#Checking chuster available or create new cluster aml_name = 'cpu-cluster' try: aml_compute = AmlCompute(ws, aml_name) print('Found existing AML compute context.') except: print('Creating new AML compute context.') aml_config = AmlCompute.provisioning_configuration(vm_size=vm_size, min_nodes=min_nodes, max_nodes=max_nodes) aml_compute = AmlCompute.create(ws, name=aml_name, provisioning_configuration=aml_config) aml_compute.wait_for_completion(show_output=True) #list of experiment with you have created in your workspace from azureml.core.experiment import Experiment list_experiments = Experiment.list(ws) list_experiments import azurerm token = azurerm.get_access_token_from_cli() azurerm.list_subscriptions(token) #Delete full workspace ws.delete(delete_dependent_resources=True, no_wait=False) ##Delete your computor cluster aml_compute.delete()
X=x_df, y=y_df, compute_target=aml_name, run_configuration=run_config, iterations=iterations, iteration_timeout_minutes=iteration_timeout_minutes, primary_metric=primary_metric, #n_cross_validations=n_cross_validations, preprocess=True) from azureml.core.experiment import Experiment experiment = Experiment(ws, 'automl_remote') remote_run = experiment.submit(automl_config, show_output=True) best_model, fitted_model = remote_run.get_output() delete = ws.delete(delete_dependent_resources=False, no_wait=False) # create a TabularDataset from multiple paths in datastore datastore_paths = [ (datastore, 'D:/Stock_Prediction/AutoML_Azure/stocks_data/stocks_data/2018Q4PredictionTestSet101.csv' ), (datastore, 'D:/Stock_Prediction/AutoML_Azure/stocks_data/stocks_data/2018Q4PredictionTestSet10.csv' ), (datastore, 'D:/Stock_Prediction/AutoML_Azure/stocks_data/stocks_data/2018Q4PredictionTrainedSet10.csv' ) ] stock_ds = Dataset.Tabular.from_delimited_files(path=datastore_paths)