def start_job(cls, job_id, initiator_role, initiator_party_id):
schedule_logger(job_id=job_id).info(
"Try to start job {} on initiator {} {}".format(
job_id, initiator_role, initiator_party_id))
job_info = {}
job_info["job_id"] = job_id
job_info["role"] = initiator_role
job_info["party_id"] = initiator_party_id
job_info["status"] = JobStatus.RUNNING
job_info["party_status"] = JobStatus.RUNNING
job_info["start_time"] = current_timestamp()
job_info["tag"] = 'end_waiting'
jobs = JobSaver.query_job(job_id=job_id,
role=initiator_role,
party_id=initiator_party_id)
if jobs:
job = jobs[0]
FederatedScheduler.start_job(job=job)
schedule_logger(job_id=job_id).info(
"start job {} on initiator {} {}".format(
job_id, initiator_role, initiator_party_id))
else:
schedule_logger(job_id=job_id).error(
"can not found job {} on initiator {} {}".format(
job_id, initiator_role, initiator_party_id))
def report_task_to_initiator(cls, task_info):
tasks = JobSaver.query_task(task_id=task_info["task_id"],
task_version=task_info["task_version"],
role=task_info["role"],
party_id=task_info["party_id"])
if tasks[0].f_federated_status_collect_type == FederatedCommunicationType.PUSH:
FederatedScheduler.report_task_to_initiator(task=tasks[0])
def schedule_rerun_job(cls, job):
if EndStatus.contains(job.f_status):
job.f_status = JobStatus.WAITING
job.f_ready_signal = False
job.f_ready_time = None
job.f_rerun_signal = False
job.f_progress = 0
job.f_end_time = None
job.f_elapsed = None
schedule_logger(job_id=job.f_job_id).info(
f"job {job.f_job_id} has been finished, set waiting to rerun")
status, response = FederatedScheduler.sync_job_status(job=job)
if status == FederatedSchedulingStatusCode.SUCCESS:
cls.rerun_signal(job_id=job.f_job_id, set_or_reset=False)
FederatedScheduler.sync_job(job=job,
update_fields=[
"ready_signal", "ready_time",
"rerun_signal", "progress",
"end_time", "elapsed"
])
schedule_logger(job_id=job.f_job_id).info(
f"job {job.f_job_id} set waiting to rerun successfully")
else:
schedule_logger(job_id=job.f_job_id).info(
f"job {job.f_job_id} set waiting to rerun failed")
else:
cls.rerun_signal(job_id=job.f_job_id, set_or_reset=False)
cls.schedule_running_job(job)
def schedule_waiting_jobs(cls, event):
job_id, initiator_role, initiator_party_id, = event.f_job_id, event.f_initiator_role, event.f_initiator_party_id,
update_status = JobQueue.update_event(job_id=job_id,
initiator_role=initiator_role,
initiator_party_id=initiator_party_id,
job_status=JobStatus.READY)
if not update_status:
schedule_logger(job_id).info(f"job {job_id} may be handled by another scheduler")
return
# apply resource on all party
jobs = JobSaver.query_job(job_id=job_id, role=initiator_role, party_id=initiator_party_id)
if not jobs:
JobQueue.delete_event(job_id=job_id)
return
job = jobs[0]
apply_status_code, federated_response = FederatedScheduler.resource_for_job(job=job, operation_type=ResourceOperation.APPLY)
if apply_status_code == FederatedSchedulingStatusCode.SUCCESS:
cls.start_job(job_id=job_id, initiator_role=initiator_role, initiator_party_id=initiator_party_id)
JobQueue.update_event(job_id=job_id,
initiator_role=initiator_role,
initiator_party_id=initiator_party_id,
job_status=JobStatus.RUNNING)
else:
# rollback resource
rollback_party = {}
failed_party = {}
for dest_role in federated_response.keys():
for dest_party_id in federated_response[dest_role].keys():
retcode = federated_response[dest_role][dest_party_id]["retcode"]
if retcode == 0:
rollback_party[dest_role] = rollback_party.get(dest_role, [])
rollback_party[dest_role].append(dest_party_id)
else:
failed_party[dest_role] = failed_party.get(dest_role, [])
failed_party[dest_role].append(dest_party_id)
schedule_logger(job_id).info("job {} apply resource failed on {}, rollback {}".format(
job_id,
",".join([",".join([f"{_r}:{_p}" for _p in _ps]) for _r, _ps in failed_party.items()]),
",".join([",".join([f"{_r}:{_p}" for _p in _ps]) for _r, _ps in rollback_party.items()]),
))
if rollback_party:
return_status_code, federated_response = FederatedScheduler.resource_for_job(job=job, operation_type=ResourceOperation.RETURN, specific_dest=rollback_party)
if return_status_code != FederatedSchedulingStatusCode.SUCCESS:
schedule_logger(job_id).info(f"job {job_id} return resource failed:\n{federated_response}")
else:
schedule_logger(job_id).info(f"job {job_id} no party should be rollback resource")
if apply_status_code == FederatedSchedulingStatusCode.ERROR:
cls.stop_job(job_id=job_id, role=initiator_role, party_id=initiator_party_id, stop_status=JobStatus.FAILED)
schedule_logger(job_id).info(f"apply resource error, stop job {job_id}")
else:
update_status = JobQueue.update_event(job_id=job_id,
initiator_role=initiator_role,
initiator_party_id=initiator_party_id,
job_status=JobStatus.WAITING)
schedule_logger(job_id).info(f"update job {job_id} status to waiting {update_status}")
def finish(cls, job, end_status):
schedule_logger(job_id=job.f_job_id).info(
"Job {} finished with {}, do something...".format(
job.f_job_id, end_status))
cls.stop_job(job_id=job.f_job_id,
role=job.f_initiator_role,
party_id=job.f_initiator_party_id,
stop_status=end_status)
FederatedScheduler.clean_job(job=job)
schedule_logger(job_id=job.f_job_id).info(
"Job {} finished with {}, done".format(job.f_job_id, end_status))
def rerun_job(cls, job_id, initiator_role, initiator_party_id, component_name):
schedule_logger(job_id=job_id).info(f"try to rerun job {job_id} on initiator {initiator_role} {initiator_party_id}")
jobs = JobSaver.query_job(job_id=job_id, role=initiator_role, party_id=initiator_party_id)
if jobs:
job = jobs[0]
else:
raise RuntimeError(f"can not found job {job_id} on initiator {initiator_role} {initiator_party_id}")
if component_name != job_utils.job_virtual_component_name():
tasks = JobSaver.query_task(job_id=job_id, role=initiator_role, party_id=initiator_party_id, component_name=component_name)
else:
tasks = JobSaver.query_task(job_id=job_id, role=initiator_role, party_id=initiator_party_id)
job_can_rerun = False
dsl_parser = schedule_utils.get_job_dsl_parser(dsl=job.f_dsl,
runtime_conf=job.f_runtime_conf,
train_runtime_conf=job.f_train_runtime_conf)
for task in tasks:
if task.f_status in {TaskStatus.WAITING, TaskStatus.COMPLETE}:
if task.f_status == TaskStatus.WAITING:
job_can_rerun = True
schedule_logger(job_id=job_id).info(f"task {task.f_task_id} {task.f_task_version} on {task.f_role} {task.f_party_id} is {task.f_status}, pass rerun")
else:
# stop old version task
FederatedScheduler.stop_task(job=job, task=task, stop_status=TaskStatus.CANCELED)
FederatedScheduler.clean_task(job=job, task=task, content_type="metrics")
# create new version task
task.f_task_version = task.f_task_version + 1
task.f_run_pid = None
task.f_run_ip = None
FederatedScheduler.create_task(job=job, task=task)
# Save the status information of all participants in the initiator for scheduling
schedule_logger(job_id=job_id).info(f"create task {task.f_task_id} new version {task.f_task_version}")
for _role, _party_ids in job.f_runtime_conf["role"].items():
for _party_id in _party_ids:
if _role == initiator_role and _party_id == initiator_party_id:
continue
JobController.initialize_tasks(job_id, _role, _party_id, False, job.f_runtime_conf["initiator"], RunParameters(**job.f_runtime_conf["job_parameters"]), dsl_parser, component_name=task.f_component_name, task_version=task.f_task_version)
schedule_logger(job_id=job_id).info(f"create task {task.f_task_id} new version {task.f_task_version} successfully")
job_can_rerun = True
if job_can_rerun:
if EndStatus.contains(job.f_status):
job.f_status = JobStatus.WAITING
job.f_end_time = None
job.f_elapsed = None
job.f_progress = 0
schedule_logger(job_id=job_id).info(f"job {job_id} has been finished, set waiting to rerun")
status, response = FederatedScheduler.sync_job_status(job=job)
if status == FederatedSchedulingStatusCode.SUCCESS:
FederatedScheduler.sync_job(job=job, update_fields=["end_time", "elapsed", "progress"])
JobQueue.create_event(job_id=job_id, initiator_role=initiator_role, initiator_party_id=initiator_party_id)
schedule_logger(job_id=job_id).info(f"job {job_id} set waiting to rerun successfully")
else:
schedule_logger(job_id=job_id).info(f"job {job_id} set waiting to rerun failed")
else:
# status updates may be delayed, and in a very small probability they will be executed after the rerun command
schedule_logger(job_id=job_id).info(f"job {job_id} status is {job.f_status}, will be run new version waiting task")
else:
schedule_logger(job_id=job_id).info(f"job {job_id} no task to rerun")
def schedule(cls, job, dsl_parser, canceled=False):
schedule_logger(job_id=job.f_job_id).info("scheduling job {} tasks".format(job.f_job_id))
initiator_tasks_group = JobSaver.get_tasks_asc(job_id=job.f_job_id, role=job.f_role, party_id=job.f_party_id)
waiting_tasks = []
for initiator_task in initiator_tasks_group.values():
# collect all party task party status
if job.f_runtime_conf_on_party["job_parameters"]["federated_status_collect_type"] == FederatedCommunicationType.PULL:
tasks_on_all_party = JobSaver.query_task(task_id=initiator_task.f_task_id, task_version=initiator_task.f_task_version)
tasks_status_on_all = set([task.f_status for task in tasks_on_all_party])
if len(tasks_status_on_all) > 1 or TaskStatus.RUNNING in tasks_status_on_all:
cls.collect_task_of_all_party(job=job, task=initiator_task)
new_task_status = cls.federated_task_status(job_id=initiator_task.f_job_id, task_id=initiator_task.f_task_id, task_version=initiator_task.f_task_version)
task_status_have_update = False
if new_task_status != initiator_task.f_status:
task_status_have_update = True
initiator_task.f_status = new_task_status
FederatedScheduler.sync_task_status(job=job, task=initiator_task)
if initiator_task.f_status == TaskStatus.WAITING:
waiting_tasks.append(initiator_task)
elif task_status_have_update and EndStatus.contains(initiator_task.f_status):
FederatedScheduler.stop_task(job=job, task=initiator_task, stop_status=initiator_task.f_status)
scheduling_status_code = SchedulingStatusCode.NO_NEXT
if not canceled:
for waiting_task in waiting_tasks:
for component in dsl_parser.get_upstream_dependent_components(component_name=waiting_task.f_component_name):
dependent_task = initiator_tasks_group[
JobSaver.task_key(task_id=job_utils.generate_task_id(job_id=job.f_job_id, component_name=component.get_name()),
role=job.f_role,
party_id=job.f_party_id
)
]
if dependent_task.f_status != TaskStatus.SUCCESS:
# can not start task
break
else:
# all upstream dependent tasks have been successful, can start this task
scheduling_status_code = SchedulingStatusCode.HAVE_NEXT
status_code = cls.start_task(job=job, task=waiting_task)
if status_code == SchedulingStatusCode.NO_RESOURCE:
# wait for the next round of scheduling
schedule_logger(job_id=job.f_job_id).info(f"job {waiting_task.f_job_id} task {waiting_task.f_task_id} can not apply resource, wait for the next round of scheduling")
break
elif status_code == SchedulingStatusCode.FAILED:
scheduling_status_code = SchedulingStatusCode.FAILED
break
else:
schedule_logger(job_id=job.f_job_id).info("have cancel signal, pass start job {} tasks".format(job.f_job_id))
schedule_logger(job_id=job.f_job_id).info("finish scheduling job {} tasks".format(job.f_job_id))
return scheduling_status_code, initiator_tasks_group.values()
def stop_job():
job_id = request.json.get('job_id')
stop_status = request.json.get("stop_status", "canceled")
jobs = JobSaver.query_job(job_id=job_id)
if jobs:
schedule_logger(job_id).info(f"stop job on this party")
kill_status, kill_details = JobController.stop_jobs(
job_id=job_id, stop_status=stop_status)
schedule_logger(job_id).info(
f"stop job on this party status {kill_status}")
schedule_logger(job_id).info(
f"request stop job {jobs[0]} to {stop_status}")
status_code, response = FederatedScheduler.request_stop_job(
job=jobs[0],
stop_status=stop_status,
command_body=jobs[0].to_json())
if status_code == FederatedSchedulingStatusCode.SUCCESS:
return get_json_result(
retcode=RetCode.SUCCESS,
retmsg=f"stop job on this party {kill_status};\n"
f"stop job on all party success")
else:
return get_json_result(retcode=RetCode.OPERATING_ERROR,
retmsg="stop job on this party {};\n"
"stop job failed:\n{}".format(
kill_status,
json_dumps(response, indent=4)))
else:
schedule_logger(job_id).info(f"can not found job {job_id} to stop")
return get_json_result(retcode=RetCode.DATA_ERROR,
retmsg="can not found job")
def request_stop_jobs(cls, jobs: [Job], stop_msg, stop_status):
if not len(jobs):
return
detect_logger().info(
f"have {len(jobs)} should be stopped, because of {stop_msg}")
for job in jobs:
try:
detect_logger(job_id=job.f_job_id).info(
f"detector request start to stop job {job.f_job_id}, because of {stop_msg}"
)
FederatedScheduler.request_stop_job(job=job,
stop_status=stop_status)
detect_logger(job_id=job.f_job_id).info(
f"detector request stop job {job.f_job_id} successfully")
except Exception as e:
detect_logger(job_id=job.f_job_id).exception(e)
def collect_task_of_all_party(cls, job, initiator_task, set_status=None):
tasks_on_all_party = JobSaver.query_task(task_id=initiator_task.f_task_id, task_version=initiator_task.f_task_version)
tasks_status_on_all = set([task.f_status for task in tasks_on_all_party])
if not len(tasks_status_on_all) > 1 and not TaskStatus.RUNNING in tasks_status_on_all:
return
status, federated_response = FederatedScheduler.collect_task(job=job, task=initiator_task)
if status != FederatedSchedulingStatusCode.SUCCESS:
schedule_logger(job_id=job.f_job_id).warning(f"collect task {initiator_task.f_task_id} {initiator_task.f_task_version} on {initiator_task.f_role} {initiator_task.f_party_id} failed")
for _role in federated_response.keys():
for _party_id, party_response in federated_response[_role].items():
if party_response["retcode"] == RetCode.SUCCESS:
JobSaver.update_task_status(task_info=party_response["data"])
JobSaver.update_task(task_info=party_response["data"])
elif party_response["retcode"] == RetCode.FEDERATED_ERROR and set_status:
tmp_task_info = {
"job_id": initiator_task.f_job_id,
"task_id": initiator_task.f_task_id,
"task_version": initiator_task.f_task_version,
"role": _role,
"party_id": _party_id,
"party_status": TaskStatus.RUNNING
}
JobSaver.update_task_status(task_info=tmp_task_info)
tmp_task_info["party_status"] = set_status
JobSaver.update_task_status(task_info=tmp_task_info)
def stop_job(cls, job_id, role, party_id, stop_status):
schedule_logger(job_id=job_id).info(
f"request stop job {job_id} with {stop_status}")
jobs = JobSaver.query_job(job_id=job_id,
role=role,
party_id=party_id,
is_initiator=True)
if len(jobs) > 0:
if stop_status == JobStatus.CANCELED:
schedule_logger(job_id=job_id).info(f"cancel job {job_id}")
set_cancel_status = cls.cancel_signal(job_id=job_id,
set_or_reset=True)
schedule_logger(job_id=job_id).info(
f"set job {job_id} cancel signal {set_cancel_status}")
job = jobs[0]
job.f_status = stop_status
schedule_logger(job_id=job_id).info(
f"request stop job {job_id} with {stop_status} to all party")
status_code, response = FederatedScheduler.stop_job(
job=jobs[0], stop_status=stop_status)
if status_code == FederatedSchedulingStatusCode.SUCCESS:
schedule_logger(job_id=job_id).info(
f"stop job {job_id} with {stop_status} successfully")
return RetCode.SUCCESS, "success"
else:
schedule_logger(job_id=job_id).info(
f"stop job {job_id} with {stop_status} failed, {response}")
return RetCode.FEDERATED_ERROR, json_dumps(response)
else:
return RetCode.SUCCESS, "can not found job"
def clean_queue():
jobs = JobSaver.query_job(is_initiator=True, status=JobStatus.WAITING)
clean_status = {}
for job in jobs:
status_code, response = FederatedScheduler.request_stop_job(
job=job, stop_status=JobStatus.CANCELED)
clean_status[job.f_job_id] = status_code
return get_json_result(retcode=0, retmsg='success', data=clean_status)
def schedule_waiting_jobs(cls, job):
job_id, initiator_role, initiator_party_id, = job.f_job_id, job.f_initiator_role, job.f_initiator_party_id,
if not cls.ready_signal(job_id=job_id, set_or_reset=True):
schedule_logger(job_id).info(f"job {job_id} may be handled by another scheduler")
return
try:
if job.f_cancel_signal:
job.f_status = JobStatus.CANCELED
FederatedScheduler.sync_job_status(job=job)
schedule_logger(job_id).info(f"job {job_id} have cancel signal")
return
apply_status_code, federated_response = FederatedScheduler.resource_for_job(job=job, operation_type=ResourceOperation.APPLY)
if apply_status_code == FederatedSchedulingStatusCode.SUCCESS:
cls.start_job(job_id=job_id, initiator_role=initiator_role, initiator_party_id=initiator_party_id)
else:
# rollback resource
rollback_party = {}
failed_party = {}
for dest_role in federated_response.keys():
for dest_party_id in federated_response[dest_role].keys():
retcode = federated_response[dest_role][dest_party_id]["retcode"]
if retcode == 0:
rollback_party[dest_role] = rollback_party.get(dest_role, [])
rollback_party[dest_role].append(dest_party_id)
else:
failed_party[dest_role] = failed_party.get(dest_role, [])
failed_party[dest_role].append(dest_party_id)
schedule_logger(job_id).info("job {} apply resource failed on {}, rollback {}".format(
job_id,
",".join([",".join([f"{_r}:{_p}" for _p in _ps]) for _r, _ps in failed_party.items()]),
",".join([",".join([f"{_r}:{_p}" for _p in _ps]) for _r, _ps in rollback_party.items()]),
))
if rollback_party:
return_status_code, federated_response = FederatedScheduler.resource_for_job(job=job, operation_type=ResourceOperation.RETURN, specific_dest=rollback_party)
if return_status_code != FederatedSchedulingStatusCode.SUCCESS:
schedule_logger(job_id).info(f"job {job_id} return resource failed:\n{federated_response}")
else:
schedule_logger(job_id).info(f"job {job_id} no party should be rollback resource")
if apply_status_code == FederatedSchedulingStatusCode.ERROR:
cls.stop_job(job_id=job_id, role=initiator_role, party_id=initiator_party_id, stop_status=JobStatus.FAILED)
schedule_logger(job_id).info(f"apply resource error, stop job {job_id}")
except Exception as e:
raise e
finally:
update_status = cls.ready_signal(job_id=job_id, set_or_reset=False)
schedule_logger(job_id).info(f"reset job {job_id} ready signal {update_status}")
def collect_task_of_all_party(cls, job, task):
status, federated_response = FederatedScheduler.collect_task(job=job, task=task)
if status != FederatedSchedulingStatusCode.SUCCESS:
schedule_logger(job_id=job.f_job_id).warning(f"collect task {task.f_task_id} {task.f_task_version} on {task.f_role} {task.f_party_id} failed")
return
for _role in federated_response.keys():
for _party_id, party_response in federated_response[_role].items():
JobSaver.update_task_status(task_info=party_response["data"])
JobSaver.update_task(task_info=party_response["data"])
def component_output_data_table():
request_data = request.json
detect_utils.check_config(config=request_data, required_arguments=['job_id', 'role', 'party_id', 'component_name'])
jobs = JobSaver.query_job(job_id=request_data.get('job_id'))
if jobs:
job = jobs[0]
return jsonify(FederatedScheduler.tracker_command(job, request_data, 'output/table'))
else:
return get_json_result(retcode=100, retmsg='No found job')
def schedule_running_job(cls, job):
schedule_logger(job_id=job.f_job_id).info("scheduling job {}".format(job.f_job_id))
dsl_parser = schedule_utils.get_job_dsl_parser(dsl=job.f_dsl,
runtime_conf=job.f_runtime_conf_on_party,
train_runtime_conf=job.f_train_runtime_conf)
task_scheduling_status_code, tasks = TaskScheduler.schedule(job=job, dsl_parser=dsl_parser, canceled=job.f_cancel_signal)
tasks_status = [task.f_status for task in tasks]
new_job_status = cls.calculate_job_status(task_scheduling_status_code=task_scheduling_status_code, tasks_status=tasks_status)
if new_job_status == JobStatus.WAITING and job.f_cancel_signal:
new_job_status = JobStatus.CANCELED
total, finished_count = cls.calculate_job_progress(tasks_status=tasks_status)
new_progress = float(finished_count) / total * 100
schedule_logger(job_id=job.f_job_id).info("Job {} status is {}, calculate by task status list: {}".format(job.f_job_id, new_job_status, tasks_status))
if new_job_status != job.f_status or new_progress != job.f_progress:
# Make sure to update separately, because these two fields update with anti-weight logic
if int(new_progress) - job.f_progress > 0:
job.f_progress = new_progress
FederatedScheduler.sync_job(job=job, update_fields=["progress"])
cls.update_job_on_initiator(initiator_job=job, update_fields=["progress"])
if new_job_status != job.f_status:
job.f_status = new_job_status
if EndStatus.contains(job.f_status):
FederatedScheduler.save_pipelined_model(job=job)
FederatedScheduler.sync_job_status(job=job)
cls.update_job_on_initiator(initiator_job=job, update_fields=["status"])
if EndStatus.contains(job.f_status):
cls.finish(job=job, end_status=job.f_status)
schedule_logger(job_id=job.f_job_id).info("finish scheduling job {}".format(job.f_job_id))
def start_task(cls, job, task):
schedule_logger(job_id=task.f_job_id).info("try to start job {} task {} {} on {} {}".format(task.f_job_id, task.f_task_id, task.f_task_version, task.f_role, task.f_party_id))
apply_status = ResourceManager.apply_for_task_resource(task_info=task.to_human_model_dict(only_primary_with=["status"]))
if not apply_status:
return SchedulingStatusCode.NO_RESOURCE
task.f_status = TaskStatus.RUNNING
update_status = JobSaver.update_task_status(task_info=task.to_human_model_dict(only_primary_with=["status"]))
if not update_status:
# Another scheduler scheduling the task
schedule_logger(job_id=task.f_job_id).info("job {} task {} {} start on another scheduler".format(task.f_job_id, task.f_task_id, task.f_task_version))
# Rollback
task.f_status = TaskStatus.WAITING
ResourceManager.return_task_resource(task_info=task.to_human_model_dict(only_primary_with=["status"]))
return SchedulingStatusCode.PASS
schedule_logger(job_id=task.f_job_id).info("start job {} task {} {} on {} {}".format(task.f_job_id, task.f_task_id, task.f_task_version, task.f_role, task.f_party_id))
FederatedScheduler.sync_task_status(job=job, task=task)
task_parameters = {}
status_code, response = FederatedScheduler.start_task(job=job, task=task, task_parameters=task_parameters)
if status_code == FederatedSchedulingStatusCode.SUCCESS:
return SchedulingStatusCode.SUCCESS
else:
return SchedulingStatusCode.FAILED
def rerun_job(cls, job_id, initiator_role, initiator_party_id, component_name):
schedule_logger(job_id=job_id).info(f"try to rerun job {job_id} on initiator {initiator_role} {initiator_party_id}")
jobs = JobSaver.query_job(job_id=job_id, role=initiator_role, party_id=initiator_party_id)
if jobs:
job = jobs[0]
else:
raise RuntimeError(f"can not found job {job_id} on initiator {initiator_role} {initiator_party_id}")
if component_name != job_utils.job_virtual_component_name():
tasks = JobSaver.query_task(job_id=job_id, role=initiator_role, party_id=initiator_party_id, component_name=component_name)
else:
tasks = JobSaver.query_task(job_id=job_id, role=initiator_role, party_id=initiator_party_id)
job_can_rerun = False
dsl_parser = schedule_utils.get_job_dsl_parser(dsl=job.f_dsl,
runtime_conf=job.f_runtime_conf_on_party,
train_runtime_conf=job.f_train_runtime_conf)
for task in tasks:
if task.f_status in {TaskStatus.WAITING, TaskStatus.SUCCESS}:
if task.f_status == TaskStatus.WAITING:
job_can_rerun = True
schedule_logger(job_id=job_id).info(f"task {task.f_task_id} {task.f_task_version} on {task.f_role} {task.f_party_id} is {task.f_status}, pass rerun")
else:
# stop old version task
FederatedScheduler.stop_task(job=job, task=task, stop_status=TaskStatus.CANCELED)
FederatedScheduler.clean_task(job=job, task=task, content_type="metrics")
# create new version task
task.f_task_version = task.f_task_version + 1
task.f_run_pid = None
task.f_run_ip = None
FederatedScheduler.create_task(job=job, task=task)
# Save the status information of all participants in the initiator for scheduling
schedule_logger(job_id=job_id).info(f"create task {task.f_task_id} new version {task.f_task_version}")
for _role, _party_ids in job.f_runtime_conf_on_party["role"].items():
for _party_id in _party_ids:
if _role == initiator_role and _party_id == initiator_party_id:
continue
JobController.initialize_tasks(job_id, _role, _party_id, False, job.f_initiator_role, job.f_initiator_party_id, RunParameters(**job.f_runtime_conf_on_party["job_parameters"]), dsl_parser, component_name=task.f_component_name, task_version=task.f_task_version)
schedule_logger(job_id=job_id).info(f"create task {task.f_task_id} new version {task.f_task_version} successfully")
job_can_rerun = True
if job_can_rerun:
schedule_logger(job_id=job_id).info(f"job {job_id} set rerun signal")
status = cls.rerun_signal(job_id=job_id, set_or_reset=True)
if status:
schedule_logger(job_id=job_id).info(f"job {job_id} set rerun signal successfully")
else:
schedule_logger(job_id=job_id).info(f"job {job_id} set rerun signal failed")
else:
FederatedScheduler.sync_job_status(job=job)
schedule_logger(job_id=job_id).info(f"job {job_id} no task to rerun")
def rerun_job():
job_id = request.json.get("job_id")
jobs = JobSaver.query_job(job_id=job_id)
if jobs:
status_code, response = FederatedScheduler.request_rerun_job(
job=jobs[0], command_body=request.json)
if status_code == FederatedSchedulingStatusCode.SUCCESS:
return get_json_result(retcode=RetCode.SUCCESS,
retmsg="rerun job success")
else:
return get_json_result(retcode=RetCode.OPERATING_ERROR,
retmsg="rerun job failed:\n{}".format(
json_dumps(response)))
else:
return get_json_result(retcode=RetCode.DATA_ERROR,
retmsg="can not found job")
def align_input_data_partitions(cls, job: Job, dsl_parser):
job_args = dsl_parser.get_args_input()
dataset = JobController.get_dataset(is_initiator=True,
role=job.f_initiator_role,
party_id=job.f_initiator_party_id,
roles=job.f_roles, job_args=job_args)
input_data_aligned_partitions = 0 # Large integers are not used
status, partys_response = FederatedScheduler.align_args(job=job, command_body=dataset)
schedule_logger(job_id=job.f_job_id).info("align partition partys response: {}".format(partys_response))
for role, party_args in partys_response.items():
for party_id, response in party_args.items():
if not response["retcode"]:
partitions = response.get('data').get('min_input_data_partition')
if partitions:
if input_data_aligned_partitions == 0 or partitions < input_data_aligned_partitions:
input_data_aligned_partitions = partitions
return input_data_aligned_partitions
def stop_job(cls, job_id, role, party_id, stop_status):
schedule_logger(job_id=job_id).info(f"request stop job {job_id}")
jobs = JobSaver.query_job(job_id=job_id, role=role, party_id=party_id, is_initiator=True)
if len(jobs) > 0:
schedule_logger(job_id=job_id).info(f"initiator cancel job {job_id}")
JobController.cancel_job(job_id=job_id, role=role, party_id=party_id)
job = jobs[0]
job.f_status = stop_status
schedule_logger(job_id=job_id).info(f"request cancel job {job_id} to all party")
status_code, response = FederatedScheduler.stop_job(job=jobs[0], stop_status=stop_status)
if status_code == FederatedSchedulingStatusCode.SUCCESS:
schedule_logger(job_id=job_id).info(f"cancel job {job_id} successfully")
return RetCode.SUCCESS, "success"
else:
schedule_logger(job_id=job_id).info(f"cancel job {job_id} failed, {response}")
return RetCode.FEDERATED_ERROR, json_dumps(response)
else:
schedule_logger(job_id=job_id).info(f"can not found job {job_id} to stop, delete event on {role} {party_id}")
JobQueue.delete_event(job_id=job_id)
return RetCode.SUCCESS, "can not found job, delete job waiting event"
def stop_job():
job_id = request.json.get('job_id')
stop_status = request.json.get("stop_status", "canceled")
jobs = JobSaver.query_job(job_id=job_id)
if jobs:
stat_logger.info(f"request stop job {jobs[0]} to {stop_status}")
status_code, response = FederatedScheduler.request_stop_job(
job=jobs[0],
stop_status=stop_status,
command_body=jobs[0].to_json())
if status_code == FederatedSchedulingStatusCode.SUCCESS:
return get_json_result(retcode=RetCode.SUCCESS,
retmsg="stop job success")
else:
return get_json_result(retcode=RetCode.OPERATING_ERROR,
retmsg="stop job failed:\n{}".format(
json_dumps(response, indent=4)))
else:
stat_logger.info(f"can not found job {jobs[0]} to stop")
return get_json_result(retcode=RetCode.DATA_ERROR,
retmsg="can not found job")
def submit(cls, job_data, job_id=None):
if not job_id:
job_id = job_utils.generate_job_id()
schedule_logger(job_id).info('submit job, job_id {}, body {}'.format(
job_id, job_data))
job_dsl = job_data.get('job_dsl', {})
job_runtime_conf = job_data.get('job_runtime_conf', {})
job_utils.check_job_runtime_conf(job_runtime_conf)
job_initiator = job_runtime_conf['initiator']
conf_adapter = JobRuntimeConfigAdapter(job_runtime_conf)
common_job_parameters = conf_adapter.get_common_parameters()
if common_job_parameters.job_type != 'predict':
# generate job model info
common_job_parameters.model_id = model_utils.gen_model_id(
job_runtime_conf['role'])
common_job_parameters.model_version = job_id
train_runtime_conf = {}
else:
# check predict job parameters
detect_utils.check_config(common_job_parameters.to_dict(),
['model_id', 'model_version'])
# get inference dsl from pipeline model as job dsl
tracker = Tracker(
job_id=job_id,
role=job_initiator['role'],
party_id=job_initiator['party_id'],
model_id=common_job_parameters.model_id,
model_version=common_job_parameters.model_version)
pipeline_model = tracker.get_output_model('pipeline')
if not job_dsl:
job_dsl = json_loads(pipeline_model['Pipeline'].inference_dsl)
train_runtime_conf = json_loads(
pipeline_model['Pipeline'].train_runtime_conf)
job = Job()
job.f_job_id = job_id
job.f_dsl = job_dsl
job.f_train_runtime_conf = train_runtime_conf
job.f_roles = job_runtime_conf['role']
job.f_work_mode = common_job_parameters.work_mode
job.f_initiator_role = job_initiator['role']
job.f_initiator_party_id = job_initiator['party_id']
path_dict = job_utils.save_job_conf(
job_id=job_id,
role=job.f_initiator_role,
job_dsl=job_dsl,
job_runtime_conf=job_runtime_conf,
job_runtime_conf_on_party={},
train_runtime_conf=train_runtime_conf,
pipeline_dsl=None)
if job.f_initiator_party_id not in job_runtime_conf['role'][
job.f_initiator_role]:
schedule_logger(job_id).info("initiator party id error:{}".format(
job.f_initiator_party_id))
raise Exception("initiator party id error {}".format(
job.f_initiator_party_id))
# create common parameters on initiator
JobController.backend_compatibility(
job_parameters=common_job_parameters)
JobController.adapt_job_parameters(
role=job.f_initiator_role,
job_parameters=common_job_parameters,
create_initiator_baseline=True)
job.f_runtime_conf = conf_adapter.update_common_parameters(
common_parameters=common_job_parameters)
dsl_parser = schedule_utils.get_job_dsl_parser(
dsl=job.f_dsl,
runtime_conf=job.f_runtime_conf,
train_runtime_conf=job.f_train_runtime_conf)
# initiator runtime conf as template
job.f_runtime_conf_on_party = job.f_runtime_conf.copy()
job.f_runtime_conf_on_party[
"job_parameters"] = common_job_parameters.to_dict()
if common_job_parameters.work_mode == WorkMode.CLUSTER:
# Save the status information of all participants in the initiator for scheduling
for role, party_ids in job.f_roles.items():
for party_id in party_ids:
if role == job.f_initiator_role and party_id == job.f_initiator_party_id:
continue
JobController.initialize_tasks(job_id, role, party_id,
False, job.f_initiator_role,
job.f_initiator_party_id,
common_job_parameters,
dsl_parser)
status_code, response = FederatedScheduler.create_job(job=job)
if status_code != FederatedSchedulingStatusCode.SUCCESS:
job.f_status = JobStatus.FAILED
job.f_tag = "submit_failed"
FederatedScheduler.sync_job_status(job=job)
raise Exception("create job failed", response)
schedule_logger(job_id).info(
'submit job successfully, job id is {}, model id is {}'.format(
job.f_job_id, common_job_parameters.model_id))
board_url = "http://{}:{}{}".format(
ServiceUtils.get_item("fateboard", "host"),
ServiceUtils.get_item("fateboard", "port"),
FATE_BOARD_DASHBOARD_ENDPOINT).format(job_id,
job_initiator['role'],
job_initiator['party_id'])
logs_directory = job_utils.get_job_log_directory(job_id)
submit_result = {
"job_id": job_id,
"model_info": {
"model_id": common_job_parameters.model_id,
"model_version": common_job_parameters.model_version
},
"logs_directory": logs_directory,
"board_url": board_url
}
submit_result.update(path_dict)
return submit_result
def submit(cls, job_data, job_id=None):
if not job_id:
job_id = job_utils.generate_job_id()
schedule_logger(job_id).info('submit job, job_id {}, body {}'.format(job_id, job_data))
job_dsl = job_data.get('job_dsl', {})
job_runtime_conf = job_data.get('job_runtime_conf', {})
job_initiator = job_runtime_conf['initiator']
job_parameters = RunParameters(**job_runtime_conf['job_parameters'])
cls.backend_compatibility(job_parameters=job_parameters)
job_utils.check_job_runtime_conf(job_runtime_conf)
if job_parameters.job_type != 'predict':
# generate job model info
job_parameters.model_id = model_utils.gen_model_id(job_runtime_conf['role'])
job_parameters.model_version = job_id
train_runtime_conf = {}
else:
detect_utils.check_config(job_parameters.to_dict(), ['model_id', 'model_version'])
# get inference dsl from pipeline model as job dsl
tracker = Tracker(job_id=job_id, role=job_initiator['role'], party_id=job_initiator['party_id'],
model_id=job_parameters.model_id, model_version=job_parameters.model_version)
pipeline_model = tracker.get_output_model('pipeline')
if not job_dsl:
job_dsl = json_loads(pipeline_model['Pipeline'].inference_dsl)
train_runtime_conf = json_loads(pipeline_model['Pipeline'].train_runtime_conf)
path_dict = job_utils.save_job_conf(job_id=job_id,
job_dsl=job_dsl,
job_runtime_conf=job_runtime_conf,
train_runtime_conf=train_runtime_conf,
pipeline_dsl=None)
job = Job()
job.f_job_id = job_id
job.f_dsl = job_dsl
job_runtime_conf["job_parameters"] = job_parameters.to_dict()
job.f_runtime_conf = job_runtime_conf
job.f_train_runtime_conf = train_runtime_conf
job.f_roles = job_runtime_conf['role']
job.f_work_mode = job_parameters.work_mode
job.f_initiator_role = job_initiator['role']
job.f_initiator_party_id = job_initiator['party_id']
initiator_role = job_initiator['role']
initiator_party_id = job_initiator['party_id']
if initiator_party_id not in job_runtime_conf['role'][initiator_role]:
schedule_logger(job_id).info("initiator party id error:{}".format(initiator_party_id))
raise Exception("initiator party id error {}".format(initiator_party_id))
dsl_parser = schedule_utils.get_job_dsl_parser(dsl=job_dsl,
runtime_conf=job_runtime_conf,
train_runtime_conf=train_runtime_conf)
cls.adapt_job_parameters(job_parameters=job_parameters)
# update runtime conf
job_runtime_conf["job_parameters"] = job_parameters.to_dict()
job.f_runtime_conf = job_runtime_conf
status_code, response = FederatedScheduler.create_job(job=job)
if status_code != FederatedSchedulingStatusCode.SUCCESS:
raise Exception("create job failed: {}".format(response))
if job_parameters.work_mode == WorkMode.CLUSTER:
# Save the status information of all participants in the initiator for scheduling
for role, party_ids in job_runtime_conf["role"].items():
for party_id in party_ids:
if role == job_initiator['role'] and party_id == job_initiator['party_id']:
continue
JobController.initialize_tasks(job_id, role, party_id, False, job_initiator, job_parameters, dsl_parser)
# push into queue
try:
JobQueue.create_event(job_id=job_id, initiator_role=initiator_role, initiator_party_id=initiator_party_id)
except Exception as e:
raise Exception(f'push job into queue failed:\n{e}')
schedule_logger(job_id).info(
'submit job successfully, job id is {}, model id is {}'.format(job.f_job_id, job_parameters.model_id))
board_url = "http://{}:{}{}".format(
ServiceUtils.get_item("fateboard", "host"),
ServiceUtils.get_item("fateboard", "port"),
FATE_BOARD_DASHBOARD_ENDPOINT).format(job_id, job_initiator['role'], job_initiator['party_id'])
logs_directory = job_utils.get_job_log_directory(job_id)
return job_id, path_dict['job_dsl_path'], path_dict['job_runtime_conf_path'], logs_directory, \
{'model_id': job_parameters.model_id, 'model_version': job_parameters.model_version}, board_url
def finish(cls, job, end_status):
schedule_logger(job_id=job.f_job_id).info("Job {} finished with {}, do something...".format(job.f_job_id, end_status))
FederatedScheduler.stop_job(job=job, stop_status=end_status)
FederatedScheduler.clean_job(job=job)
JobQueue.delete_event(job_id=job.f_job_id)
schedule_logger(job_id=job.f_job_id).info("Job {} finished with {}, done".format(job.f_job_id, end_status))
