Flyte Pipeline in One Jupyter Notebook

In this example, we will implement a simple pipeline that takes hyperparameters, does EDA, feature engineering, and measures the Gradient Boosting model’s performance using mean absolute error (MAE), all in one notebook.

First, let’s import the libraries we will use in this example.

import os
import pathlib

from flytekit import Resources, kwtypes, workflow
from flytekitplugins.papermill import NotebookTask

We define a NotebookTask to run the Jupyter notebook.

NotebookTask Parameters

notebook_path	Path to the Jupyter notebook file
inputs	Inputs to be sent to the notebook
outputs	Outputs to be returned from the notebook
requests	Specify compute resource requests for your task.

This notebook returns mae_score as the output.

nb = NotebookTask(
    name="pipeline-nb",
    notebook_path=os.path.join(pathlib.Path(__file__).parent.absolute(), "supermarket_regression.ipynb"),
    inputs=kwtypes(
        n_estimators=int,
        max_depth=int,
        max_features=str,
        min_samples_split=int,
        random_state=int,
    ),
    outputs=kwtypes(mae_score=float),
    requests=Resources(mem="500Mi"),
)

Since a task need not be defined, we create a workflow and return the MAE score.

@workflow
def notebook_wf(
    n_estimators: int = 150,
    max_depth: int = 3,
    max_features: str = "sqrt",
    min_samples_split: int = 4,
    random_state: int = 2,
) -> float:
    output = nb(
        n_estimators=n_estimators,
        max_depth=max_depth,
        max_features=max_features,
        min_samples_split=min_samples_split,
        random_state=random_state,
    )
    return output.mae_score

We can now run the notebook locally.

if __name__ == "__main__":
    print(notebook_wf())