def __init__(
self,
servername: str,
host: str = '',
http_port: int = 8080,
grpc_port: int = 8081,
workers: int = 1,
ws_size: int = 16 *
1024 *
1024,
ssl_key=None,
ssl_cert=None,
timeout=300):
self.server_name = servername
self.app = None
self.host = host or get_host_ip()
self.http_port = http_port or 80
self.grpc_port = grpc_port
self.workers = workers
self.keyfile = ssl_key
self.certfile = ssl_cert
self.ws_size = int(ws_size)
self.timeout = int(timeout)
protocal = "https" if self.certfile else "http"
self.url = f"{protocal}://{self.host}:{self.http_port}"
def __init__(self, estimator=None):
"""
Initial a big model service for JointInference
:param estimator: Customize estimator
"""
super(BigModelService, self).__init__(estimator=estimator)
self.local_ip = self.get_parameters("BIG_MODEL_BIND_IP", get_host_ip())
self.port = int(self.get_parameters("BIG_MODEL_BIND_PORT", "5000"))
def __init__(self, estimator=None, hard_example_mining: dict = None):
super(JointInference, self).__init__(estimator=estimator)
self.job_kind = K8sResourceKind.JOINT_INFERENCE_SERVICE.value
self.local_ip = get_host_ip()
self.remote_ip = self.get_parameters("BIG_MODEL_IP", self.local_ip)
self.port = int(self.get_parameters("BIG_MODEL_PORT", "5000"))
report_msg = {
"name": self.worker_name,
"namespace": self.config.namespace,
"ownerName": self.job_name,
"ownerKind": self.job_kind,
"kind": "inference",
"results": []
}
period_interval = int(self.get_parameters("LC_PERIOD", "30"))
self.lc_reporter = LCReporter(lc_server=self.config.lc_server,
message=report_msg,
period_interval=period_interval)
self.lc_reporter.setDaemon(True)
self.lc_reporter.start()
if callable(self.estimator):
self.estimator = self.estimator()
if not os.path.exists(self.model_path):
raise FileExistsError(f"{self.model_path} miss")
else:
self.estimator.load(self.model_path)
self.cloud = ModelClient(service_name=self.job_name,
host=self.remote_ip,
port=self.port)
self.hard_example_mining_algorithm = None
if not hard_example_mining:
hard_example_mining = self.get_hem_algorithm_from_config()
if hard_example_mining:
hem = hard_example_mining.get("method", "IBT")
hem_parameters = hard_example_mining.get("param", {})
self.hard_example_mining_algorithm = ClassFactory.get_cls(
ClassType.HEM, hem)(**hem_parameters)
def __init__(
self,
aggregation: str,
host: str = None,
http_port: int = None,
exit_round: int = 1,
participants_count: int = 1,
ws_size: int = 10 * 1024 * 1024):
if not host:
host = Context.get_parameters("AGG_BIND_IP", get_host_ip())
if not http_port:
http_port = int(Context.get_parameters("AGG_BIND_PORT", 7363))
super(
AggregationServer,
self).__init__(
servername=aggregation,
host=host,
http_port=http_port,
ws_size=ws_size)
self.aggregation = aggregation
self.participants_count = participants_count
self.exit_round = max(int(exit_round), 1)
self.app = FastAPI(
routes=[
APIRoute(
f"/{aggregation}",
self.client_info,
response_class=JSONResponse,
),
WebSocketRoute(
f"/{aggregation}",
BroadcastWs
)
],
)
self.app.shutdown = False