def __init__(self, estimator, config=None):
self.config = BaseConfig()
if config:
self.config.from_json(config)
self.log = LOGGER
self.estimator = set_backend(estimator=estimator, config=self.config)
self.job_kind = K8sResourceKind.DEFAULT.value
self.job_name = self.config.job_name or self.config.service_name
self.worker_name = self.config.worker_name or self.job_name
self.namespace = self.config.namespace or self.job_name
self.lc_server = self.config.lc_server
if str(self.get_parameters("MODEL_HOT_UPDATE",
"False")).lower() == "true":
ModelLoadingThread(self.estimator, self.report_task_info).start()
def set_backend(estimator=None, config=None):
"""Create Trainer class"""
if estimator is None:
return
if config is None:
config = BaseConfig()
use_cuda = False
backend_type = os.getenv('BACKEND_TYPE',
config.get("backend_type", "UNKNOWN"))
backend_type = str(backend_type).upper()
device_category = os.getenv('DEVICE_CATEGORY',
config.get("device_category", "CPU"))
if 'CUDA_VISIBLE_DEVICES' in os.environ:
os.environ['DEVICE_CATEGORY'] = 'GPU'
use_cuda = True
else:
os.environ['DEVICE_CATEGORY'] = device_category
if backend_type == "TENSORFLOW":
from sedna.backend.tensorflow import TFBackend as REGISTER
elif backend_type == "KERAS":
from sedna.backend.tensorflow import KerasBackend as REGISTER
else:
warnings.warn(f"{backend_type} Not Support yet, use itself")
from sedna.backend.base import BackendBase as REGISTER
model_save_url = config.get("model_url")
base_model_save = config.get("base_model_url") or model_save_url
model_save_name = config.get("model_name")
return REGISTER(estimator=estimator,
use_cuda=use_cuda,
model_save_path=base_model_save,
model_name=model_save_name,
model_save_url=model_save_url)
def __init__(self):
BaseConfig.__init__(self)
def __init__(self):
BaseConfig.__init__(self)
self.bind_ip = os.getenv("BIG_MODEL_BIND_IP", "0.0.0.0")
self.bind_port = (
int(os.getenv("BIG_MODEL_BIND_PORT", "5000"))
)
def __init__(self):
BaseConfig.__init__(self)
self.agg_ip = os.getenv("AGG_IP", "0.0.0.0")
self.agg_port = int(os.getenv("AGG_PORT", "7363"))
def __init__(self):
BaseConfig.__init__(self)
self.model_urls = os.getenv("MODEL_URLS")
self.base_model_url = os.getenv("BASE_MODEL_URL")
self.saved_model_name = "model.pb"
def __init__(self):
BaseConfig.__init__(self)
self.bind_ip = os.getenv("AGG_BIND_IP", "0.0.0.0")
self.bind_port = int(os.getenv("AGG_BIND_PORT", "7363"))
self.participants_count = int(os.getenv("PARTICIPANTS_COUNT", "1"))
class JobBase:
""" sedna feature base class """
parameters = Context
def __init__(self, estimator, config=None):
self.config = BaseConfig()
if config:
self.config.from_json(config)
self.log = LOGGER
self.estimator = set_backend(estimator=estimator, config=self.config)
self.job_kind = K8sResourceKind.DEFAULT.value
self.job_name = self.config.job_name or self.config.service_name
self.worker_name = self.config.worker_name or self.job_name
self.namespace = self.config.namespace or self.job_name
self.lc_server = self.config.lc_server
if str(self.get_parameters("MODEL_HOT_UPDATE",
"False")).lower() == "true":
ModelLoadingThread(self.estimator, self.report_task_info).start()
@property
def model_path(self):
if os.path.isfile(self.config.model_url):
return self.config.model_url
return self.get_parameters('model_path') or FileOps.join_path(
self.config.model_url, self.estimator.model_name)
def train(self, **kwargs):
raise NotImplementedError
def inference(self, x=None, post_process=None, **kwargs):
res = self.estimator.predict(x, kwargs=kwargs)
callback_func = None
if callable(post_process):
callback_func = post_process
elif post_process is not None:
callback_func = ClassFactory.get_cls(ClassType.CALLBACK,
post_process)
return callback_func(res) if callback_func else res
def evaluate(self, data, post_process=None, **kwargs):
callback_func = None
if callable(post_process):
callback_func = post_process
elif post_process is not None:
callback_func = ClassFactory.get_cls(ClassType.CALLBACK,
post_process)
res = self.estimator.evaluate(data=data, **kwargs)
return callback_func(res) if callback_func else res
def get_parameters(self, param, default=None):
return self.parameters.get_parameters(param=param, default=default)
def report_task_info(self, task_info, status, results=None, kind="train"):
message = {
"name": self.worker_name,
"namespace": self.namespace,
"ownerName": self.job_name,
"ownerKind": self.job_kind,
"kind": kind,
"status": status
}
if results:
message["results"] = results
if task_info:
message["ownerInfo"] = task_info
try:
LCClient.send(self.lc_server, self.worker_name, message)
except Exception as err:
self.log.error(err)