---

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Please check here for complete documentation

Example

The below data science flavored code is a well-known iris example from scikit-learn.

"""
Example of Logistic regression using scikit-learn
https://scikit-learn.org/stable/auto_examples/linear_model/plot_iris_logistic.html
"""

import matplotlib.pyplot as plt
import numpy as np
from sklearn import datasets
from sklearn.inspection import DecisionBoundaryDisplay
from sklearn.linear_model import LogisticRegression


def load_data():
    # import some data to play with
    iris = datasets.load_iris()
    X = iris.data[:, :2]  # we only take the first two features.
    Y = iris.target

    return X, Y


def model_fit(X: np.ndarray, Y: np.ndarray, C: float = 1e5):
    logreg = LogisticRegression(C=C)
    logreg.fit(X, Y)

    return logreg


def generate_plots(X: np.ndarray, Y: np.ndarray, logreg: LogisticRegression):
    _, ax = plt.subplots(figsize=(4, 3))
    DecisionBoundaryDisplay.from_estimator(
        logreg,
        X,
        cmap=plt.cm.Paired,
        ax=ax,
        response_method="predict",
        plot_method="pcolormesh",
        shading="auto",
        xlabel="Sepal length",
        ylabel="Sepal width",
        eps=0.5,
    )

    # Plot also the training points
    plt.scatter(X[:, 0], X[:, 1], c=Y, edgecolors="k", cmap=plt.cm.Paired)

    plt.xticks(())
    plt.yticks(())

    plt.savefig("iris_logistic.png")

    # TODO: What is the right value?
    return 0.6


## Without any orchestration
def main():
    X, Y = load_data()
    logreg = model_fit(X, Y, C=1.0)
    generate_plots(X, Y, logreg)


## With runnable orchestration
def runnable_pipeline():
    # The below code can be anywhere
    from runnable import Catalog, Pipeline, PythonTask, metric, pickled

    # X, Y = load_data()
    load_data_task = PythonTask(
        function=load_data,
        name="load_data",
        returns=[pickled("X"), pickled("Y")],  # (1)
    )

    # logreg = model_fit(X, Y, C=1.0)
    model_fit_task = PythonTask(
        function=model_fit,
        name="model_fit",
        returns=[pickled("logreg")],
    )

    # generate_plots(X, Y, logreg)
    generate_plots_task = PythonTask(
        function=generate_plots,
        name="generate_plots",
        terminate_with_success=True,
        catalog=Catalog(put=["iris_logistic.png"]),  # (2)
        returns=[metric("score")],
    )

    pipeline = Pipeline(
        steps=[load_data_task, model_fit_task, generate_plots_task],
    )  # (4)

    pipeline.execute()

    return pipeline


if __name__ == "__main__":
    # main()
    runnable_pipeline()

1. Return two serialized objects X and Y.
2. Store the file iris_logistic.png for future reference.
3. Define the sequence of tasks.
4. Define a pipeline with the tasks

The difference between native driver and runnable orchestration:

!!! tip inline end "Notebooks and Shell scripts"

You can execute notebooks and shell scripts too!!

They can be written just as you would want them, *plain old notebooks and scripts*.

- X, Y = load_data()
+load_data_task = PythonTask(
+    function=load_data,
+     name="load_data",
+     returns=[pickled("X"), pickled("Y")], (1)
+    )

-logreg = model_fit(X, Y, C=1.0)
+model_fit_task = PythonTask(
+   function=model_fit,
+   name="model_fit",
+   returns=[pickled("logreg")],
+   )

-generate_plots(X, Y, logreg)
+generate_plots_task = PythonTask(
+   function=generate_plots,
+   name="generate_plots",
+   terminate_with_success=True,
+   catalog=Catalog(put=["iris_logistic.png"]), (2)
+   )


+pipeline = Pipeline(
+   steps=[load_data_task, model_fit_task, generate_plots_task], (3)

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• Domain code remains completely independent of driver code.
• The driver function has an equivalent and intuitive runnable expression
• Reproducible by default, runnable stores metadata about code/data/config for every execution.
• The pipeline is runnable in any environment.

Documentation

More details about the project and how to use it available here.

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Installation

The minimum python version that runnable supports is 3.8

pip install runnable

Please look at the installation guide for more information.

Pipelines can be:

Linear

A simple linear pipeline with tasks either python functions, notebooks, or shell scripts

Parallel branches

Execute branches in parallel

loops or map

Execute a pipeline over an iterable parameter.

Arbitrary nesting

Any nesting of parallel within map and so on.