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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>
<li>Refer to <a href="/how-to-guides/store-models">storing models</a> for specifying where to store your models. </li>
</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 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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@ -58,7 +58,7 @@
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@ -342,7 +342,7 @@
<li class="md-nav__item">
<a href="tutorial/" class="md-nav__link">
Train and deploy a SOTA model
Tutorial overview
</a>
</li>
@ -416,24 +416,46 @@
<li class="md-nav__item">
<a href="how-to-guides/create-service/" class="md-nav__link">
How to instantiate a GreatAI service
</a>
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Simple AI lifecycle with classification
<label class="md-nav__link" for="__nav_4">
Reference
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Simple AI lifecycle with classification
Reference
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@ -442,63 +464,6 @@
<li class="md-nav__item">
<a href="simple/data/" class="md-nav__link">
Train a domain classifier on the semantic scholar dataset
</a>
</li>
<li class="md-nav__item">
<a href="simple/train/" class="md-nav__link">
Train a domain classifier on the semantic scholar dataset
</a>
</li>
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<a href="simple/deploy/" class="md-nav__link">
Train a domain classifier on the semantic scholar dataset
</a>
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</ul>
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<a href="reference/" class="md-nav__link">
Reference
@ -513,6 +478,55 @@
<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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View models
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@ -607,12 +621,14 @@
<img src="media/logo.png" width=80>
</div>
<p><a href="https://github.com/schmelczer/great-ai/actions/workflows/check.yml"><img alt="Test" src="https://github.com/schmelczer/great-ai/actions/workflows/test.yml/badge.svg" /></a>
<a href="https://sonar.schmelczer.com/dashboard?id=great-ai"><img alt="Quality Gate Status" src="https://sonar.scoutinscience.com/api/project_badges/measure?project=great-ai&amp;metric=alert_status" /></a>
<p><a href="https://sonar.scoutinscience.com/dashboard?id=great-ai"><img alt="Sonar line coverage" src="https://sonar.scoutinscience.com/api/project_badges/measure?project=great-ai&amp;metric=coverage" /></a>
<a href="https://sonar.scoutinscience.com/dashboard?id=great-ai"><img alt="Sonar technical debt" src="https://sonar.scoutinscience.com/api/project_badges/measure?project=great-ai&amp;metric=sqale_index" /></a>
<a href="https://sonar.scoutinscience.com/dashboard?id=great-ai"><img alt="Sonar LoC" src="https://sonar.scoutinscience.com/api/project_badges/measure?project=great-ai&amp;metric=ncloc" /></a>
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<a href="https://pepy.tech/project/great-ai"><img alt="Downloads" src="https://pepy.tech/badge/great-ai/month" /></a></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>. 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">
<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."
@ -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> 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>
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<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>
<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
@ -659,7 +676,7 @@
</ol>
</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>
</ol>
<details class="note">
@ -691,7 +708,7 @@
</ul>
<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>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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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"
"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] 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] Deployable Jupyter Notebooks
- [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
@ -59,7 +62,7 @@ def hello_world(name: str) -> str: #(2)
2. it gets a `process_batch` method for supporting parallel execution,
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
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.
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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@ -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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@ -448,6 +413,55 @@
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reference/index.md Normal file
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@ -0,0 +1,56 @@
# 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

View file

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<h1 id="largefile">LargeFile<a class="headerlink" href="#largefile" title="Permanent link">#</a></h1>
<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="o">*</span>
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# LargeFile
```python
from great_ai.large_file import *
```

View file

@ -1 +0,0 @@
::: great_ai.utilities.unique

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