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I used pipeline_ml_factory in kedro-mlflow-tutorial and found that log_model_kwargs and kpm_kwargs argument of are ignored : Galileo-Galilei/kedro-mlflow-tutorial#17
Context
I was trying to store the model on mlflow with a different artifact path than the default one
Steps to Reproduce
Specify log_model_kwargs={"artifact_path": "fake"} (cf.link to above issue) in pipeline_ml_factory
run the pipeline (kedro run --pipeline=training)
Open the ui kedro mlflow ui
Check the last run: the artifact is named "model" instead of "fake"
Your Environment
Include as many relevant details about the environment in which you experienced the bug:
kedro and kedro-mlflow version used (pip show kedro and pip show kedro-mlflow): kedro=0.18.1 and kedro-mlflow=0.10.0 (the latest)
Python version used (python -V): all
Operating system and version: all
Does the bug also happen with the last version on master?
Yes
Solution
The root cause is that these args are not pass to the new "filter" pipeline method introduced in kedro==0.18. The internal conversion after filtering "_turn_pipeline_to_ml" should pass these args.
Description
I used
pipeline_ml_factory
in kedro-mlflow-tutorial and found thatlog_model_kwargs
andkpm_kwargs
argument of are ignored : Galileo-Galilei/kedro-mlflow-tutorial#17Context
I was trying to store the model on mlflow with a different artifact path than the default one
Steps to Reproduce
log_model_kwargs={"artifact_path": "fake"}
(cf.link to above issue) inpipeline_ml_factory
kedro run --pipeline=training
)kedro mlflow ui
Your Environment
Include as many relevant details about the environment in which you experienced the bug:
kedro
andkedro-mlflow
version used (pip show kedro
andpip show kedro-mlflow
): kedro=0.18.1 and kedro-mlflow=0.10.0 (the latest)python -V
): allDoes the bug also happen with the last version on master?
Yes
Solution
The root cause is that these args are not pass to the new "filter" pipeline method introduced in
kedro==0.18
. The internal conversion after filtering "_turn_pipeline_to_ml" should pass these args.kedro-mlflow/kedro_mlflow/pipeline/pipeline_ml.py
Lines 168 to 171 in 9f005f6
should be:
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