def init():
"""
Initializer called once per node that runs the scoring job. Parse command
line arguments and get the right model to use for scoring.
"""
try:
print("Initializing batch scoring script...")
model_filter = parse_args()
amlmodel = get_latest_model(
model_filter[0], model_filter[1], model_filter[2]
) # NOQA: E501
global model
modelpath = amlmodel.get_model_path(model_name=model_filter[0])
model = joblib.load(modelpath)
print("Loaded model {}".format(model_filter[0]))
except Exception as ex:
print("Error: {}".format(ex))
args = parser.parse_args()
if (args.run_id is not None):
run_id = args.run_id
if (run_id == 'amlcompute'):
run_id = run.parent.id
model_name = args.model_name
metric_eval = "mse"
allow_run_cancel = args.allow_run_cancel
# Parameterize the matrices on which the models should be compared
# Add golden data set on which all the model performance can be evaluated
try:
firstRegistration = False
tag_name = 'experiment_name'
model = get_latest_model(
model_name, tag_name, exp.name, ws)
if (model is not None):
production_model_mse = 10000
if (metric_eval in model.tags):
production_model_mse = float(model.tags[metric_eval])
new_model_mse = float(run.parent.get_metrics().get(metric_eval))
if (production_model_mse is None or new_model_mse is None):
print("Unable to find", metric_eval, "metrics, "
"exiting evaluation")
if((allow_run_cancel).lower() == 'true'):
run.parent.cancel()
else:
print(
"Current Production model mse: {}, "
"New trained model mse: {}".format(