Flex-Kedro: Kedro plugin for flexible config

pipelinex.flex_kedro API document

Flex-Kedro provides more options to configure Kedro projects flexibly and thus quickly by KFlex-Kedro-Pipeline and Flex-Kedro-Context features.

Flex-Kedro-Pipeline: Kedro plugin for quicker pipeline set up

If you want to define Kedro pipelines quickly, you can consider to use pipelinex.FlexiblePipeline instead of kedro.pipeline.Pipeline. pipelinex.FlexiblePipeline adds the following options to kedro.pipeline.Pipeline.

• Optionally specify the default Python module (path of .py file) if you want to omit the module name

• Optionally specify the Python function decorator to apply to each node

• For sub-pipelines consisting of nodes of only single input and single output, you can optionally use Sequential API similar to PyTorch (torch.nn.Sequential) and Keras (tf.keras.Sequential)

An example is available in the Flex-Kedro-Context section.

Flex-Kedro-Context: Kedro plugin for YAML lovers

If you want to take advantage of YAML more than Kedro supports, you can consider to use pipelinex.FlexibleContext instead of kedro.framework.context.KedroContext. pipelinex.FlexibleContext adds preprocess of parameters.yml and catalog.yml to kedro.framework.context.KedroContext to provide flexibility. This option is for YAML lovers only. If you don’t like YAML very much, skip this one.

Define Kedro pipelines in parameters.yml

You can define the inter-task dependency (DAG) for Kedro pipelines in parameters.yml using PIPELINES key. To define each Kedro pipeline, you can use the kedro.pipeline.Pipeline or its variant such as pipelinex.FlexiblePipeline as shown below.

# parameters.yml

PIPELINES:
  __default__:
    =: pipelinex.FlexiblePipeline
    module: # Optionally specify the default Python module so you can omit the module name to which functions belongs
    decorator: # Optionally specify function decorator(s) to apply to each node
    nodes:
      - inputs: ["params:model", train_df, "params:cols_features", "params:col_target"]
        func: sklearn_demo.train_model
        outputs: model

      - inputs: [model, test_df, "params:cols_features"]
        func: sklearn_demo.run_inference
        outputs: pred_df

Configure Kedro run config using RUN_CONFIG key in parameters.yml

• Optionally run nodes in parallel

• Optionally run only missing nodes (skip tasks which have already been run to resume pipeline using the intermediate data files or databases.)

Note: You can use Kedro CLI to overwrite these run configs.

# parameters.yml

RUN_CONFIG:
  pipeline_name: __default__
  runner: SequentialRunner # Set to "ParallelRunner" to run in parallel
  only_missing: False # Set True to run only missing nodes
  tags: # None
  node_names: # None
  from_nodes: # None
  to_nodes: # None
  from_inputs: # None
  load_versions: # None

Use HatchDict feature in parameters.yml

You can use HatchDict feature in parameters.yml.

# parameters.yml

model:
  =: sklearn.linear_model.LogisticRegression
  C: 1.23456
  max_iter: 987
  random_state: 42
cols_features: # Columns used as features in the Titanic data table
  - Pclass # The passenger's ticket class
  - Parch # # of parents / children aboard the Titanic
col_target: Survived # Column used as the target: whether the passenger survived or not

Enable caching for Kedro DataSets in catalog.yml

Enable caching using cached key set to True if you do not want Kedro to load the data from disk/database which were in the memory. (kedro.io.CachedDataSet is used under the hood.)

Use HatchDict feature in catalog.yml

You can use HatchDict feature in catalog.yml.