def _save_example(mlflow_model: Model, input_example: ModelInputExample, path: str):
"""
Save example to a file on the given path and updates passed Model with example metadata.
The metadata is a dictionary with the following fields:
- 'artifact_path': example path relative to the model directory.
- 'type': Type of example. Currently the only supported value is 'dataframe'
- 'pandas_orient': Determines the json encoding for dataframe examples in terms of pandas
orient convention. Defaults to 'split'.
:param mlflow_model: Model metadata that will get updated with the example metadata.
:param path: Where to store the example file. Should be model the model directory.
"""
example = _Example(input_example)
example.save(path)
mlflow_model.saved_input_example_info = example.info
def _save_example(mlflow_model: Model, input_example: ModelInputExample, path: str):
"""
Save example to a file on the given path and updates passed Model with example metadata.
The metadata is a dictionary with the following fields:
- 'artifact_path': example path relative to the model directory.
- 'type': Type of example. Currently the supported values are 'dataframe' and 'ndarray'
- One of the following metadata based on the `type`:
- 'pandas_orient': Used to store dataframes. Determines the json encoding for dataframe
examples in terms of pandas orient convention. Defaults to 'split'.
- 'format: Used to store tensors. Determines the standard used to store a tensor input
example. MLflow uses a JSON-formatted string representation of TF serving
input.
:param mlflow_model: Model metadata that will get updated with the example metadata.
:param path: Where to store the example file. Should be model the model directory.
"""
example = _Example(input_example)
example.save(path)
mlflow_model.saved_input_example_info = example.info