FIX #58 - Move MlflowPipelineHook args to PipelineML class #60
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This will close #53 btw. |
Galileo-Galilei
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turn1a
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Sep 8, 2020
kedro_mlflow/pipeline/pipeline_ml.py
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| """Store all necessary information for calling mlflow.log_model in the pipeline. | ||
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| Args: | ||
| nodes (Iterable[Union[Node,): The `node`s of the training pipeline. |
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- The type of nodes needs updating.
- The order of arguments in docstring is wrong.
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I did not notice that the autodocstring extension changed the order of the argument in the docstring. Idon't know why, maybe because of *args not being the last argument.
kedro_mlflow/pipeline/pipeline_ml.py
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| - a path to an "environment.yml" : data is loaded and used as they are | ||
| - a Dict : used as the environment | ||
| - None: a base conda environment with your current python version and your project version at training time. | ||
| Defaults to None. |
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- This should be indented with one more tab
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Well spotted. I have also refactored the docstring to make shorter lines length for readibility.
If you put |
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…lineHook` to `PipelineML` class
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conda_envandmodel_namearguments fromMlflowPipelineHooktoPipelineMLclass #58: moveMlflowPipelineHookargs toPipelineMLclassRemoveflatten_dict_paramfromMlflowNodeHook#59: moveMlflowNodeHookargs toKedroMlflowConfigclassI have changed my mind: #59 is much trickier than it seems because of the need to import
get_mlflow_configinside the hook which has a lot of side effects. For the sake of clarity, let's merge only the fix for #58 first. I'll open a separated PR for #59.