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))