def __init__(self, url, verbose=False):
super(CustomInferenceServerClient, self).__init__(url, verbose=False)
channel_opt = [('grpc.max_send_message_length', 10 * 3 * 1024 * 1024),
('grpc.max_receive_message_length', 5 * 1024 * 1024)]
self._channel = grpc.insecure_channel(url, options=channel_opt)
self._client_stub = grpc_service_pb2_grpc.GRPCInferenceServiceStub(
self._channel)
self._verbose = verbose
self._stream = None
def test_grpc(self):
channel = grpc.insecure_channel("localhost:8001")
grpc_stub = grpc_service_pb2_grpc.GRPCInferenceServiceStub(channel)
# Send request with invalid output name
for trial in _trials:
model_name = "{}_nobatch_zero_1_float32".format(trial)
request = self.requestGenerator(model_name, "DUMMY")
try:
response = grpc_stub.ModelInfer(request)
self.assertTrue(
False,
"unexpected success for unknown output " + model_name)
except grpc.RpcError as rpc_error:
msg = rpc_error.details()
self.assertTrue(
msg.startswith(
"unexpected inference output 'DUMMY' for model"))
default='localhost:8001',
help='Inference server URL. Default is localhost:8001.')
FLAGS = parser.parse_args()
# We use a simple model that takes 2 input tensors of 16 integers
# each and returns 2 output tensors of 16 integers each. One
# output tensor is the element-wise sum of the inputs and one
# output is the element-wise difference.
model_name = "simple_string"
model_version = ""
batch_size = 1
# Create gRPC stub for communicating with the server
channel = grpc.insecure_channel(FLAGS.url)
grpc_stub = grpc_service_pb2_grpc.GRPCInferenceServiceStub(channel)
# Generate the request
request = grpc_service_pb2.ModelInferRequest()
request.model_name = model_name
request.model_version = model_version
# Populate the inputs in inference request
input0 = grpc_service_pb2.ModelInferRequest().InferInputTensor()
input0.name = "INPUT0"
input0.datatype = "BYTES"
input0.shape.extend([1, 16])
for i in range(16):
input0.contents.byte_contents.append(('{}'.format(i)).encode('utf-8'))
input1 = grpc_service_pb2.ModelInferRequest().InferInputTensor()
