Flyte Pipeline in One Jupyter Notebook

Once you have a Union account, install union:

pip install union

Export the following environment variable to build and push images to your own container registry:

        
# replace with your registry name
export IMAGE_SPEC_REGISTRY="<your-container-registry>"

Then run the following commands to run the workflow:

        
    
git clone https://github.com/unionai/unionai-examples
cd unionai-examples
union run --remote tutorials/sentiment_classifier/sentiment_classifier.py main --model distilbert-base-uncased

The source code for this tutorial can be found here {octicon}mark-github.

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 pathlib

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

We define a NotebookTask to run the Jupyter notebook. This notebook returns mae_score as the output.

        
    
nb = NotebookTask(
    name="pipeline-nb",
    notebook_path=str(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())