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 0.8.x to 0.9.x

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). The setup() method needs to be called with context:

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

context = load_context(".")

# the new best practice is just to remove these lines
mlflow_config = get_mlflow_config(context)  # pass context instead of session
mlflow_config.setup(context)  # pass context instead of session

This is not necessary: the mlflow config is automatically set up when the context is loaded, so unless you need to access the config manually you can get rid of these 2 lines

Migration from 0.7.x to 0.8.x

Update the mlflow.yml configuration file with kedro mlflow init --force command
pipeline_ml_factory(pipeline_ml=<your-pipeline-ml>,...) (resp. KedroPipelineModel(pipeline_ml=<your-pipeline-ml>, ...)) first argument is renamed pipeline. Change the call to pipeline_ml_factory(pipeline=<your-pipeline-ml>) (resp. KedroPipelineModel(pipeline=<your-pipeline-ml>, ...)).
Change the call from pipeline_ml_factory(..., model_signature=<model-signature>, conda_env=<conda-env>, model_name=<model_name>) to `` pipeline_ml_factory(…, log_model_kwargs=dict(signature=, conda_env=, artifact_path=<model_name>}). Notice that the arguments are renamed to match mlflow's and they are passed as a dict in log_model_kwargs`.

Migration from 0.6.x to 0.7.x

If you are working with kedro==0.17.0, update your template to kedro>=0.17.1.

Migration from 0.5.x to 0.6.x

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.x to 0.5.x

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.config 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.x to 0.4.x

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.