'--protocol',
type=str,
required=False,
default='HTTP',
help='Protocol (HTTP/gRPC) used to communicate with ' +
'the inference service. Default is HTTP.')
parser.add_argument('image_filename', type=str, help='Input image.')
FLAGS = parser.parse_args()
try:
if FLAGS.protocol.lower() == "grpc":
# Create gRPC client for communicating with the server
triton_client = grpcclient.InferenceServerClient(FLAGS.url)
else:
# Create HTTP client for communicating with the server
triton_client = httpclient.InferenceServerClient(FLAGS.url)
except Exception as e:
print("context creation failed: " + str(e))
sys.exit()
# Make sure the model matches our requirements, and get some
# properties of the model that we need for preprocessing
try:
model_metadata = triton_client.get_model_metadata(
model_name=FLAGS.model_name, model_version=FLAGS.model_version)
except InferenceServerException as e:
print("failed to retrieve the metadata: " + str(e))
sys.exit()
try:
model_config = triton_client.get_model_config(
'--verbose',
action="store_true",
required=False,
default=False,
help='Enable verbose output')
parser.add_argument(
'-u',
'--url',
type=str,
required=False,
default='localhost:8000',
help='Inference server URL. Default is localhost:8000.')
FLAGS = parser.parse_args()
try:
triton_client = httpclient.InferenceServerClient(url=FLAGS.url,
verbose=FLAGS.verbose)
except Exception as e:
print("context creation failed: " + str(e))
sys.exit()
model_name = 'simple'
# Infer
inputs = []
outputs = []
inputs.append(httpclient.InferInput('INPUT0', [1, 16], "INT32"))
inputs.append(httpclient.InferInput('INPUT1', [1, 16], "INT32"))
# Create the data for the two input tensors. Initialize the first
# to unique integers and the second to all ones.
input0_data = np.arange(start=0, stop=16, dtype=np.int32)
