def requestGenerator(input_name, output_name, c, h, w, format, dtype, FLAGS):
# Preprocess image into input data according to model requirements
image_data = None
with Image.open(FLAGS.image_filename) as img:
image_data = preprocess(img, format, dtype, c, h, w, FLAGS.scaling)
repeated_image_data = [image_data for _ in range(FLAGS.batch_size)]
batched_image_data = np.stack(repeated_image_data, axis=0)
# Set the input data
inputs = []
if FLAGS.protocol.lower() == "grpc":
inputs.append(grpcclient.InferInput(input_name, batched_image_data.shape, dtype))
inputs[0].set_data_from_numpy(batched_image_data)
else:
inputs.append(httpclient.InferInput(input_name, batched_image_data.shape, dtype))
inputs[0].set_data_from_numpy(batched_image_data, binary_data=False)
outputs = []
if FLAGS.protocol.lower() == "grpc":
outputs.append(grpcclient.InferRequestedOutput(output_name, class_count=FLAGS.classes))
else:
outputs.append(httpclient.InferRequestedOutput(
output_name, binary_data=False, class_count=FLAGS.classes))
yield inputs, outputs, FLAGS.model_name, FLAGS.model_version
def async_stream_send(triton_client, values, batch_size, sequence_id,
model_name, model_version):
count = 1
for value in values:
# Create the tensor for INPUT
value_data = np.full(shape=[batch_size, 1],
fill_value=value,
dtype=np.int32)
inputs = []
inputs.append(grpcclient.InferInput('INPUT', value_data.shape,
"INT32"))
# Initialize the data
inputs[0].set_data_from_numpy(value_data)
outputs = []
outputs.append(grpcclient.InferRequestedOutput('OUTPUT'))
# Issue the asynchronous sequence inference.
triton_client.async_stream_infer(model_name=model_name,
inputs=inputs,
outputs=outputs,
request_id='{}_{}'.format(
sequence_id, count),
sequence_id=sequence_id,
sequence_start=(count == 1),
sequence_end=(count == len(values)))
count = count + 1
def requestGenerator(input_name, output_name, c, h, w, format, dtype, FLAGS):
inputs = []
inputs.append(grpcclient.InferInput(input_name))
# Preprocess image into input data according to model requirements
image_data = None
with Image.open(FLAGS.image_filename) as img:
image_data = preprocess(img, format, dtype, c, h, w, FLAGS.scaling)
repeated_image_data = [image_data for _ in range(FLAGS.batch_size)]
batched_image_data = np.stack(repeated_image_data, axis=0)
# Set the input data
inputs[0].set_data_from_numpy(batched_image_data)
outputs = []
outputs.append(grpcclient.InferOutput(output_name))
outputs[0].set_parameter("classification", 2)
yield inputs, outputs, FLAGS.model_name, FLAGS.model_version
def sync_send(triton_client, result_list, values, batch_size, sequence_id,
model_name, model_version):
count = 1
for value in values:
# Create the tensor for INPUT
value_data = np.full(shape=[batch_size, 1],
fill_value=value,
dtype=np.int32)
inputs = []
inputs.append(grpcclient.InferInput('INPUT'))
# Initialize the data
inputs[0].set_data_from_numpy(value_data)
outputs = []
outputs.append(grpcclient.InferOutput('OUTPUT'))
# Issue the asynchronous sequence inference.
result = triton_client.infer(model_name=model_name,
inputs=inputs,
outputs=outputs,
sequence_id=sequence_id,
sequence_start=(count == 1),
sequence_end=(count == len(values)))
result_list.append(result.as_numpy('OUTPUT'))
count = count + 1
# Put input data values into shared memory
cudashm.set_shared_memory_region(shm_ip0_handle, [input0_data])
cudashm.set_shared_memory_region(shm_ip1_handle, [input1_data])
# Register Input0 and Input1 shared memory with Triton Server
triton_client.register_cuda_shared_memory(
"input0_data", cudashm.get_raw_handle(shm_ip0_handle), 0,
input_byte_size)
triton_client.register_cuda_shared_memory(
"input1_data", cudashm.get_raw_handle(shm_ip1_handle), 0,
input_byte_size)
# Set the parameters to use data from shared memory
inputs = []
inputs.append(grpcclient.InferInput('INPUT0', [1, 16], "INT32"))
inputs[-1].set_shared_memory("input0_data", input_byte_size)
inputs.append(grpcclient.InferInput('INPUT1', [1, 16], "INT32"))
inputs[-1].set_shared_memory("input1_data", input_byte_size)
outputs = []
outputs.append(grpcclient.InferRequestedOutput('OUTPUT0'))
outputs[-1].set_shared_memory("output0_data", output_byte_size)
outputs.append(grpcclient.InferRequestedOutput('OUTPUT1'))
outputs[-1].set_shared_memory("output1_data", output_byte_size)
results = triton_client.infer(model_name=model_name,
inputs=inputs,
outputs=outputs)
default='localhost:8001',
help='Inference server URL. Default is localhost:8001.')
FLAGS = parser.parse_args()
try:
triton_client = grpcclient.InferenceServerClient(url=FLAGS.url,
verbose=FLAGS.verbose)
except Exception as e:
print("context creation failed: " + str(e))
sys.exit()
model_name = 'simple_string'
inputs = []
outputs = []
inputs.append(grpcclient.InferInput('INPUT0', [1, 16], "BYTES"))
inputs.append(grpcclient.InferInput('INPUT1', [1, 16], "BYTES"))
# Create the data for the two input tensors. Initialize the first
# to unique integers and the second to all ones.
in0 = np.arange(start=0, stop=16, dtype=np.int32)
in0 = np.expand_dims(in0, axis=0)
in1 = np.ones(shape=(1, 16), dtype=np.int32)
expected_sum = np.add(in0, in1)
expected_diff = np.subtract(in0, in1)
in0n = np.array([str(x) for x in in0.reshape(in0.size)], dtype=object)
input0_data = in0n.reshape(in0.shape)
in1n = np.array([str(x) for x in in1.reshape(in1.size)], dtype=object)
input1_data = in1n.reshape(in1.shape)
default='localhost:8001',
help='Inference server URL. Default is localhost:8001.')
FLAGS = parser.parse_args()
try:
triton_client = grpcclient.InferenceServerClient(FLAGS.url)
except Exception as e:
print("context creation failed: " + str(e))
sys.exit()
model_name = 'simple'
# Infer
inputs = []
outputs = []
inputs.append(grpcclient.InferInput('INPUT0'))
inputs.append(grpcclient.InferInput('INPUT1'))
# 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)
input0_data = np.expand_dims(input0_data, axis=0)
input1_data = np.ones(shape=(1, 16), dtype=np.int32)
# Initialize the data
inputs[0].set_data_from_numpy(input0_data)
inputs[1].set_data_from_numpy(input1_data)
outputs.append(grpcclient.InferOutput('OUTPUT0'))
outputs.append(grpcclient.InferOutput('OUTPUT1'))
