from azureml.pipeline.core import PipelineData from azureml.pipeline.steps import PythonScriptStep # Get the training dataset diabetes_ds = ws.datasets.get("diabetes dataset") # Create a PipelineData for the model folder prepped_data_folder = PipelineData("prepped_data_folder", datastore=ws.get_default_datastore()) # Step 1: Run the data prep script prep_step = PythonScriptStep(name = "Prepare Data", source_directory = experiment_folder, script_name = "prep_diabetes.py", arguments = ['--input-data', diabetes_ds.as_named_input('raw_data'), '--prepped-data', prepped_data_folder], outputs=[prepped_data_folder], compute_target = pipeline_cluster, runconfig = pipeline_run_config, allow_reuse = True) # Step 2: Run the training script train_step = PythonScriptStep(name = "Train and Register Model", source_directory = experiment_folder, script_name = "train_diabetes.py", arguments = ['--training-folder', prepped_data_folder], inputs=[prepped_data_folder], compute_target = pipeline_cluster, runconfig = pipeline_run_config, allow_reuse = True) print("Pipeline steps defined")
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