def _handle_py4j_error(reraised_error_type, reraised_error_text):
"""
Logs information about an exception that is currently being handled
and reraises it with the specified error text as a message.
"""
traceback.print_exc()
tb = sys.exc_info()[2]
reraise(reraised_error_type, reraised_error_type(reraised_error_text), tb)
def _handle_serving_error(error_message, error_code, include_traceback=True):
"""
Logs information about an exception thrown by model inference code that is currently being
handled and reraises it with the specified error message. The exception stack trace
is also included in the reraised error message.
:param error_message: A message for the reraised exception.
:param error_code: An appropriate error code for the reraised exception. This should be one of
the codes listed in the `mlflow.protos.databricks_pb2` proto.
:param include_traceback: Whether to include the current traceback in the returned error.
"""
if include_traceback:
traceback_buf = StringIO()
traceback.print_exc(file=traceback_buf)
traceback_str = traceback_buf.getvalue()
e = MlflowException(message=error_message, error_code=error_code, stack_trace=traceback_str)
else:
e = MlflowException(message=error_message, error_code=error_code)
reraise(MlflowException, e)