kedro-wings 0.1.2

Kedro Wings automatically creates catalog entries to simplify Kedro pipeline writing.

Kedro Wings

Give your next kedro project Wings! The perfect plugin for brand new pipelines, and new kedro users. This plugin enables easy and fast creation of datasets so that you can get straight into coding your pipelines.

Iris Example

The following example is a recreation of the iris example pipeline.

Kedro Wings enables super fast creation of pipelines by taking care of all the catalog work for you. Catalog entries are automatically created by parsing the values for your nodes' inputs and outputs.

This pipeline automatically creates a dataset that reads from the iris.csv and then it creates 12 more datasets, corresponding to the outputs and inputs of the other datasets.

wing_example = Pipeline([
    node(
        split_data,
        inputs=['01_raw/iris.csv', 'params:example_test_data_ratio'],
        outputs=dict(
            train_x="02_intermediate/example_train_x.csv",
            train_y="02_intermediate/example_train_y.csv",
            test_x="02_intermediate/example_test_x.csv",
            test_y="02_intermediate/example_test_y.csv")
        ),
    node(
        train_model,
        ["02_intermediate/example_train_x.csv", "02_intermediate/example_train_y.csv", "parameters"],
        outputs="06_models/example_model.pkl",
    ),
    node(
        predict,
        inputs=dict(
            model="06_models/example_model.pkl",
            test_x="02_intermediate/example_test_x.csv"
        ),
        outputs="07_model_output/example_predictions.pkl",
    ),
    node(
        report_accuracy,
        inputs=["07_model_output/example_predictions.pkl", "02_intermediate/example_test_y.csv"],
        None
    ),
])

Installation

Kedro Wings is available on pypi, and is installed with kedro hooks.

pip install kedro-wings

from kedro_quick_pipes import KedroQuickPipes


class ProjectContext(KedroContext):
    hooks = (
        KedroQuickPipes(),
    )

Usage

Catalog Creation

Catalog entries are created using dataset input and output strings. The API is simple:

inputs="[PATH]/[NAME].[EXT]"

The PATH portion determines the directory where a file will be saved. The NAME portion determines the final output name of the file to be saved. The EXT portion determines the dataset used to save and load that particular data.

Default Datasets

The following are the datasets available by default.

".csv": {"type": "pandas.CSVDataSet"},
".yml": {"type": "yaml.YAMLDataSet"},
".yaml": {"type": "yaml.YAMLDataSet"},
".xls": {"type": "pandas.ExcelDataSet"},
".txt": {"type": "text.TextDataSet"},
".png": {"type": "pillow.ImageDataSet"},
".jpg": {"type": "pillow.ImageDataSet"},
".jpeg": {"type": "pillow.ImageDataSet"},
".img": {"type": "pillow.ImageDataSet"},
".pkl": {"type": "pickle.PickleDataSet"},

Configuration

Kedro Wings supports configuration on instantiation of the hook.

KedroWings(dataset_configs, paths, root, enabled)

dataset_configs

:param dataset_configs: A mapping of file name extensions to the type of dataset to be created.

Ex: Make default csv files use pipes as separators
dataset_configs={
    '.csv': {'type': 'pandas.CSVDataSet', 'sep': '|'},
}

This allows the default dataset configurations to be overridden. This also allows the default extension to dataset mapping to be overridden or extended for other datasets.

paths

:param paths: A mapping of old path names to new path names.

Ex: Moving data from 06_models to a new_models folder
paths={
    '06_models': 'new_models',
}

This allows specified paths to be remmaped

root

:param root: The root directory to save files to. Default: data

Ex: Saving data to s3 instead of the local directory.
root='s3a://my-bucket/kedro-data'

This setting is prepended to any paths parsed. This is useful if the dataset supports fsspec.

enabled

:param enabled: Convenience flag to enable or disable this plugin.

Ex: Use an environment variable to enable or disable wings
enabled=os.getenv('ENABLE_WINGS')
```

This setting allows easy enabling and disabling of the plugin.