-date-localized-plugin git-revision-date-localized-plugin-date">July 10, 2022</span>
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@ -728,13 +745,13 @@
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@ -3,13 +3,15 @@
<img src="media/logo.png" width=80> <img src="media/logo.png" width=80>
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[![Test](https://github.com/schmelczer/great-ai/actions/workflows/test.yml/badge.svg)](https://github.com/schmelczer/great-ai/actions/workflows/check.yml) [![Sonar line coverage](https://sonar.scoutinscience.com/api/project_badges/measure?project=great-ai&metric=coverage)](https://sonar.scoutinscience.com/dashboard?id=great-ai)
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Applying AI is becoming increasingly easier but many case studies have shown that these applications are often deployed poorly. This may lead to suboptimal performance and to introducing [unintended biases](https://en.wikipedia.org/wiki/Weapons_of_Math_Destruction){ target=_blank }. To extend the list of available solutions, ==GreatAI helps you easily transform your prototype AI code into production-ready software.== Applying AI is becoming increasingly easier but many case studies have shown that these applications are often deployed poorly. This may lead to suboptimal performance and to introducing [unintended biases](https://en.wikipedia.org/wiki/Weapons_of_Math_Destruction){ target=_blank }. GreatAI helps fixing this by allowing you to ==easily transform your prototype AI code into production-ready software==.
??? quote "Case studies" ??? quote "Case studies"
"There is a need to consider and adapt well established SE practices which have been ignored or had a very narrow focus in ML literature." "There is a need to consider and adapt well established SE practices which have been ignored or had a very narrow focus in ML literature."
@ -36,9 +38,10 @@ Applying AI is becoming increasingly easier but many case studies have shown tha
- [x] A simple, unified configuration interface - [x] A simple, unified configuration interface
- [x] Fully-typed API for [Pylance](https://github.com/microsoft/pylance-release){ target=_blank } and [MyPy](http://mypy-lang.org){ target=_blank } support - [x] Fully-typed API for [Pylance](https://github.com/microsoft/pylance-release){ target=_blank } and [MyPy](http://mypy-lang.org){ target=_blank } support
- [x] Auto-reload for development - [x] Auto-reload for development
- [x] Deployable Jupyter Notebooks
- [x] Docker support for deployment - [x] Docker support for deployment
- [x] Dashboard for high-level overview and searching traces - [x] Deployable Jupyter Notebooks
- [x] Dashboard for high-level overview and analysing traces
- [ ] Support for direct file input
## Hello world ## Hello world
@ -59,7 +62,7 @@ def hello_world(name: str) -> str: #(2)
2. it gets a `process_batch` method for supporting parallel execution, 2. it gets a `process_batch` method for supporting parallel execution,
3. and it can be deployed using the `great-ai` command-line tool. 3. and it can be deployed using the `great-ai` command-line tool.
2. [Typing functions](https://docs.python.org/3/library/typing.html){ target=_blank } is recommended in general, however, not necessary for GreatAI to work. 2. [Typing functions](https://docs.python.org/3/library/typing.html){ target=_blank } is recommended in general, however, not required for GreatAI to work.
??? note ??? note
In practice, `hello_world` could be an inference function of some AI/ML application. But it could also just wrap a black-box solution of some SaaS. Either ways, it is imperative to have continuos oversight of the services you provide and data you process. In practice, `hello_world` could be an inference function of some AI/ML application. But it could also just wrap a black-box solution of some SaaS. Either ways, it is imperative to have continuos oversight of the services you provide and data you process.
@ -94,7 +97,7 @@ GreatAI fits between the prototype and deployment phase of your (or your organis
There are other, existing solutions aiming to facilitate this phase. [Amazon SageMaker](https://aws.amazon.com/sagemaker){ target=_blank } and [Seldon Core](https://www.seldon.io/solutions/open-source-projects/core){ target=_blank } provide the most comprehensive suite of features. If you have the opportunity use those, do that because they're great. There are other, existing solutions aiming to facilitate this phase. [Amazon SageMaker](https://aws.amazon.com/sagemaker){ target=_blank } and [Seldon Core](https://www.seldon.io/solutions/open-source-projects/core){ target=_blank } provide the most comprehensive suite of features. If you have the opportunity use those, do that because they're great.
However, research indicates that professionals rarely use them. This may be due to their inherent setup and operating complexity. GreatAI is designed to be as simple to use as possible. Its clear, high-level API and sensible default configuration makes it extremely easy to start using. Despite its relative simplicity over Seldon Core, it still implements many [best-practices](https://se-ml.github.io){ target=_blank }, and thus, can meaningfully improve your deployment without requiring prohibitively large effort. However, research indicates that professionals rarely use them. This may be due to their inherent setup and operating complexity. GreatAI is designed to be as simple to use as possible. Its clear, high-level API and sensible default configuration makes it extremely easy to start using. Despite its relative simplicity over Seldon Core, it still implements many of the [SE4ML best-practices](https://se-ml.github.io){ target=_blank }, and thus, can meaningfully improve your deployment without requiring prohibitively large effort.
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@ -277,7 +277,7 @@
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Train and deploy a SOTA model Tutorial overview
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@ -351,24 +351,46 @@
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@ -377,63 +399,6 @@
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Train a domain classifier on the semantic scholar dataset
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Train a domain classifier on the semantic scholar dataset
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Train a domain classifier on the semantic scholar dataset
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Reference Reference
@ -441,6 +406,55 @@
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Utilities
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# Reference
```python
from great_ai import *
```
## Core
::: great_ai.GreatAI
options:
filters: ['!__init__']
show_root_heading: true
::: great_ai.configure
options:
show_root_heading: true
::: great_ai.save_model
options:
show_root_heading: true
::: great_ai.use_model
options:
show_root_heading: true
::: great_ai.parameter
options:
show_root_heading: true
::: great_ai.log_metric
options:
show_root_heading: true
## Remote calls
::: great_ai.call_remote_great_ai
options:
show_root_heading: true
::: great_ai.call_remote_great_ai_async
options:
show_root_heading: true
## Ground-truth
::: great_ai.add_ground_truth
options:
show_root_heading: true
::: great_ai.query_ground_truth
options:
show_root_heading: true
::: great_ai.delete_ground_truth
options:
show_root_heading: true

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# LargeFile
```python
from great_ai.large_file import *
```

1
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::: great_ai.utilities.unique

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# Utilities
```python
from great_ai.utilities import *
```
## NLP tools
Well-tested tools that can be used in production with confidence. The toolbox of feature-extraction functions is expected to grow to cover other domains as well.
::: great_ai.utilities.clean
::: great_ai.utilities.get_sentences
::: great_ai.utilities.language.predict_language
::: great_ai.utilities.language.is_english
::: great_ai.utilities.language.english_name_of_language
::: great_ai.utilities.evaluate_ranking.evaluate_ranking
## Parallel processing
Multiprocessing and multithreading-based parallelism with support for `async` functions. Its main purpose is to implement [great_ai.GreatAI.process_batch][], however, the parallel processing functions are also convenient for covering other types of mapping needs with a friendlier API than [joblib](https://joblib.readthedocs.io/en/latest/parallel.html){ target=_blank } or [multiprocess](https://pypi.org/project/multiprocess/){ target=_blank }.
::: great_ai.utilities.parallel_map.simple_parallel_map
::: great_ai.utilities.parallel_map.parallel_map
::: great_ai.utilities.parallel_map.threaded_parallel_map
## Composable parallel processing
Because both [threaded_parallel_map][great_ai.utilities.parallel_map.threaded_parallel_map.threaded_parallel_map] and [parallel_map][great_ai.utilities.parallel_map.parallel_map.parallel_map] have a streaming interface, it is easy to compose them and end up with, for example, a process for each CPU core with its own thread-pool or event-loop. Longer pipelines are also easy to imagine. The chunking methods help in these compositions.
For more info, check-out [the scraping guide](/how-to-guides/scraping).
::: great_ai.utilities.chunk
::: great_ai.utilities.unchunk
## Operations
::: great_ai.utilities.config_file.config_file
::: great_ai.utilities.logger.get_logger

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<h1 id="view-models">View models<a class="headerlink" href="#view-models" title="Permanent link">#</a></h1>
<div class="doc doc-object doc-class">
<div class="doc doc-contents first">
<p class="doc doc-class-bases">
Bases: <code><span title="typing.Generic">Generic</span>[<span title="great_ai.views.trace.T">T</span>]</code>, <code><span title="great_ai.helper.HashableBaseModel">HashableBaseModel</span></code></p>
<details class="quote">
<summary>Source code in <code>great_ai/views/trace.py</code></summary>
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<span class="normal"><a href="#__codelineno-0-62">62</a></span>
<span class="normal"><a href="#__codelineno-0-63">63</a></span>
<span class="normal"><a href="#__codelineno-0-64">64</a></span>
<span class="normal"><a href="#__codelineno-0-65">65</a></span>
<span class="normal"><a href="#__codelineno-0-66">66</a></span>
<span class="normal"><a href="#__codelineno-0-67">67</a></span>
<span class="normal"><a href="#__codelineno-0-68">68</a></span>
<span class="normal"><a href="#__codelineno-0-69">69</a></span>
<span class="normal"><a href="#__codelineno-0-70">70</a></span>
<span class="normal"><a href="#__codelineno-0-71">71</a></span>
<span class="normal"><a href="#__codelineno-0-72">72</a></span>
<span class="normal"><a href="#__codelineno-0-73">73</a></span>
<span class="normal"><a href="#__codelineno-0-74">74</a></span>
<span class="normal"><a href="#__codelineno-0-75">75</a></span>
<span class="normal"><a href="#__codelineno-0-76">76</a></span>
<span class="normal"><a href="#__codelineno-0-77">77</a></span>
<span class="normal"><a href="#__codelineno-0-78">78</a></span>
<span class="normal"><a href="#__codelineno-0-79">79</a></span>
<span class="normal"><a href="#__codelineno-0-80">80</a></span>
<span class="normal"><a href="#__codelineno-0-81">81</a></span>
<span class="normal"><a href="#__codelineno-0-82">82</a></span>
<span class="normal"><a href="#__codelineno-0-83">83</a></span>
<span class="normal"><a href="#__codelineno-0-84">84</a></span>
<span class="normal"><a href="#__codelineno-0-85">85</a></span></pre></div></td><td class="code"><div><pre><span></span><code><a id="__codelineno-0-12" name="__codelineno-0-12"></a><span class="k">class</span> <span class="nc">Trace</span><span class="p">(</span><span class="n">Generic</span><span class="p">[</span><span class="n">T</span><span class="p">],</span> <span class="n">HashableBaseModel</span><span class="p">):</span>
<a id="__codelineno-0-13" name="__codelineno-0-13"></a> <span class="n">trace_id</span><span class="p">:</span> <span class="nb">str</span>
<a id="__codelineno-0-14" name="__codelineno-0-14"></a> <span class="n">created</span><span class="p">:</span> <span class="nb">str</span>
<a id="__codelineno-0-15" name="__codelineno-0-15"></a> <span class="n">original_execution_time_ms</span><span class="p">:</span> <span class="nb">float</span>
<a id="__codelineno-0-16" name="__codelineno-0-16"></a> <span class="n">logged_values</span><span class="p">:</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Any</span><span class="p">]</span>
<a id="__codelineno-0-17" name="__codelineno-0-17"></a> <span class="n">models</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">Model</span><span class="p">]</span>
<a id="__codelineno-0-18" name="__codelineno-0-18"></a> <span class="n">exception</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span>
<a id="__codelineno-0-19" name="__codelineno-0-19"></a> <span class="n">output</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">T</span><span class="p">]</span>
<a id="__codelineno-0-20" name="__codelineno-0-20"></a> <span class="n">feedback</span><span class="p">:</span> <span class="n">Any</span> <span class="o">=</span> <span class="kc">None</span>
<a id="__codelineno-0-21" name="__codelineno-0-21"></a> <span class="n">tags</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span>
<a id="__codelineno-0-22" name="__codelineno-0-22"></a>
<a id="__codelineno-0-23" name="__codelineno-0-23"></a> <span class="k">class</span> <span class="nc">Config</span><span class="p">:</span>
<a id="__codelineno-0-24" name="__codelineno-0-24"></a> <span class="n">extra</span> <span class="o">=</span> <span class="n">Extra</span><span class="o">.</span><span class="n">ignore</span>
<a id="__codelineno-0-25" name="__codelineno-0-25"></a>
<a id="__codelineno-0-26" name="__codelineno-0-26"></a> <span class="nd">@property</span>
<a id="__codelineno-0-27" name="__codelineno-0-27"></a> <span class="k">def</span> <span class="nf">input</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Any</span><span class="p">:</span>
<a id="__codelineno-0-28" name="__codelineno-0-28"></a> <span class="k">return</span> <span class="p">(</span>
<a id="__codelineno-0-29" name="__codelineno-0-29"></a> <span class="bp">self</span><span class="o">.</span><span class="n">logged_values</span><span class="p">[</span><span class="s2">&quot;input&quot;</span><span class="p">]</span>
<a id="__codelineno-0-30" name="__codelineno-0-30"></a> <span class="k">if</span> <span class="nb">list</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">logged_values</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span> <span class="o">==</span> <span class="p">[</span><span class="s2">&quot;input&quot;</span><span class="p">]</span>
<a id="__codelineno-0-31" name="__codelineno-0-31"></a> <span class="k">else</span> <span class="bp">self</span><span class="o">.</span><span class="n">logged_values</span>
<a id="__codelineno-0-32" name="__codelineno-0-32"></a> <span class="p">)</span>
<a id="__codelineno-0-33" name="__codelineno-0-33"></a>
<a id="__codelineno-0-34" name="__codelineno-0-34"></a> <span class="nd">@property</span>
<a id="__codelineno-0-35" name="__codelineno-0-35"></a> <span class="k">def</span> <span class="nf">models_flat</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
<a id="__codelineno-0-36" name="__codelineno-0-36"></a> <span class="k">return</span> <span class="s2">&quot;, &quot;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">m</span><span class="o">.</span><span class="n">key</span><span class="si">}</span><span class="s2">:</span><span class="si">{</span><span class="n">m</span><span class="o">.</span><span class="n">version</span><span class="si">}</span><span class="s2">&quot;</span> <span class="k">for</span> <span class="n">m</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">models</span><span class="p">)</span>
<a id="__codelineno-0-37" name="__codelineno-0-37"></a>
<a id="__codelineno-0-38" name="__codelineno-0-38"></a> <span class="nd">@property</span>
<a id="__codelineno-0-39" name="__codelineno-0-39"></a> <span class="k">def</span> <span class="nf">output_flat</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
<a id="__codelineno-0-40" name="__codelineno-0-40"></a> <span class="k">return</span> <span class="n">pformat</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">output</span><span class="p">,</span> <span class="n">indent</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">compact</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<a id="__codelineno-0-41" name="__codelineno-0-41"></a>
<a id="__codelineno-0-42" name="__codelineno-0-42"></a> <span class="nd">@property</span>
<a id="__codelineno-0-43" name="__codelineno-0-43"></a> <span class="k">def</span> <span class="nf">exception_flat</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
<a id="__codelineno-0-44" name="__codelineno-0-44"></a> <span class="k">return</span> <span class="p">(</span>
<a id="__codelineno-0-45" name="__codelineno-0-45"></a> <span class="s2">&quot;null&quot;</span>
<a id="__codelineno-0-46" name="__codelineno-0-46"></a> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">exception</span> <span class="ow">is</span> <span class="kc">None</span>
<a id="__codelineno-0-47" name="__codelineno-0-47"></a> <span class="k">else</span> <span class="n">pformat</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">exception</span><span class="p">,</span> <span class="n">indent</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">compact</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<a id="__codelineno-0-48" name="__codelineno-0-48"></a> <span class="p">)</span>
<a id="__codelineno-0-49" name="__codelineno-0-49"></a>
<a id="__codelineno-0-50" name="__codelineno-0-50"></a> <span class="nd">@property</span>
<a id="__codelineno-0-51" name="__codelineno-0-51"></a> <span class="k">def</span> <span class="nf">feedback_flat</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
<a id="__codelineno-0-52" name="__codelineno-0-52"></a> <span class="k">return</span> <span class="p">(</span>
<a id="__codelineno-0-53" name="__codelineno-0-53"></a> <span class="s2">&quot;null&quot;</span>
<a id="__codelineno-0-54" name="__codelineno-0-54"></a> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">feedback</span> <span class="ow">is</span> <span class="kc">None</span>
<a id="__codelineno-0-55" name="__codelineno-0-55"></a> <span class="k">else</span> <span class="n">pformat</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">feedback</span><span class="p">,</span> <span class="n">indent</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">compact</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<a id="__codelineno-0-56" name="__codelineno-0-56"></a> <span class="p">)</span>
<a id="__codelineno-0-57" name="__codelineno-0-57"></a>
<a id="__codelineno-0-58" name="__codelineno-0-58"></a> <span class="nd">@property</span>
<a id="__codelineno-0-59" name="__codelineno-0-59"></a> <span class="k">def</span> <span class="nf">tags_flat</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
<a id="__codelineno-0-60" name="__codelineno-0-60"></a> <span class="k">return</span> <span class="s2">&quot;,</span><span class="se">\n</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">tags</span><span class="p">)</span>
<a id="__codelineno-0-61" name="__codelineno-0-61"></a>
<a id="__codelineno-0-62" name="__codelineno-0-62"></a> <span class="k">def</span> <span class="nf">to_flat_dict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">include_original</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Any</span><span class="p">]:</span>
<a id="__codelineno-0-63" name="__codelineno-0-63"></a> <span class="k">return</span> <span class="p">{</span>
<a id="__codelineno-0-64" name="__codelineno-0-64"></a> <span class="o">**</span><span class="p">(</span>
<a id="__codelineno-0-65" name="__codelineno-0-65"></a> <span class="bp">self</span><span class="o">.</span><span class="n">dict</span><span class="p">()</span>
<a id="__codelineno-0-66" name="__codelineno-0-66"></a> <span class="k">if</span> <span class="n">include_original</span>
<a id="__codelineno-0-67" name="__codelineno-0-67"></a> <span class="k">else</span> <span class="p">{</span>
<a id="__codelineno-0-68" name="__codelineno-0-68"></a> <span class="s2">&quot;trace_id&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">trace_id</span><span class="p">,</span>
<a id="__codelineno-0-69" name="__codelineno-0-69"></a> <span class="s2">&quot;created&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">created</span><span class="p">,</span>
<a id="__codelineno-0-70" name="__codelineno-0-70"></a> <span class="s2">&quot;original_execution_time_ms&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">original_execution_time_ms</span><span class="p">,</span>
<a id="__codelineno-0-71" name="__codelineno-0-71"></a> <span class="p">}</span>
<a id="__codelineno-0-72" name="__codelineno-0-72"></a> <span class="p">),</span>
<a id="__codelineno-0-73" name="__codelineno-0-73"></a> <span class="o">**</span><span class="p">{</span>
<a id="__codelineno-0-74" name="__codelineno-0-74"></a> <span class="n">k</span><span class="p">:</span> <span class="n">pformat</span><span class="p">(</span><span class="n">v</span><span class="p">,</span> <span class="n">indent</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">compact</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<a id="__codelineno-0-75" name="__codelineno-0-75"></a> <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">logged_values</span><span class="o">.</span><span class="n">items</span><span class="p">()</span>
<a id="__codelineno-0-76" name="__codelineno-0-76"></a> <span class="p">},</span>
<a id="__codelineno-0-77" name="__codelineno-0-77"></a> <span class="s2">&quot;models_flat&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">models_flat</span><span class="p">,</span>
<a id="__codelineno-0-78" name="__codelineno-0-78"></a> <span class="s2">&quot;exception_flat&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">exception_flat</span><span class="p">,</span>
<a id="__codelineno-0-79" name="__codelineno-0-79"></a> <span class="s2">&quot;output_flat&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">output_flat</span><span class="p">,</span>
<a id="__codelineno-0-80" name="__codelineno-0-80"></a> <span class="s2">&quot;feedback_flat&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">feedback_flat</span><span class="p">,</span>
<a id="__codelineno-0-81" name="__codelineno-0-81"></a> <span class="s2">&quot;tags_flat&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">tags_flat</span><span class="p">,</span>
<a id="__codelineno-0-82" name="__codelineno-0-82"></a> <span class="p">}</span>
<a id="__codelineno-0-83" name="__codelineno-0-83"></a>
<a id="__codelineno-0-84" name="__codelineno-0-84"></a> <span class="k">def</span> <span class="fm">__repr__</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
<a id="__codelineno-0-85" name="__codelineno-0-85"></a> <span class="k">return</span> <span class="sa">f</span><span class="s2">&quot;Trace[</span><span class="si">{</span><span class="nb">type</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">output</span><span class="p">)</span><span class="o">.</span><span class="vm">__name__</span><span class="si">}</span><span class="s2">](</span><span class="si">{</span><span class="n">pformat</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">dict</span><span class="p">(),</span> <span class="n">indent</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">compact</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span><span class="si">}</span><span class="s2">)&quot;</span>
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# View models
::: great_ai.views.trace.Trace

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search/search_index.json
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simple-mag/train/index.html
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@ -58,7 +58,7 @@
<body dir="ltr" data-md-color-scheme="default" data-md-color-primary="" data-md-color-accent=""> <body dir="ltr" data-md-color-scheme="default" data-md-color-primary="light-blue" data-md-color-accent="">
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@ -277,7 +277,7 @@
<li class="md-nav__item"> <li class="md-nav__item">
<a href="../../tutorial/" class="md-nav__link"> <a href="../../tutorial/" class="md-nav__link">
Train and deploy a SOTA model Tutorial overview
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@ -351,24 +351,46 @@
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<a href="../../how-to-guides/create-service/" class="md-nav__link">
How to instantiate a GreatAI service
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Simple AI lifecycle with classification Reference
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Simple AI lifecycle with classification Reference
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@ -377,63 +399,6 @@
<li class="md-nav__item">
<a href="../../simple/data/" class="md-nav__link">
Train a domain classifier on the semantic scholar dataset
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<a href="../../simple/train/" class="md-nav__link">
Train a domain classifier on the semantic scholar dataset
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Train a domain classifier on the semantic scholar dataset
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Reference Reference
@ -441,6 +406,55 @@
</li> </li>
<li class="md-nav__item">
<a href="../../reference/utilities/" class="md-nav__link">
Utilities
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<a href="../../reference/large-file/" class="md-nav__link">
LargeFile
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<a href="../../reference/views/" class="md-nav__link">
View models
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189
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@ -279,7 +279,7 @@
<li class="md-nav__item"> <li class="md-nav__item">
<a href="../" class="md-nav__link"> <a href="../" class="md-nav__link">
Train and deploy a SOTA model Tutorial overview
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@ -414,24 +414,46 @@
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@ -440,63 +462,6 @@
<li class="md-nav__item">
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Train a domain classifier on the semantic scholar dataset
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Train a domain classifier on the semantic scholar dataset
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@ -504,6 +469,55 @@
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@ -1977,13 +1991,13 @@ def predict_domain(sentence, model):
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@ -1997,10 +2011,37 @@ def predict_domain(sentence, model):
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<title>Train and deploy a SOTA model - GreatAI documentation</title> <title>Tutorial overview - GreatAI documentation</title>
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Train a domain classifier on the semantic scholar dataset
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Train a domain classifier on the semantic scholar dataset
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Train a domain classifier on the semantic scholar dataset
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Utilities
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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> <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 GreatAI 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 [great_ai.utilities][] can integrate into your Data Science workflow.</li>
<li>You will use [great_ai.large_file][] 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> <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>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> <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>
<h2 id="objectives">Objectives<a class="headerlink" href="#objectives" title="Permanent link">#</a></h2>
<ol>
<li>You will see how the <code>great_ai.utilities</code> can integrate into your Data Science workflow.</li>
<li>You will use <code>great_ai.large_file</code> to version and store your trained model</li>
<li>You will use <code>GreatAI</code> to prepare your model for a robust and responsible deployment</li>
</ol>
<div class="admonition success"> <div class="admonition success">
<p class="admonition-title">Success</p> <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> <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>
</div> </div>
<div style="display: flex; justify-content: space-evenly;"> <div style="display: flex; justify-content: space-evenly;">
<p><a class="md-button md-button--primary" href="train/"><span class="twemoji"><svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.1.1 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc.--><path d="M160 80c0-26.51 21.5-48 48-48h32c26.5 0 48 21.49 48 48v352c0 26.5-21.5 48-48 48h-32c-26.5 0-48-21.5-48-48V80zM0 272c0-26.5 21.49-48 48-48h32c26.5 0 48 21.5 48 48v160c0 26.5-21.5 48-48 48H48c-26.51 0-48-21.5-48-48V272zM400 96c26.5 0 48 21.5 48 48v288c0 26.5-21.5 48-48 48h-32c-26.5 0-48-21.5-48-48V144c0-26.5 21.5-48 48-48h32z"/></svg></span> Train it</a></p> <p><a class="md-button md-button--primary" href="train/"><span class="twemoji"><svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.1.1 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc.--><path d="M160 80c0-26.51 21.5-48 48-48h32c26.5 0 48 21.49 48 48v352c0 26.5-21.5 48-48 48h-32c-26.5 0-48-21.5-48-48V80zM0 272c0-26.5 21.49-48 48-48h32c26.5 0 48 21.5 48 48v160c0 26.5-21.5 48-48 48H48c-26.51 0-48-21.5-48-48V272zM400 96c26.5 0 48 21.5 48 48v288c0 26.5-21.5 48-48 48h-32c-26.5 0-48-21.5-48-48V144c0-26.5 21.5-48 48-48h32z"/></svg></span> Train it</a></p>
<p><a class="md-button md-button--secondary" href="deploy/"><span class="twemoji"><svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M6 20a6 6 0 0 1-6-6c0-3.09 2.34-5.64 5.35-5.96A7.496 7.496 0 0 1 12 4a7.5 7.5 0 0 1 7.35 6A5.02 5.02 0 0 1 24 15a5 5 0 0 1-5 5H6M9.09 8.4 4.5 13l4.59 4.6 1.41-1.42L7.32 13l3.18-3.18L9.09 8.4m5.82 0L13.5 9.82 16.68 13l-3.18 3.18 1.41 1.42L19.5 13l-4.59-4.6Z"/></svg></span> Deploy it</a></p> <p><a class="md-button md-button--primary" href="deploy/"><span class="twemoji"><svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M6 20a6 6 0 0 1-6-6c0-3.09 2.34-5.64 5.35-5.96A7.496 7.496 0 0 1 12 4a7.5 7.5 0 0 1 7.35 6A5.02 5.02 0 0 1 24 15a5 5 0 0 1-5 5H6M9.09 8.4 4.5 13l4.59 4.6 1.41-1.42L7.32 13l3.18-3.18L9.09 8.4m5.82 0L13.5 9.82 16.68 13l-3.18 3.18 1.41 1.42L19.5 13l-4.59-4.6Z"/></svg></span> Deploy it</a></p>
</div> </div>
<h2 id="summary">Summary<a class="headerlink" href="#summary" title="Permanent link">#</a></h2> <h2 id="summary">Summary<a class="headerlink" href="#summary" title="Permanent link">#</a></h2>
<h3 id="the-training-notebook">The <a href="train/">training notebook</a><a class="headerlink" href="#the-training-notebook" title="Permanent link">#</a></h3> <h3 id="the-training-notebook">The <a href="train/">training notebook</a><a class="headerlink" href="#the-training-notebook" title="Permanent link">#</a></h3>
<p>We load and preprocess the dataset while relying on <code>great_ai.utilities.clean</code> for the heavy-lifting. Additionally, the preprocessing is parallelised using <code>great_ai.utilities.simple_parallel_map</code>.</p> <p>We load and preprocess the dataset while relying on [great_ai.utilities.clean][] for the heavy-lifting. Additionally, the preprocessing is parallelised using <a class="autorefs autorefs-internal" href="../reference/utilities/#great_ai.utilities.parallel_map.simple_parallel_map.simple_parallel_map">great_ai.utilities.simple_parallel_map</a></p>
<p>After training and evaluating a model, it is exported using <code>great_ai.save_model</code>.</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"> <details class="tip">
<summary>Using the cloud</summary> <summary>Remote storage</summary>
<p>To store your model remotely, you need to set your credentials before calling <code>save_model</code>.</p> <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/">AWS S3</a>: <p>For example, to use <a href="https://aws.amazon.com/s3/">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> <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>
@ -663,7 +678,7 @@
</code></pre></div></p> </code></pre></div></p>
</details> </details>
<h3 id="the-deployment-notebook">The <a href="deploy/">deployment notebook</a><a class="headerlink" href="#the-deployment-notebook" title="Permanent link">#</a></h3> <h3 id="the-deployment-notebook">The <a href="deploy/">deployment notebook</a><a class="headerlink" href="#the-deployment-notebook" title="Permanent link">#</a></h3>
<p>We create an inference function that can be hardened by wrapping it in a <code>great_ai.GreatAI</code> instance.</p> <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> <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-1-2" 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-1-3" name="__codelineno-1-3" href="#__codelineno-1-3"></a>
@ -674,7 +689,7 @@
<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> <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> </code></pre></div>
<ol> <ol>
<li><code>@use_model</code> loads and injects your model into the <code>predict_domain</code> function's <code>model</code> argument. <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 it is always given to the function.</li> You can freely reference it knowing that it is always given to the function.</li>
</ol> </ol>
<p>Finally, we deploy the model, inference function, and the GreatAI wrapping all of these. For that we either use: <code>great-ai deploy.ipynb</code> or build a Docker image.</p> <p>Finally, we deploy the model, inference function, and the GreatAI wrapping all of these. For that we either use: <code>great-ai deploy.ipynb</code> or build a Docker image.</p>
@ -684,7 +699,7 @@
<small> <small>
Last update: Last update:
<span class="git-revision-date-localized-plugin git-revision-date-localized-plugin-date">July 9, 2022</span> <span class="git-revision-date-localized-plugin git-revision-date-localized-plugin-date">July 10, 2022</span>
</small> </small>
@ -741,10 +756,37 @@
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25
tutorial/index.md
View file

@ -1,16 +1,19 @@
# Train and deploy a SOTA model # Train and deploy a SOTA model
Let's see GreatAI in action by going over the life-cycle of a simple service.
## Objectives
1. You will see how the [great_ai.utilities][] can integrate into your Data Science workflow.
2. You will use [great_ai.large_file][] to version and store your trained model.
3. You will use [GreatAI][great_ai.GreatAI] to prepare your model for a robust and responsible deployment.
## Overview ## Overview
You are going to train a field of study (domain) classifier for scientific sentences. The exact task was proposed by the [SciBERT paper](https://arxiv.org/abs/1903.10676) in which SciBERT [achieved an F1-score of 0.6571](https://paperswithcode.com/sota/sentence-classification-on-paper-field). We are going to outperform it using a trivial text classification model: a [Linear SVM](https://scikit-learn.org/stable/modules/generated/sklearn.svm.LinearSVC.html). You are going to train a field of study (domain) classifier for scientific sentences. The exact task was proposed by the [SciBERT paper](https://arxiv.org/abs/1903.10676) in which SciBERT [achieved an F1-score of 0.6571](https://paperswithcode.com/sota/sentence-classification-on-paper-field). We are going to outperform it using a trivial text classification model: a [Linear SVM](https://scikit-learn.org/stable/modules/generated/sklearn.svm.LinearSVC.html).
We use the same synthetic dataset derived from the [Microsoft Academic Graph](https://www.microsoft.com/en-us/research/project/microsoft-academic-graph/). The dataset is [available here](https://github.com/allenai/scibert/tree/master/data/text_classification/mag). We use the same synthetic dataset derived from the [Microsoft Academic Graph](https://www.microsoft.com/en-us/research/project/microsoft-academic-graph/). The dataset is [available here](https://github.com/allenai/scibert/tree/master/data/text_classification/mag).
## Objectives
1. You will see how the `great_ai.utilities` can integrate into your Data Science workflow.
2. You will use `great_ai.large_file` to version and store your trained model
3. You will use `GreatAI` to prepare your model for a robust and responsible deployment
!!! success !!! success
You are ready to start the tutorial. Feel free to come back to the [summary](#summary) section once you're finished. You are ready to start the tutorial. Feel free to come back to the [summary](#summary) section once you're finished.
@ -18,7 +21,7 @@ We use the same synthetic dataset derived from the [Microsoft Academic Graph](ht
<div style="display: flex; justify-content: space-evenly;" markdown> <div style="display: flex; justify-content: space-evenly;" markdown>
[:fontawesome-solid-chart-simple: Train it](train.ipynb){ .md-button .md-button--primary } [:fontawesome-solid-chart-simple: Train it](train.ipynb){ .md-button .md-button--primary }
[:material-cloud-tags: Deploy it](deploy.ipynb){ .md-button .md-button--secondary } [:material-cloud-tags: Deploy it](deploy.ipynb){ .md-button .md-button--primary }
</div> </div>
@ -26,11 +29,11 @@ We use the same synthetic dataset derived from the [Microsoft Academic Graph](ht
### The [training notebook](train.ipynb) ### The [training notebook](train.ipynb)
We load and preprocess the dataset while relying on `great_ai.utilities.clean` for the heavy-lifting. Additionally, the preprocessing is parallelised using `great_ai.utilities.simple_parallel_map`. We load and preprocess the dataset while relying on [great_ai.utilities.clean][] for the heavy-lifting. Additionally, the preprocessing is parallelised using [great_ai.utilities.simple_parallel_map][]
After training and evaluating a model, it is exported using `great_ai.save_model`. After training and evaluating a model, it is exported using [great_ai.save_model][].
??? tip "Using the cloud" ??? tip "Remote storage"
To store your model remotely, you need to set your credentials before calling `save_model`. To store your model remotely, you need to set your credentials before calling `save_model`.
For example, to use [AWS S3](https://aws.amazon.com/s3/): For example, to use [AWS S3](https://aws.amazon.com/s3/):
@ -51,7 +54,7 @@ After training and evaluating a model, it is exported using `great_ai.save_model
### The [deployment notebook](deploy.ipynb) ### The [deployment notebook](deploy.ipynb)
We create an inference function that can be hardened by wrapping it in a `great_ai.GreatAI` instance. We create an inference function that can be hardened by wrapping it in a [GreatAI][great_ai.GreatAI] instance.
```python ```python
from great_ai import GreatAI, use_model from great_ai import GreatAI, use_model
@ -64,7 +67,7 @@ def predict_domain(sentence, model):
return str(model.predict(inputs)[0]) return str(model.predict(inputs)[0])
``` ```
1. `@use_model` loads and injects your model into the `predict_domain` function's `model` argument. 1. [@use_model][great_ai.use_model] loads and injects your model into the `predict_domain` function's `model` argument.
You can freely reference it knowing that it is always given to the function. You can freely reference it knowing that it is always given to the function.
Finally, we deploy the model, inference function, and the GreatAI wrapping all of these. For that we either use: `great-ai deploy.ipynb` or build a Docker image. Finally, we deploy the model, inference function, and the GreatAI wrapping all of these. For that we either use: `great-ai deploy.ipynb` or build a Docker image.

189
tutorial/train/index.html
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@ -58,7 +58,7 @@
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@ -279,7 +279,7 @@
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Train and deploy a SOTA model Tutorial overview
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@ -394,24 +394,46 @@
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