def _init_logger(automl_settings_obj=None): sdk_has_custom_dimension_logger = False try: from azureml.telemetry import set_diagnostics_collection if automl_settings_obj is not None: set_diagnostics_collection(send_diagnostics=automl_settings_obj.send_telemetry, verbosity=automl_settings_obj.telemetry_verbosity) except: print("set_diagnostics_collection failed.") try: from azureml.train.automl._logging import get_logger if "automl_settings" in inspect.getcallargs(get_logger, log_file_name="AutoML_remote.log"): logger = get_logger(log_file_name="AutoML_remote.log", automl_settings=automl_settings_obj) sdk_has_custom_dimension_logger = True else: logger = get_logger(log_file_name="AutoML_remote.log") sdk_has_custom_dimension_logger = False logger.info("sdk_has_custom_dimension_logger {}.".format(sdk_has_custom_dimension_logger)) except ImportError: logger = logging.getLogger(__name__) logger.addHandler(logging.NullHandler()) logger.info("Init logger successfully with automl_settings {}.".format(automl_settings_obj)) try: from automl.client.core.common.utilities import get_sdk_dependencies logger.info(get_sdk_dependencies()) except Exception as e: pass return logger, sdk_has_custom_dimension_logger
workspace_name = '...' resource_group = '...' subscription_id = '...' workspace_region = '...' ws = Workspace.create(name=workspace_name, subscription_id=subscription_id, resource_group=resource_group, location=workspace_region, exist_ok=True) experiment_name = 'PdM_pipeline' # choose a name for experiment experiment = Experiment(ws, experiment_name) set_diagnostics_collection(send_diagnostics=True) # create AML compute aml_compute_target = "aml-compute" try: aml_compute = AmlCompute(ws, aml_compute_target) print("found existing compute target.") except: print("creating new compute target") provisioning_config = AmlCompute.provisioning_configuration( vm_size="STANDARD_D2_V2", idle_seconds_before_scaledown=1800, min_nodes=0, max_nodes=4) aml_compute = ComputeTarget.create(ws, aml_compute_target,