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,
