def resource_for_task(cls, task_info, operation_type):
cores_per_task, memory_per_task = cls.calculate_task_resource(
task_info=task_info)
if cores_per_task or memory_per_task:
filters, updates = cls.update_resource_sql(
resource_model=Job,
cores=cores_per_task,
memory=memory_per_task,
operation_type=operation_type,
)
filters.append(Job.f_job_id == task_info["job_id"])
filters.append(Job.f_role == task_info["role"])
filters.append(Job.f_party_id == task_info["party_id"])
filters.append(Job.f_resource_in_use == True)
operate = Job.update(updates).where(*filters)
operate_status = operate.execute() > 0
else:
operate_status = True
if operate_status:
schedule_logger(job_id=task_info["job_id"]).info(
"task {} {} {} resource successfully".format(
task_info["task_id"], task_info["task_version"],
operation_type))
else:
schedule_logger(job_id=task_info["job_id"]).warning(
"task {} {} {} resource failed".format(
task_info["task_id"], task_info["task_version"],
operation_type))
return operate_status
def kill_task_executor_process(task: Task, only_child=False):
try:
if not task.f_run_pid:
schedule_logger(task.f_job_id).info("job {} task {} {} {} with {} party status no process pid".format(
task.f_job_id, task.f_task_id, task.f_role, task.f_party_id, task.f_party_status))
return KillProcessStatusCode.NOT_FOUND
pid = int(task.f_run_pid)
schedule_logger(task.f_job_id).info("try to stop job {} task {} {} {} with {} party status process pid:{}".format(
task.f_job_id, task.f_task_id, task.f_role, task.f_party_id, task.f_party_status, pid))
if not check_job_process(pid):
schedule_logger(task.f_job_id).info("can not found job {} task {} {} {} with {} party status process pid:{}".format(
task.f_job_id, task.f_task_id, task.f_role, task.f_party_id, task.f_party_status, pid))
return KillProcessStatusCode.NOT_FOUND
p = psutil.Process(int(pid))
if not is_task_executor_process(task=task, process=p):
schedule_logger(task.f_job_id).warning("this pid {} is not job {} task {} {} {} executor".format(
pid, task.f_job_id, task.f_task_id, task.f_role, task.f_party_id))
return KillProcessStatusCode.ERROR_PID
for child in p.children(recursive=True):
if check_job_process(child.pid) and is_task_executor_process(task=task, process=child):
child.kill()
if not only_child:
if check_job_process(p.pid) and is_task_executor_process(task=task, process=p):
p.kill()
schedule_logger(task.f_job_id).info("successfully stop job {} task {} {} {} process pid:{}".format(
task.f_job_id, task.f_task_id, task.f_role, task.f_party_id, pid))
return KillProcessStatusCode.KILLED
except Exception as e:
raise e
def report_task_to_initiator(cls, task: Task):
"""
:param task:
:return:
"""
if task.f_role != task.f_initiator_role and task.f_party_id != task.f_initiator_party_id:
exception = None
for t in range(DEFAULT_FEDERATED_COMMAND_TRYS):
try:
response = federated_api(
job_id=task.f_job_id,
method='POST',
endpoint='/initiator/{}/{}/{}/{}/{}/{}/report'.format(
task.f_job_id, task.f_component_name,
task.f_task_id, task.f_task_version, task.f_role,
task.f_party_id),
src_party_id=task.f_party_id,
dest_party_id=task.f_initiator_party_id,
src_role=task.f_role,
json_body=task.to_human_model_dict(
only_primary_with=cls.REPORT_TO_INITIATOR_FIELDS),
federated_mode=task.f_federated_mode)
except Exception as e:
exception = e
continue
if response["retcode"] != RetCode.SUCCESS:
exception = Exception(response["retmsg"])
else:
return True
else:
schedule_logger(job_id=task.f_job_id).error(
f"report task to initiator error: {exception}")
return False
else:
return False
def local_api(job_id,
method,
endpoint,
json_body,
api_version=API_VERSION,
try_times=3):
endpoint = f"/{api_version}{endpoint}"
exception = None
for t in range(try_times):
try:
url = "http://{}:{}{}".format(RuntimeConfig.JOB_SERVER_HOST,
RuntimeConfig.HTTP_PORT, endpoint)
audit_logger(job_id).info('local api request: {}'.format(url))
action = getattr(requests, method.lower(), None)
http_response = action(url=url,
data=json_dumps(json_body),
headers=HEADERS)
audit_logger(job_id).info(http_response.text)
response = http_response.json()
audit_logger(job_id).info('local api response: {} {}'.format(
endpoint, response))
return response
except Exception as e:
schedule_logger(job_id).exception(e)
exception = e
else:
raise Exception('local request error: {}'.format(exception))
def run_subprocess(job_id, config_dir, process_cmd, log_dir=None):
schedule_logger(job_id=job_id).info('start process command: {}'.format(
' '.join(process_cmd)))
os.makedirs(config_dir, exist_ok=True)
if log_dir:
os.makedirs(log_dir, exist_ok=True)
std_log = open(os.path.join(log_dir if log_dir else config_dir, 'std.log'),
'w')
pid_path = os.path.join(config_dir, 'pid')
if os.name == 'nt':
startupinfo = subprocess.STARTUPINFO()
startupinfo.dwFlags |= subprocess.STARTF_USESHOWWINDOW
startupinfo.wShowWindow = subprocess.SW_HIDE
else:
startupinfo = None
p = subprocess.Popen(process_cmd,
stdout=std_log,
stderr=std_log,
startupinfo=startupinfo)
with open(pid_path, 'w') as f:
f.truncate()
f.write(str(p.pid) + "\n")
f.flush()
schedule_logger(job_id=job_id).info(
'start process command: {} successfully, pid is {}'.format(
' '.join(process_cmd), p.pid))
return p
def start_session_stop(task):
job_parameters = RunParameters(**get_job_parameters(
job_id=task.f_job_id, role=task.f_role, party_id=task.f_party_id))
computing_session_id = generate_session_id(task.f_task_id,
task.f_task_version,
task.f_role, task.f_party_id)
if task.f_status != TaskStatus.WAITING:
schedule_logger(task.f_job_id).info(
f'start run subprocess to stop task session {computing_session_id}'
)
else:
schedule_logger(task.f_job_id).info(
f'task is waiting, pass stop session {computing_session_id}')
return
task_dir = os.path.join(get_job_directory(job_id=task.f_job_id),
task.f_role, task.f_party_id,
task.f_component_name, 'session_stop')
os.makedirs(task_dir, exist_ok=True)
process_cmd = [
'python3', sys.modules[session_utils.SessionStop.__module__].__file__,
'-j', computing_session_id, '--computing',
job_parameters.computing_engine, '--federation',
job_parameters.federation_engine, '--storage',
job_parameters.storage_engine, '-c',
'stop' if task.f_status == JobStatus.SUCCESS else 'kill'
]
p = run_subprocess(job_id=task.f_job_id,
config_dir=task_dir,
process_cmd=process_cmd,
log_dir=None)
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 insert_metrics_into_db(self,
metric_namespace: str,
metric_name: str,
data_type: int,
kv,
job_level=False):
try:
tracking_metric = self.get_dynamic_db_model(
TrackingMetric, self.job_id)()
tracking_metric.f_job_id = self.job_id
tracking_metric.f_component_name = (
self.component_name
if not job_level else job_utils.job_virtual_component_name())
tracking_metric.f_task_id = self.task_id
tracking_metric.f_task_version = self.task_version
tracking_metric.f_role = self.role
tracking_metric.f_party_id = self.party_id
tracking_metric.f_metric_namespace = metric_namespace
tracking_metric.f_metric_name = metric_name
tracking_metric.f_type = data_type
default_db_source = tracking_metric.to_json()
tracking_metric_data_source = []
for k, v in kv:
db_source = default_db_source.copy()
db_source['f_key'] = serialize_b64(k)
db_source['f_value'] = serialize_b64(v)
db_source['f_create_time'] = current_timestamp()
tracking_metric_data_source.append(db_source)
self.bulk_insert_into_db(
self.get_dynamic_db_model(TrackingMetric, self.job_id),
tracking_metric_data_source)
except Exception as e:
schedule_logger(self.job_id).exception(
"An exception where inserted metric {} of metric namespace: {} to database:\n{}"
.format(metric_name, metric_namespace, e))
def get_output_data_table(self, output_data_infos, tracker_client=None):
"""
Get component output data table, will run in the task executor process
:param output_data_infos:
:return:
"""
output_tables_meta = {}
if output_data_infos:
for output_data_info in output_data_infos:
schedule_logger(self.job_id).info(
"Get task {} {} output table {} {}".format(
output_data_info.f_task_id,
output_data_info.f_task_version,
output_data_info.f_table_namespace,
output_data_info.f_table_name))
if not tracker_client:
data_table_meta = storage.StorageTableMeta(
name=output_data_info.f_table_name,
namespace=output_data_info.f_table_namespace)
else:
data_table_meta = tracker_client.get_table_meta(
output_data_info.f_table_name,
output_data_info.f_table_namespace)
output_tables_meta[
output_data_info.f_data_name] = data_table_meta
return output_tables_meta
def query_output_data_infos(cls, **kwargs):
try:
tracking_output_data_info_model = cls.get_dynamic_db_model(
TrackingOutputDataInfo, kwargs.get("job_id"))
filters = []
for f_n, f_v in kwargs.items():
attr_name = 'f_%s' % f_n
if hasattr(tracking_output_data_info_model, attr_name):
filters.append(
operator.attrgetter('f_%s' % f_n)(
tracking_output_data_info_model) == f_v)
if filters:
output_data_infos_tmp = tracking_output_data_info_model.select(
).where(*filters)
else:
output_data_infos_tmp = tracking_output_data_info_model.select(
)
output_data_infos_group = {}
# Only the latest version of the task output data is retrieved
for output_data_info in output_data_infos_tmp:
group_key = cls.get_output_data_group_key(
output_data_info.f_task_id, output_data_info.f_data_name)
if group_key not in output_data_infos_group:
output_data_infos_group[group_key] = output_data_info
elif output_data_info.f_task_version > output_data_infos_group[
group_key].f_task_version:
output_data_infos_group[group_key] = output_data_info
return output_data_infos_group.values()
except Exception as e:
schedule_logger(kwargs.get("job_id")).exception(e)
return []
def insert_summary_into_db(self, summary_data: dict):
try:
summary_model = self.get_dynamic_db_model(ComponentSummary,
self.job_id)
DB.create_tables([summary_model])
summary_obj = summary_model.get_or_none(
summary_model.f_job_id == self.job_id,
summary_model.f_component_name == self.component_name,
summary_model.f_role == self.role,
summary_model.f_party_id == self.party_id,
summary_model.f_task_id == self.task_id,
summary_model.f_task_version == self.task_version)
if summary_obj:
summary_obj.f_summary = serialize_b64(summary_data,
to_str=True)
summary_obj.f_update_time = current_timestamp()
summary_obj.save()
else:
self.get_dynamic_db_model(
ComponentSummary,
self.job_id).create(f_job_id=self.job_id,
f_component_name=self.component_name,
f_role=self.role,
f_party_id=self.party_id,
f_task_id=self.task_id,
f_task_version=self.task_version,
f_summary=serialize_b64(summary_data,
to_str=True),
f_create_time=current_timestamp())
except Exception as e:
schedule_logger(self.job_id).exception(
"An exception where querying summary job id: {} "
"component name: {} to database:\n{}".format(
self.job_id, self.component_name, e))
def read_metrics_from_db(self,
metric_namespace: str,
metric_name: str,
data_type,
job_level=False):
metrics = []
try:
tracking_metric_model = self.get_dynamic_db_model(
TrackingMetric, self.job_id)
tracking_metrics = tracking_metric_model.select(
tracking_metric_model.f_key,
tracking_metric_model.f_value).where(
tracking_metric_model.f_job_id == self.job_id,
tracking_metric_model.f_component_name == (
self.component_name if not job_level else
job_utils.job_virtual_component_name()),
tracking_metric_model.f_role == self.role,
tracking_metric_model.f_party_id == self.party_id,
tracking_metric_model.f_metric_namespace ==
metric_namespace,
tracking_metric_model.f_metric_name == metric_name,
tracking_metric_model.f_type == data_type)
for tracking_metric in tracking_metrics:
yield deserialize_b64(tracking_metric.f_key), deserialize_b64(
tracking_metric.f_value)
except Exception as e:
schedule_logger(self.job_id).exception(e)
raise e
return metrics
def save_pipelined_model(cls, job_id, role, party_id):
schedule_logger(job_id).info(
'job {} on {} {} start to save pipeline'.format(
job_id, role, party_id))
job_dsl, job_runtime_conf, train_runtime_conf = job_utils.get_job_configuration(
job_id=job_id, role=role, party_id=party_id)
job_parameters = job_runtime_conf.get('job_parameters', {})
model_id = job_parameters['model_id']
model_version = job_parameters['model_version']
job_type = job_parameters.get('job_type', '')
if job_type == 'predict':
return
dag = schedule_utils.get_job_dsl_parser(
dsl=job_dsl,
runtime_conf=job_runtime_conf,
train_runtime_conf=train_runtime_conf)
predict_dsl = dag.get_predict_dsl(role=role)
pipeline = pipeline_pb2.Pipeline()
pipeline.inference_dsl = json_dumps(predict_dsl, byte=True)
pipeline.train_dsl = json_dumps(job_dsl, byte=True)
pipeline.train_runtime_conf = json_dumps(job_runtime_conf, byte=True)
pipeline.fate_version = RuntimeConfig.get_env("FATE")
pipeline.model_id = model_id
pipeline.model_version = model_version
tracker = Tracker(job_id=job_id,
role=role,
party_id=party_id,
model_id=model_id,
model_version=model_version)
tracker.save_pipelined_model(pipelined_buffer_object=pipeline)
if role != 'local':
tracker.save_machine_learning_model_info()
schedule_logger(job_id).info(
'job {} on {} {} save pipeline successfully'.format(
job_id, role, party_id))
def federated_coordination_on_grpc(job_id, method, host, port, endpoint, src_party_id, src_role, dest_party_id, json_body, api_version=API_VERSION,
overall_timeout=DEFAULT_REMOTE_REQUEST_TIMEOUT, try_times=3):
endpoint = f"/{api_version}{endpoint}"
json_body['src_role'] = src_role
json_body['src_party_id'] = src_party_id
if CHECK_NODES_IDENTITY:
get_node_identity(json_body, src_party_id)
_packet = wrap_grpc_packet(json_body, method, endpoint, src_party_id, dest_party_id, job_id,
overall_timeout=overall_timeout)
_routing_metadata = gen_routing_metadata(src_party_id=src_party_id, dest_party_id=dest_party_id)
exception = None
for t in range(try_times):
try:
channel, stub = get_command_federation_channel(host, port)
_return, _call = stub.unaryCall.with_call(_packet, metadata=_routing_metadata, timeout=(overall_timeout/1000))
audit_logger(job_id).info("grpc api response: {}".format(_return))
channel.close()
response = json_loads(_return.body.value)
return response
except Exception as e:
exception = e
schedule_logger(job_id).warning(f"remote request {endpoint} error, sleep and try again")
time.sleep(2 * (t+1))
else:
tips = 'Please check rollSite and fateflow network connectivity'
"""
if 'Error received from peer' in str(exception):
tips = 'Please check if the fate flow server of the other party is started. '
if 'failed to connect to all addresses' in str(exception):
tips = 'Please check whether the rollsite service(port: 9370) is started. '
"""
raise Exception('{}rpc request error: {}'.format(tips, exception))
def schedule_ready_job(cls, job):
job_id, initiator_role, initiator_party_id, = job.f_job_id, job.f_initiator_role, job.f_initiator_party_id
update_status = cls.ready_signal(job_id=job_id,
set_or_reset=False,
ready_timeout_ttl=60 * 1000)
schedule_logger(job_id).info(
f"reset job {job_id} ready signal {update_status}")
def schedule_ready_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.WAITING,
ttl=5*60*1000)
schedule_logger(job_id).info(f"update job {job_id} ready status to waiting {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 check_job_is_timeout(job: Job):
job_parameters = job.f_runtime_conf_on_party["job_parameters"]
timeout = job_parameters.get("timeout", JOB_DEFAULT_TIMEOUT)
now_time = current_timestamp()
running_time = (now_time - job.f_create_time)/1000
if running_time > timeout:
schedule_logger(job_id=job.f_job_id).info('job {} run time {}s timeout'.format(job.f_job_id, running_time))
return True
else:
return False
def get_remaining_resource(cls, resource_model: typing.Union[EngineRegistry, Job], filters):
remaining_cores, remaining_memory = None, None
try:
objs = resource_model.select(resource_model.f_remaining_cores, resource_model.f_remaining_memory).where(
*filters)
if objs:
remaining_cores, remaining_memory = objs[0].f_remaining_cores, objs[0].f_remaining_memory
except Exception as e:
schedule_logger().exception(e)
finally:
return remaining_cores, remaining_memory
def job_command(cls,
job,
command,
command_body=None,
dest_only_initiator=False,
specific_dest=None,
order_federated=False):
federated_response = {}
job_parameters = job.f_runtime_conf_on_party["job_parameters"]
if dest_only_initiator:
dest_partys = [(job.f_initiator_role, [job.f_initiator_party_id])]
api_type = "initiator"
elif specific_dest:
dest_partys = specific_dest.items()
api_type = "party"
else:
dest_partys = job.f_roles.items()
api_type = "party"
if order_federated:
dest_partys = schedule_utils.federated_order_reset(
dest_partys,
scheduler_partys_info=[(job.f_initiator_role,
job.f_initiator_party_id)])
for dest_role, dest_party_ids in dest_partys:
federated_response[dest_role] = {}
for dest_party_id in dest_party_ids:
try:
response = federated_api(
job_id=job.f_job_id,
method='POST',
endpoint='/{}/{}/{}/{}/{}'.format(
api_type, job.f_job_id, dest_role, dest_party_id,
command),
src_party_id=job.f_initiator_party_id,
dest_party_id=dest_party_id,
src_role=job.f_initiator_role,
json_body=command_body if command_body else {},
federated_mode=job_parameters["federated_mode"])
federated_response[dest_role][dest_party_id] = response
except Exception as e:
schedule_logger(job_id=job.f_job_id).exception(e)
federated_response[dest_role][dest_party_id] = {
"retcode": RetCode.FEDERATED_ERROR,
"retmsg": "Federated schedule error, {}".format(e)
}
if federated_response[dest_role][dest_party_id]["retcode"]:
schedule_logger(job_id=job.f_job_id).warning(
"an error occurred while {} the job to role {} party {}: \n{}"
.format(
command, dest_role, dest_party_id,
federated_response[dest_role][dest_party_id]
["retmsg"]))
return cls.return_federated_response(
federated_response=federated_response)
def federated_task_status(cls, job_id, task_id, task_version):
tasks_on_all_party = JobSaver.query_task(task_id=task_id,
task_version=task_version)
tasks_party_status = [
task.f_party_status for task in tasks_on_all_party
]
status = cls.calculate_multi_party_task_status(tasks_party_status)
schedule_logger(job_id=job_id).info(
"job {} task {} {} status is {}, calculate by task party status list: {}"
.format(job_id, task_id, task_version, status, tasks_party_status))
return status
def save_metric_meta(self,
metric_namespace: str,
metric_name: str,
metric_meta: MetricMeta,
job_level: bool = False):
schedule_logger(self.job_id).info(
'save job {} component {} on {} {} {} {} metric meta'.format(
self.job_id, self.component_name, self.role, self.party_id,
metric_namespace, metric_name))
self.insert_metrics_into_db(metric_namespace, metric_name, 0,
metric_meta.to_dict().items(), job_level)
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 save_pipelined_model(cls, job_id, role, party_id):
schedule_logger(job_id).info(
'job {} on {} {} start to save pipeline'.format(
job_id, role, party_id))
job_dsl, job_runtime_conf, runtime_conf_on_party, train_runtime_conf = job_utils.get_job_configuration(
job_id=job_id, role=role, party_id=party_id)
job_parameters = runtime_conf_on_party.get('job_parameters', {})
if role in job_parameters.get("assistant_role", []):
return
model_id = job_parameters['model_id']
model_version = job_parameters['model_version']
job_type = job_parameters.get('job_type', '')
work_mode = job_parameters['work_mode']
roles = runtime_conf_on_party['role']
initiator_role = runtime_conf_on_party['initiator']['role']
initiator_party_id = runtime_conf_on_party['initiator']['party_id']
if job_type == 'predict':
return
dag = schedule_utils.get_job_dsl_parser(
dsl=job_dsl,
runtime_conf=job_runtime_conf,
train_runtime_conf=train_runtime_conf)
predict_dsl = dag.get_predict_dsl(role=role)
pipeline = pipeline_pb2.Pipeline()
pipeline.inference_dsl = json_dumps(predict_dsl, byte=True)
pipeline.train_dsl = json_dumps(job_dsl, byte=True)
pipeline.train_runtime_conf = json_dumps(job_runtime_conf, byte=True)
pipeline.fate_version = RuntimeConfig.get_env("FATE")
pipeline.model_id = model_id
pipeline.model_version = model_version
pipeline.parent = True
pipeline.loaded_times = 0
pipeline.roles = json_dumps(roles, byte=True)
pipeline.work_mode = work_mode
pipeline.initiator_role = initiator_role
pipeline.initiator_party_id = initiator_party_id
pipeline.runtime_conf_on_party = json_dumps(runtime_conf_on_party,
byte=True)
pipeline.parent_info = json_dumps({}, byte=True)
tracker = Tracker(job_id=job_id,
role=role,
party_id=party_id,
model_id=model_id,
model_version=model_version)
tracker.save_pipelined_model(pipelined_buffer_object=pipeline)
if role != 'local':
tracker.save_machine_learning_model_info()
schedule_logger(job_id).info(
'job {} on {} {} save pipeline successfully'.format(
job_id, role, party_id))
def report_task_update_to_driver(cls, task_info):
"""
Report task update to FATEFlow Server
:param task_info:
:return:
"""
schedule_logger().info("report task {} {} {} {} to driver".format(
task_info["task_id"],
task_info["task_version"],
task_info["role"],
task_info["party_id"],
))
ControllerClient.report_task(task_info=task_info)
def get_table_meta(self, table_info):
schedule_logger(self.job_id).info(f'start get table meta:{table_info}')
table_meta_dict = storage.StorageTableMeta(
namespace=table_info.get("namespace"),
name=table_info.get("table_name"),
create_address=False).to_dict()
schedule_logger(
self.job_id).info(f'get table meta success: {table_meta_dict}')
table_meta_dict["part_of_data"] = serialize_b64(
table_meta_dict["part_of_data"], to_str=True)
table_meta_dict["schema"] = serialize_b64(table_meta_dict["schema"],
to_str=True)
return table_meta_dict
def save_metric_data(self,
metric_namespace: str,
metric_name: str,
metrics: List[Metric],
job_level=False):
schedule_logger(self.job_id).info(
'save job {} component {} on {} {} {} {} metric data'.format(
self.job_id, self.component_name, self.role, self.party_id,
metric_namespace, metric_name))
kv = []
for metric in metrics:
kv.append((metric.key, metric.value))
self.insert_metrics_into_db(metric_namespace, metric_name, 1, kv,
job_level)
def bulk_insert_into_db(self, model, data_source):
try:
try:
DB.create_tables([model])
except Exception as e:
schedule_logger(self.job_id).exception(e)
batch_size = 50 if RuntimeConfig.USE_LOCAL_DATABASE else 1000
for i in range(0, len(data_source), batch_size):
with DB.atomic():
model.insert_many(data_source[i:i + batch_size]).execute()
return len(data_source)
except Exception as e:
schedule_logger(self.job_id).exception(e)
return 0
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 update_task(cls, task_info):
"""
Save to local database and then report to Initiator
:param task_info:
:return:
"""
update_status = False
try:
update_status = JobSaver.update_task(task_info=task_info)
cls.report_task_to_initiator(task_info=task_info)
except Exception as e:
schedule_logger(job_id=task_info["job_id"]).exception(e)
finally:
return update_status