def _exp_final_callback(self, job_end: float, exp_json: dict) -> dict:
"""Calculates the average test error from all partitions.
:param job_end: Time of the job end.
:param _: Catches additional callback arguments.
:returns: The result in a dictionary.
"""
experiment_json = {"state": "FINISHED"}
final_result = self.result_dict
util.finalize_experiment(
exp_json,
final_result[self.main_metric_key],
self.app_id,
self.run_id,
experiment_json,
self.duration,
self.log_dir,
None,
None,
)
self.experiment_done = True
self.prep_results(str(job_end))
print("List of the results:\n")
for key, value in final_result.items():
print(key, " : ", value)
print("Finished experiment. Total run time: " +
util.time_diff(self.job_start, job_end))
return {"test result": final_result}
def _exp_final_callback(self, job_end: float, exp_json: dict) -> dict:
"""Calculates the average test error from all partitions.
:param job_end: Time of the job end.
:param _: Catches additional callback arguments.
:returns: The result in a dictionary.
"""
experiment_json = {"state": "FINISHED"}
final_result = self.average_metric()
util.finalize_experiment(
exp_json,
final_result,
self.app_id,
self.run_id,
experiment_json,
self.duration,
self.log_dir,
None,
None,
)
self.experiment_done = True
self.prep_results(str(job_end))
print("Final average test loss: {:.3f}".format(final_result))
print("Finished experiment. Total run time: " +
util.time_diff(self.job_start, job_end))
return {"test result": self.average_metric()}
def finalize(self, job_end: float) -> dict:
"""Saves a summary of the experiment to a dict and logs it in the DFS.
:param job_end: Time of the job end.
:returns: The experiment summary dict.
"""
self.job_end = job_end
self.duration = util.seconds_to_milliseconds(self.job_end -
self.job_start)
duration_str = util.time_diff(self.job_start, self.job_end)
results = self.prep_results(duration_str)
print(results)
self.log(results)
EnvSing.get_instance().dump(
json.dumps(self.result, default=util.json_default_numpy),
self.log_dir + "/result.json",
)
EnvSing.get_instance().dump(self.json(), self.log_dir + "/maggy.json")
return self.result_dict
def _exp_final_callback(self, job_end: float, _: Any) -> dict:
"""Calculates the average test error from all partitions.
:param job_end: Time of the job end.
:param _: Catches additional callback arguments.
:returns: The result in a dictionary.
"""
result = {"test result": self.average_metric()}
exp_ml_id = str(self.app_id) + "_" + str(self.run_id)
EnvSing.get_instance().attach_experiment_xattr(
exp_ml_id,
{"state": "FINISHED", "duration": int(job_end - self.job_start) * 1000},
"FULL_UPDATE",
)
print("Final average test loss: {:.3f}".format(self.average_metric()))
print(
"Finished experiment. Total run time: "
+ util.time_diff(self.job_start, job_end)
)
return result
