Submit child run with an unregistered dataset in script. training_ds = unregistered_ds.register(workspace = workspace, In the following example, training_ds is the name that would be displayed. For this scenario, the name assigned to the dataset when you registered it to the workspace is the name displayed. OutputFileDatasetConfig objects can also be used to persist data between pipeline steps. Pass an OutputFileDatasetConfig object through either the outputs or arguments parameter when submitting an experiment run.
The following are scenarios where your data is tracked as an output dataset. When methods like, get_by_name() or get_by_id() are called in your script. The following are scenarios where your data is tracked as an input dataset.Īs a DatasetConsumptionConfig object through either the inputs or arguments parameter of your ScriptRunConfig object when submitting the experiment run. Inputs=,Īzure Machine Learning tracks your data throughout your experiment as input and output datasets. Prep_step = PythonScriptStep(script_name="prepare.py", # configure pipeline step to use dataset as the input and output
Output_ds = output_ds.register(name='prepared_weather_ds', create_new_version=True) Output_ds = PipelineData('prepared_weather_ds', datastore=datastore).as_dataset() Input_ds = Dataset.get_by_name(workspace, 'weather_ds') runconfig import CondaDependencies, RunConfiguration from re import Datasetįrom import PythonScriptStepįrom import Pipeline, PipelineDataįrom re. This behavior allows the versioned output datasets to be reproducible. ML pipelines populate the output of each step into a new folder every time the pipeline reruns. When you rerun pipelines, the output of each pipeline step is registered as a new dataset version. You can use a dataset as the input and output of each ML pipeline step. # create & register weather_ds version 2 pointing to all files in the folder of week 27 and 28ĭatastore_path2 = ĭataset2 = _files(path = datastore_path2)ĭescription = 'weather data in week 27, 28', # create & register weather_ds version 1 pointing to all files in the folder of week 27ĭatastore_path1 = ĭataset1 = _files(path=datastore_path1) # get the default datastore of the workspaceĭatastore = workspace.get_default_datastore()
#Updated version of interwrite workspace code#
The following image and sample code show the recommended way to structure your data folders and to create dataset versions that reference those folders: When new data comes in, save new data files into a separate data folder and then create a new dataset version to include data from that new folder. If you want to make sure that each dataset version is reproducible, we recommend that you not modify data content referenced by the dataset version. When you load data from a dataset, the current data content referenced by the dataset is always loaded. If the data referenced by your dataset is overwritten or deleted, calling a specific version of the dataset does not revert the change. Because datasets are references to the data in your storage service, you have a single source of truth, managed by your storage service. When you create a dataset version, you're not creating an extra copy of data with the workspace. Titanic_ds = Dataset.get_by_name(workspace = workspace, # Get a dataset by name and version number The following code gets version 1 of the titanic_ds dataset. titanic_ds = titanic_ds.register(workspace = workspace,īy default, the get_by_name() method on the Dataset class returns the latest version of the dataset registered with the workspace. If there's no existing titanic_ds dataset registered with the workspace, the code creates a new dataset with the name titanic_ds and sets its version to 1. The following code registers a new version of the titanic_ds dataset by setting the create_new_version parameter to True. You can register multiple datasets under the same name and retrieve a specific version by name and version number. import reīy registering a dataset, you can version, reuse, and share it across experiments and with colleagues. Retrieve an existing one by running the following code, or create a new workspace. This SDK includes the azureml-datasets package.Īn Azure Machine Learning workspace.
#Updated version of interwrite workspace how to#
In this article, you'll learn how to version and track Azure Machine Learning datasets for reproducibility.
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