def initialize_client(self, tf_serving_host=None, tf_serving_port=None):
signature_message = None
if self.type == "onnx":
from cortex.lib.client.onnx import ONNXClient
client = ONNXClient(self.models)
if self.models[0].name == consts.SINGLE_MODEL_NAME:
signature_message = "ONNX model signature: {}".format(
client.input_signatures[consts.SINGLE_MODEL_NAME]
)
else:
signature_message = "ONNX model signatures: {}".format(client.input_signatures)
cx_logger().info(signature_message)
return client
elif self.type == "tensorflow":
from cortex.lib.client.tensorflow import TensorFlowClient
for model in self.models:
validate_model_dir(model.base_path)
tf_serving_address = tf_serving_host + ":" + tf_serving_port
client = TensorFlowClient(tf_serving_address, self.models)
if self.models[0].name == consts.SINGLE_MODEL_NAME:
signature_message = "TensorFlow model signature: {}".format(
client.input_signatures[consts.SINGLE_MODEL_NAME]
)
else:
signature_message = "TensorFlow model signatures: {}".format(
client.input_signatures
)
cx_logger().info(signature_message)
return client
return None
def initialize_client(
self,
tf_serving_host: Optional[str] = None,
tf_serving_port: Optional[str] = None
) -> Union[PythonClient, TensorFlowClient, ONNXClient]:
"""
Initialize client that gives access to models specified in the API spec (cortex.yaml).
Only applies when models are provided in the API spec.
Args:
tf_serving_host: Host of TF serving server. To be only used when the TensorFlow predictor is used.
tf_serving_port: Port of TF serving server. To be only used when the TensorFlow predictor is used.
Return:
The client for the respective predictor type.
"""
signature_message = None
client = None
if _are_models_specified(self.api_spec):
if self.type == PythonPredictorType:
client = PythonClient(self.api_spec, self.models,
self.model_dir, self.models_tree)
if self.type in [
TensorFlowPredictorType, TensorFlowNeuronPredictorType
]:
tf_serving_address = tf_serving_host + ":" + tf_serving_port
client = TensorFlowClient(
tf_serving_address,
self.api_spec,
self.models,
self.model_dir,
self.models_tree,
)
if not self.caching_enabled:
cron = TFSAPIServingThreadUpdater(interval=5.0,
client=client._client)
cron.start()
if self.type == ONNXPredictorType:
client = ONNXClient(self.api_spec, self.models, self.model_dir,
self.models_tree)
return client
def initialize_client(self, args):
if self.type == "onnx":
from cortex.lib.client.onnx import ONNXClient
_, prefix = self.storage.deconstruct_s3_path(self.model)
model_path = os.path.join(args.model_dir, os.path.basename(prefix))
client = ONNXClient(model_path)
cx_logger().info("ONNX model signature: {}".format(client.input_signature))
return client
elif self.type == "tensorflow":
from cortex.lib.client.tensorflow import TensorFlowClient
validate_model_dir(args.model_dir)
client = TensorFlowClient("localhost:" + str(args.tf_serve_port), self.signature_key)
cx_logger().info("TensorFlow model signature: {}".format(client.input_signature))
return client
return None
def initialize_client(self,
model_dir=None,
tf_serving_host=None,
tf_serving_port=None):
if self.type == "onnx":
from cortex.lib.client.onnx import ONNXClient
model_path = os.path.join(model_dir, os.path.basename(self.model))
client = ONNXClient(model_path)
cx_logger().info("ONNX model signature: {}".format(
client.input_signature))
return client
elif self.type == "tensorflow":
from cortex.lib.client.tensorflow import TensorFlowClient
tf_serving_address = tf_serving_host + ":" + tf_serving_port
validate_model_dir(model_dir)
client = TensorFlowClient(tf_serving_address, self.signature_key)
cx_logger().info("TensorFlow model signature: {}".format(
client.input_signature))
return client
return None