def init():
global model
# This name is model.id of model that we want to deploy deserialize the model file back
# into a sklearn model
model_path = os.path.join(os.getenv('AZUREML_MODEL_DIR'), 'model.pkl')
try:
model = joblib.load(model_path)
except Exception as e:
path = os.path.normpath(model_path)
path_split = path.split(os.sep)
log_server.update_custom_dimensions({'model_name': path_split[1], 'model_version': path_split[2]})
logging_utilities.log_traceback(e, logger)
raise
def init():
global model
#model_path = Model.get_model_path("best_hyperdrive_model")
model_path = os.path.join(os.getenv('AZUREML_MODEL_DIR'), 'model.joblib')
path = os.path.normpath(model_path)
path_split = path.split(os.sep)
log_server.update_custom_dimensions({
'model_name': path_split[1],
'model_version': path_split[2]
})
try:
logger.info("Loading model from path.")
model = joblib.load(model_path)
logger.info("Loading successful.")
except Exception as e:
logging_utilities.log_traceback(e, logger)
raise
def init():
global model
# This name is model.id of model that we want to deploy deserialize the model file back
# into a sklearn model
model_base_path = os.path.join(os.getenv('AZUREML_MODEL_DIR'), 'outputs')
model_path = os.path.join(model_base_path, 'model.pkl')
path = os.path.normpath(model_path)
path_split = path.split(os.sep)
log_server.update_custom_dimensions({
'model_name': path_split[-3],
'model_version': path_split[-2]
})
try:
logger.info("Loading model from path.")
model = joblib.load(model_path)
logger.info("Loading successful.")
except Exception as e:
logging_utilities.log_traceback(e, logger)
raise
def init():
global model
# This name is model.id of model that we want to deploy deserialize the model file back
# into a sklearn model
#model_path = os.path.join(os.getenv('AZUREML_MODEL_DIR'), 'automl_bestmodel.pkl')
#model_path= Model.get_model_path(model_name="automl_bestmodel.pkl")
#logger.error(model_path+" Muzammil1")
model_path = os.path.join(os.getenv('AZUREML_MODEL_DIR'), 'model.pkl')
#aar= os.listdir(model_path)
#print(aar)
#logger.error(aar+" Muzammil2")
path = os.path.normpath(model_path)
path_split = path.split(os.sep)
log_server.update_custom_dimensions({'model_name': path_split[1], 'model_version': path_split[2]})
try:
logger.info("Loading model from path.")
model = joblib.load(model_path)
logger.info("Loading successful.")
except Exception as e:
logging_utilities.log_traceback(e, logger)
raise
