def test_server_start():
input_names = [
"IteratorGetNext:0", "IteratorGetNext:1", "IteratorGetNext:4"
]
output_names = ["loss/Softmax"]
port = random.randint(1000, 65535)
parallel_inference_config = ParallelInferenceConfig(workers=1)
serving_config = ServingConfig(
http_port=port,
input_data_format="NUMPY",
output_data_format="NUMPY",
log_timings=True,
)
tensorflow_config = TensorFlowConfig(
model_config_type=ModelConfigType(
model_type="TENSORFLOW", model_loading_path="bert_mrpc_frozen.pb"),
tensor_data_types_config=TensorDataTypesConfig(
input_data_types={
"IteratorGetNext:0": "INT32",
"IteratorGetNext:1": "INT32",
"IteratorGetNext:4": "INT32",
}),
)
model_pipeline_step = ModelStep(
model_config=tensorflow_config,
parallel_inference_config=parallel_inference_config,
input_names=input_names,
output_names=output_names,
)
inference = InferenceConfiguration(serving_config=serving_config,
steps=[model_pipeline_step])
server = Server(inference_config=inference,
extra_start_args="-Xmx8g",
jar_path="konduit.jar")
server.start()
client = Client(input_data_format="NUMPY",
prediction_type="NUMPY",
port=port)
data_input = {
"IteratorGetNext:0": np.load("../data/input-0.npy"),
"IteratorGetNext:1": np.load("../data/input-1.npy"),
"IteratorGetNext:4": np.load("../data/input-4.npy"),
}
assert is_port_in_use(port)
try:
predicted = client.predict(data_input)
print(predicted)
server.stop()
except Exception as e:
print(e)
server.stop()
