azure_ml_sdk.services.asset.dataset
===================================

.. py:module:: azure_ml_sdk.services.asset.dataset


Classes
-------

.. autoapisummary::

   azure_ml_sdk.services.asset.dataset.DatasetManager


Module Contents
---------------

.. py:class:: DatasetManager(ml_client: azure.ai.ml.MLClient)

   Bases: :py:obj:`altametris.azure_ml_sdk.services.asset.base.BaseAssetManager`


   Class for handling datasets in Azure Machine Learning workspace.


   .. py:method:: register(datastore_name: str, data_path: str, dataset_name: str, dataset_version: Optional[str] = None, dataset_description: str = '') -> Union[azure.ai.ml.entities.Data, None]

      Register a dataset using a data folder in a datastore.

      Several cases can happen according to the input parameters of the dataset.
      If the data path is valid, and the version is valid, then the dataset will be registered.
      If the data path is invalid (not existing in the datastore), then a dataset will be registered anyway.
      If the same version is used, then an error will be raised.
      If the version is set to None, then the dataset will be registered with an incremented version.

      :param datastore_name: The name of the datastore that points to the data.
      :type datastore_name: str
      :param data_path: The path of the data folder in the datastore.
      :type data_path: str
      :param dataset_name: The name of the dataset to be registered.
      :type dataset_name: str
      :param dataset_version: The version of the dataset to be registered. Defaults to None
      :type dataset_version: str
      :param dataset_description: The description of the dataset to be registered. Defaults to empty string.
      :type dataset_description: str