def _send_ok_response_to_mq(self, job_id, status_code, status_msg, result):
"""
This function sends response back to the MLCP (Machine Learning Control Panel) via queue if the job succeeded
:param job_id: the job identifier used for monitoring via the MLCP.
:param status_code: result status of the flow run.
:param status_msg: result message of the flow run.
:param result: response of the flow run - if json serialized returned in the message queue as well.
:return: None
"""
response_obj = {
"job_id": job_id,
"status_code": status_code,
"status_msg": status_msg
}
try:
# trying to JSON-ify result object
response_obj['result'] = result
response_json = json.dumps(response_obj)
except Exception as error:
print(error)
response_obj['result'] = {}
response_json = json.dumps(response_obj)
message_queue_instance.send_message(
settings['queues']['send_queue_name'], response_json)
def _dispatch_jobs_to_mq(self, configurations):
"""
This function sends configurations to the queue to be picked up later by a listening Application/Worker.
:param configurations: list of configurations to be sent
:return: None
"""
for configuration in configurations:
response_json = json.dumps(configuration)
message_queue_instance.send_message(settings['queues']['send_queue_name'], json.dumps(response_json))
def _send_error_response_to_mq(self, job_id, status_code, status_msg):
"""
This function sends response back to the MLCP (Machine Learning Control Panel) via queue if the job failed
:param job_id: the job identifier used for monitoring via the MLCP.
:param status_code: error status of the flow run.
:param status_msg: error message of the flow run.
:return: None
"""
response_json = json.dumps({"job_id": job_id, "status_code": status_code, "status_msg": status_msg})
message_queue_instance.send_message(settings['queues']['send_queue_name'], response_json)
def run_msg_sender(self, asset_name, config_path, config_name=None):
"""
This function is an endpoint of the ML App Library to be used in a local environment.
It sends a local configuration file in your local computer to be run in an outside Application/Worker via
message queue.
:param asset_name: name of the asset to be run
:param config_path: path to configuration file
:param config_name: in case configuration file is python looks for variable in this name as the configuration
"""
try:
message_to_send = read_json_file(config_path)
except Exception as e:
message_to_send = self._read_py_file(asset_name, config_path, config_name)
job_id = str(uuid.uuid4())
message_to_send['job_id'] = job_id
message_queue_instance.send_message(settings['queues']['listen_queue_names'][0], json.dumps(message_to_send))
print("Message Sent (job_id: " + job_id + "): ", asset_name, config_path)