Flama

Machine Learning Responsive

Scikit Learn

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import flama
from sklearn.neural_network import MLPClassifier

model = MLPClassifier(activation="tanh", max_iter=2000, hidden_layer_sizes=(10,))
model.fit(
    np.array([[0, 0], [0, 1], [1, 0], [1, 1]]),
    np.array([0, 1, 1, 0]),
)

flama.dump(model, "sklearn_model.flm")

Production-Ready First

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Command Line

> flama serve path/to/model.flm

INFO:     Started server process [78260]
INFO:     Waiting for application startup.
INFO:     Application startup complete.
INFO:     Uvicorn running on http://127.0.0.1:8000 (Press CTRL+C to quit)

Effortless Development

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There is a wide spectrum of data validation libraries for Python to combine data types into structures, validate them, and provide tools for serialisation of app-level objects to primitive Python types.

Flama natively supports both Typesystem and Marshmallow, data type validation libraries which make possible the standardisation of the API via generation of OpenAPI schemas, and allow the user to define API schemas effortlessly.

Flama Schema generator gathers all the API information needed directly from your code and infers the schema that represents your API based on OpenAPI standard. The schema will be also served at the route /schema/ by default.

Models Lifecycle

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Models Lifecycle

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from flama import Flama, ModelComponentBuilder


with open("/path/to/model.flm", "rb") as f:
    component = ModelComponentBuilder.loads(f.read())
    ModelType = component.get_model_type  # Get the type to allow inject dependency


app = Flama(components=[component])


@app.get("/")
def model_view(model: ModelType, model_input: str):
    """
    tags:
        - model
    summary:
        Model prediction.
    description:
        Interact with the model to generate a prediction based on
        given input
    responses:
        200:
            description: Model prediction.
    """
    model_output = model.predict(model_input)
    return {"model_output": model_output}

Extensibility

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Extensibility

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import typing

import mlflow
from flama import Module, Flama


class MLFlowModule(Module):
    name = "mlflow"

    def __init__(self, app: Flama, url: str = None, *args, **kwargs):
        super().__init__(app, *args, **kwargs)
        self.url = url

    async def on_startup(self):
        mlflow.set_tracking_uri(self.url)

    async def on_shutdown(self):
        ...

    def search_runs(self, experiment_ids: typing.List[str], filter_string: str):
        return mlflow.search_runs(experiment_ids, filter_string)


app = Flama(modules=[MLFlowModule])

# Module usage example
model = app.mlflow.search_runs(["foo"], "tags.name = 'bar'")

Development Tools

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