Custom Mlflow Models
- class kedro_mlflow.mlflow.kedro_pipeline_model.KedroPipelineModel(pipeline_ml: kedro_mlflow.pipeline.pipeline_ml.PipelineML, catalog: kedro.io.data_catalog.DataCatalog, runner: Optional[kedro.runner.runner.AbstractRunner] = None, copy_mode: Optional[Union[Dict[str, str], str]] = None)
Bases:
mlflow.pyfunc.model.PythonModel
- __init__(pipeline_ml: kedro_mlflow.pipeline.pipeline_ml.PipelineML, catalog: kedro.io.data_catalog.DataCatalog, runner: Optional[kedro.runner.runner.AbstractRunner] = None, copy_mode: Optional[Union[Dict[str, str], str]] = None)
[summary]
- Parameters
pipeline_ml (PipelineML) – A PipelineML object to
Model (store as a Mlflow) –
catalog (The copy_mode of each DataSet of the) – The DataCatalog associated
PipelineMl (to the) –
runner (Optional[AbstractRunner], optional) – The kedro
if (AbstractRunner to use. Defaults to SequentialRunner) –
None. –
copy_mode (Optional[Union[Dict[str,str], str]]) –
catalog –
memory. (when reconstructing the DataCatalog in) –
either (You can pass) –
None to use Kedro default mode for each dataset
a single string (“deepcopy”, “copy” and “assign”)
to apply to all datasets - a dictionnary with (dataset name, copy_mode) key/values pairs. The associated mode must be a valid kedro mode (“deepcopy”, “copy” and “assign”) for each. Defaults to None.
- property copy_mode
- load_context(context)
Loads artifacts from the specified
PythonModelContext
that can be used bypredict()
when evaluating inputs. When loading an MLflow model withload_pyfunc()
, this method is called as soon as thePythonModel
is constructed.The same
PythonModelContext
will also be available during calls topredict()
, but it may be more efficient to override this method and load artifacts from the context at model load time.- Parameters
context – A
PythonModelContext
instance containing artifacts that the model can use to perform inference.
- predict(context, model_input)
Evaluates a pyfunc-compatible input and produces a pyfunc-compatible output. For more information about the pyfunc input/output API, see the pyfunc-inference-api.
- Parameters
context – A
PythonModelContext
instance containing artifacts that the model can use to perform inference.model_input – A pyfunc-compatible input for the model to evaluate.