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