03_Pipelines.md

Pipelines

PipelineML and pipeline_ml_factory

PipelineML is a new class which extends Pipeline and enable to bind two pipelines (one of training, one of inference) together. This class comes with a KedroPipelineModel class for logging it in mlflow. A pipeline logged as a mlflow model can be served using mlflow models serve and mlflow models predict command.

The PipelineML class is not intended to be used directly. A pipeline_ml_factory factory is provided for user friendly interface.

Example within kedro template:

# in src/PYTHON_PACKAGE/pipeline.py

from PYTHON_PACKAGE.pipelines import data_science as ds


def create_pipelines(**kwargs) -> Dict[str, Pipeline]:
    data_science_pipeline = ds.create_pipeline()
    training_pipeline = pipeline_ml_factory(
        training=data_science_pipeline.only_nodes_with_tags(
            "training"
        ),  # or whatever your logic is for filtering
        inference=data_science_pipeline.only_nodes_with_tags("inference"),
    )

    return {
        "ds": data_science_pipeline,
        "training": training_pipeline,
        "__default__": data_engineering_pipeline + data_science_pipeline,
    }

Now each time you will run kedro run --pipeline=training (provided you registered MlflowHook in you run.py), the full inference pipeline will be registered as a mlflow model (with all the outputs produced by training as artifacts : the machine learning model, but also the scaler, vectorizer, imputer, or whatever object fitted on data you create in training and that is used in inference).

Note that:

• the inference pipeline input_name can be a MemoryDataset and it belongs to inference pipeline inputs
• Apart form input_name, all other inference pipeline inputs must be persisted locally on disk (i.e. it must not be MemoryDataset and must have a local filepath)
• the inference pipeline inputs must belong to training outputs (vectorizer, binarizer, machine learning model...)
• the inference pipeline must have one and only one output

``PipelineML`` objects do not implement all filtering methods of a regular ``Pipeline``, and you cannot add or substract 2 ``PipelineML`` together. The rationale is that a filtered ``PipelineML`` is not a ``PipelineML`` in general, because the [filtering is not consistent between training and inference](https://github.com/Galileo-Galilei/kedro-mlflow/issues/554). You can see the ones which are supported [in the code](https://github.com/Galileo-Galilei/kedro-mlflow/blob/master/kedro_mlflow/pipeline/pipeline_ml.py#L162).

You can also directly log a PipelineML object in mlflow programatically:

from pathlib import Path
from kedro.framework.context import load_context
from kedro_mlflow.mlflow import KedroPipelineModel
from mlflow.models import ModelSignature

# pipeline_training is your PipelineML object, created as previsously
catalog = load_context(".").io

# artifacts are all the inputs of the inference pipelines that are persisted in the catalog
artifacts = pipeline_training.extract_pipeline_artifacts(catalog)

# get the schema of the input dataset
input_data = catalog.load(pipeline_training.input_name)
model_signature = infer_signature(model_input=input_data)

mlflow.pyfunc.log_model(
    artifact_path="model",
    python_model=KedroPipelineModel(pipeline=pipeline_training, catalog=catalog),
    artifacts=artifacts,
    conda_env={"python": "3.10.0", dependencies: ["kedro==0.18.11"]},
    signature=model_signature,
)

It is also possible to pass arguments to KedroPipelineModel to specify the runner or the copy_mode of MemoryDataset for the inference Pipeline. This may be faster especially for compiled model (e.g keras, tensorflow...), and more suitable for an API serving pattern. Since kedro-mlflow==0.12.0, copy_mode="assign" has become the default.

KedroPipelineModel(pipeline=pipeline_training, catalog=catalog, copy_mode="assign")

Available copy_mode are assign, copy and deepcopy. It is possible to pass a dictionary to specify different copy mode for each dataset.