118 lines
4.5 KiB
Markdown
118 lines
4.5 KiB
Markdown
# How to instantiate a GreatAI service
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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.
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Let's say that you have the following greeter function:
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```python title="greeter.py"
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def my_greeter_function(your_name):
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return f'Hi {your_name}!'
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```
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You can simply decorate (wrap) this function with the `GreatAI.create` factory.
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```python title="greeter.py"
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from great_ai import GreatAI
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@GreatAI.create
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def greeter(your_name):
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return f'Hi {your_name}!'
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```
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??? info "Why not simply use `@GreatAI?`"
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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.
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## With types
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[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.
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```python title="type_safe_greeter.py"
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from great_ai import GreatAI
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@GreatAI.create
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def type_safe_greeter(your_name: str) -> str:
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return f'Hi {your_name}!'
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```
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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 }.
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## With async
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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.
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```python title="async_greeter.py"
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from great_ai import GreatAI
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from asyncio import sleep
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@GreatAI.create
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async def async_greeter(your_name: str) -> str:
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await sleep(2) # simulate IO-heavy operation
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return f'Hi {your_name}!'
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```
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## With decorators
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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.
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### Using `use_model`
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If you have previously saved a model with `save_model`, you can inject it into your function by calling `use_model`.
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```python title="greeter_with_model.py"
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from great_ai import GreatAI, use_model
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@GreatAI.create
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@use_model('name_of_my_model', version='latest') #(1)
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def type_safe_greeter(your_name: str, model) -> str:
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return f'Hi {your_name}!'
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assert type_safe_greeter('Andras').output == 'Hi Andras'
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```
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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.
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!!! important
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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.
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### Using `parameter`
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If you wish to turn of logging or specify custom validation for your parameters, you can use the `@parameter` decorator.
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!!! note
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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.
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```python "greeter_with_validation.py"
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from great_ai import GreatAI, use_model
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@GreatAI.create
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@use_model('name_of_my_model', version='latest')
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def type_safe_greeter(your_name: str, model) -> str:
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return f'Hi {your_name}!'
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assert type_safe_greeter('Andras').output == 'Hi Andras'
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```
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!!! important
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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.
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## Complex example
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Refer to the following example summarising the options you have when instantiating a GreatAI service.
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```python title="complex.py"
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from great_ai import save_model, GreatAI, parameter, use_model
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save_model(4, 'secret-number') #(2)
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@GreatAI.create
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@parameter('positive_number', validator=lambda n: n > 0)
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@use_model('secret-number', version='latest', model_kwarg_name='secret')
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def add_number(positive_number: int, secret: int) -> int:
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return positive_number + secret
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assert add_number(1).output == 5
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```
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2. Refer to [storing models](/how-to-guides/store-models) for specifying where to store your models.
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