Migration guide

This page explains how to migrate an existing kedro project to a more up to date kedro-mlflow versions with breaking changes.

Migration from 0.5.0 to 0.6.0

kedro==0.16.x is no longer supported. You need to update your project template to kedro==0.17.0 template.

Migration from 0.4.1 to 0.5.0

The only breaking change with the previous release is the format of KedroPipelineMLModel class. Hence, if you saved a pipeline as a Mlflow Model with pipeline_ml_factory in kedro-mlflow==0.4.x, loading it (either with MlflowModelLoggerDataSet or mlflow.pyfunc.load_model) with kedro-mlflow==0.5.0 installed will raise an error. You will need either to retrain the model or to load it with kedro-mlflow==0.4.x.

Migration from 0.4.0 to 0.4.1

There are no breaking change in this patch release except if you retrieve the mlflow configuration manually (e.g. in a script or a jupyter notebok). You must add an extra call to the setup() method:

from kedro.framework.context import load_context
from kedro_mlflow.framework.context import get_mlflow_config

context=load_context(".")
mlflow_config=get_mlflow_config(context)
mlflow_config.setup() # <-- add this line which did not exists in 0.4.0

Migration from 0.3.0 to 0.4.0

Catalog entries

Replace the following entries:

old	new
kedro_mlflow.io.MlflowArtifactDataSet	kedro_mlflow.io.artifacts.MlflowArtifactDataSet
kedro_mlflow.io.MlflowMetricsDataSet	kedro_mlflow.io.metrics.MlflowMetricsDataSet

Hooks

Hooks are now auto-registered if you use kedro>=0.16.4. You can remove the following entry from your run.py:

hooks = (
    MlflowPipelineHook(),
    MlflowNodeHook()
)

KedroPipelineModel

Be aware that if you have saved a pipeline as a mlflow model with pipeline_ml_factory, retraining this pipeline with kedro-mlflow==0.4.0 will lead to a new behaviour. Let assume the name of your output in the DataCatalog was predictions, the output of a registered model will be modified from:

{
    predictions:
        {
            <your model-predictions>
        }
}

to:

{
    <your model-predictions>
}

Thus, parsing the predictions of this model must be updated accordingly.