def create_set_either_shm_region(shm_region_names, input_list, input_byte_size,
output_byte_size, use_system_shared_memory,
use_cuda_shared_memory):
if use_cuda_shared_memory and use_system_shared_memory:
raise ValueError(
"Cannot set both System and CUDA shared memory flags to 1")
if not (use_system_shared_memory or use_cuda_shared_memory):
return []
if use_cuda_shared_memory:
shm_ip_handle = cudashm.create_shared_memory_region(
shm_region_names[0] + "_data", input_byte_size, 0)
shm_op_handle = cudashm.create_shared_memory_region(
shm_region_names[1] + "_data", output_byte_size, 0)
cudashm.set_shared_memory_region(shm_ip_handle, input_list)
elif use_system_shared_memory:
shm_ip_handle = shm.create_shared_memory_region(
shm_region_names[0] + "_data", "/" + shm_region_names[0],
input_byte_size)
shm_op_handle = shm.create_shared_memory_region(
shm_region_names[1] + "_data", "/" + shm_region_names[1],
output_byte_size)
shm.set_shared_memory_region(shm_ip_handle, input_list)
return [shm_ip_handle, shm_op_handle]
def prepare(self, **kwargs):
concurrency = 10
# Make sure the model matches our requirements, and get some
# properties of the model that we need for preprocessing
print("Model metadata:", self.model_name, self.model_version)
try:
model_metadata = self.triton_client.get_model_metadata(
model_name=self.model_name, model_version=self.model_version)
except InferenceServerException as e:
print("failed to retrieve the metadata: " + str(e))
sys.exit(1)
try:
model_config = self.triton_client.get_model_config(
model_name=self.model_name, model_version=self.model_version)
except InferenceServerException as e:
print("failed to retrieve the config: " + str(e))
sys.exit(1)
self.max_batch_size, self.input_name, self.output_name, self.c, self.h, self.w, self.format, self.dtype, self.out_shapes = parse_model_grpc(
model_metadata, model_config.config)
self.in_handle_name = f'{self.model_name}_data_{os.getpid()}'
self.input_bytesize = 12 * self.w * self.h * self.max_batch_size
self.in_handle = cudashm.create_shared_memory_region(
self.in_handle_name, self.input_bytesize, 0)
self.out_handle_name = f'{self.model_name}_data_out_{os.getpid()}'
self.out_bytesize = 12 * 512 * self.max_batch_size
self.out_handle = cudashm.create_shared_memory_region(
self.out_handle_name, self.out_bytesize, 0)
self.triton_client.unregister_cuda_shared_memory(self.in_handle_name)
self.triton_client.unregister_cuda_shared_memory(self.out_handle_name)
self.triton_client.register_cuda_shared_memory(
self.in_handle_name, cudashm.get_raw_handle(self.in_handle), 0,
self.input_bytesize)
self.triton_client.register_cuda_shared_memory(
self.out_handle_name, cudashm.get_raw_handle(self.out_handle), 0,
self.out_bytesize)
def test_http_out_of_shared_memory(self):
triton_client = tritonhttpclient.InferenceServerClient("localhost:8000")
inputs = []
inputs.append(tritonhttpclient.InferInput('INPUT', [1], "UINT8"))
inputs[0].set_data_from_numpy(np.arange(1, dtype=np.uint8))
# Set up too small CUDA shared memory for outputs, expect query
# returns default value
triton_client.unregister_system_shared_memory()
triton_client.unregister_cuda_shared_memory()
shm_op0_handle = cudashm.create_shared_memory_region(
"output0_data", 1, 0)
shm_op1_handle = cudashm.create_shared_memory_region(
"output1_data", 1, 0)
triton_client.register_cuda_shared_memory(
"output0_data", cudashm.get_raw_handle(shm_op0_handle), 0, 1)
triton_client.register_cuda_shared_memory(
"output1_data", cudashm.get_raw_handle(shm_op1_handle), 0, 1)
outputs = []
outputs.append(
tritonhttpclient.InferRequestedOutput('OUTPUT0', binary_data=True))
outputs[-1].set_shared_memory("output0_data", 1)
outputs.append(
tritonhttpclient.InferRequestedOutput('OUTPUT1', binary_data=True))
outputs[-1].set_shared_memory("output1_data", 1)
try:
triton_client.infer(model_name="query",
inputs=inputs,
outputs=outputs)
self.assertTrue(False, "expect error with query information")
except InferenceServerException as ex:
self.assertTrue("OUTPUT0 CPU 0" in ex.message())
self.assertTrue("OUTPUT1 CPU 0" in ex.message())
cudashm.destroy_shared_memory_region(shm_op0_handle)
cudashm.destroy_shared_memory_region(shm_op1_handle)
triton_client.unregister_system_shared_memory()
triton_client.unregister_cuda_shared_memory()
def create_set_shm_regions(input0_list, input1_list, output0_byte_size,
output1_byte_size, outputs, shm_region_names,
precreated_shm_regions, use_system_shared_memory,
use_cuda_shared_memory):
if use_system_shared_memory and use_cuda_shared_memory:
raise ValueError(
"Cannot set both System and CUDA shared memory flags to 1")
if not (use_system_shared_memory or use_cuda_shared_memory):
return [], []
input0_byte_size = sum([i0.nbytes for i0 in input0_list])
input1_byte_size = sum([i1.nbytes for i1 in input1_list])
if shm_region_names is None:
shm_region_names = ['input0', 'input1', 'output0', 'output1']
shm_op0_handle = None
shm_op1_handle = None
if use_system_shared_memory:
shm_ip0_handle = shm.create_shared_memory_region(
shm_region_names[0] + '_data', '/' + shm_region_names[0],
input0_byte_size)
shm_ip1_handle = shm.create_shared_memory_region(
shm_region_names[1] + '_data', '/' + shm_region_names[1],
input1_byte_size)
i = 0
if "OUTPUT0" in outputs:
if precreated_shm_regions is None:
shm_op0_handle = shm.create_shared_memory_region(
shm_region_names[2] + '_data', '/' + shm_region_names[2],
output0_byte_size)
else:
shm_op0_handle = precreated_shm_regions[0]
i += 1
if "OUTPUT1" in outputs:
if precreated_shm_regions is None:
shm_op1_handle = shm.create_shared_memory_region(
shm_region_names[2 + i] + '_data',
'/' + shm_region_names[2 + i], output1_byte_size)
else:
shm_op1_handle = precreated_shm_regions[i]
shm.set_shared_memory_region(shm_ip0_handle, input0_list)
shm.set_shared_memory_region(shm_ip1_handle, input1_list)
if use_cuda_shared_memory:
shm_ip0_handle = cudashm.create_shared_memory_region(
shm_region_names[0] + '_data', input0_byte_size, 0)
shm_ip1_handle = cudashm.create_shared_memory_region(
shm_region_names[1] + '_data', input1_byte_size, 0)
i = 0
if "OUTPUT0" in outputs:
if precreated_shm_regions is None:
shm_op0_handle = cudashm.create_shared_memory_region(
shm_region_names[2] + '_data', output0_byte_size, 0)
else:
shm_op0_handle = precreated_shm_regions[0]
i += 1
if "OUTPUT1" in outputs:
if precreated_shm_regions is None:
shm_op1_handle = cudashm.create_shared_memory_region(
shm_region_names[2 + i] + '_data', output1_byte_size, 0)
else:
shm_op1_handle = precreated_shm_regions[i]
cudashm.set_shared_memory_region(shm_ip0_handle, input0_list)
cudashm.set_shared_memory_region(shm_ip1_handle, input1_list)
return shm_region_names, [
shm_ip0_handle, shm_ip1_handle, shm_op0_handle, shm_op1_handle
]
def create_set_shm_regions(input0_list, input1_list, output0_byte_size,
output1_byte_size, outputs, shm_region_names,
precreated_shm_regions, use_system_shared_memory,
use_cuda_shared_memory):
# Lazy shm imports...
if use_system_shared_memory:
import tritonclient.utils.shared_memory as shm
if use_cuda_shared_memory:
import tritonclient.utils.cuda_shared_memory as cudashm
if use_system_shared_memory and use_cuda_shared_memory:
raise ValueError(
"Cannot set both System and CUDA shared memory flags to 1")
if not (use_system_shared_memory or use_cuda_shared_memory):
return [], []
if input0_list[0].dtype == np.object_:
input0_byte_size = sum(
[serialized_byte_size(i0) for i0 in input0_list])
else:
input0_byte_size = sum([i0.nbytes for i0 in input0_list])
if input1_list[0].dtype == np.object_:
input1_byte_size = sum(
[serialized_byte_size(i1) for i1 in input1_list])
else:
input1_byte_size = sum([i1.nbytes for i1 in input1_list])
if shm_region_names is None:
shm_region_names = ['input0', 'input1', 'output0', 'output1']
shm_op0_handle = None
shm_op1_handle = None
if use_system_shared_memory:
shm_ip0_handle = shm.create_shared_memory_region(
shm_region_names[0] + '_data', '/' + shm_region_names[0],
input0_byte_size)
shm_ip1_handle = shm.create_shared_memory_region(
shm_region_names[1] + '_data', '/' + shm_region_names[1],
input1_byte_size)
i = 0
if "OUTPUT0" in outputs:
if precreated_shm_regions is None:
shm_op0_handle = shm.create_shared_memory_region(
shm_region_names[2] + '_data', '/' + shm_region_names[2],
output0_byte_size)
else:
shm_op0_handle = precreated_shm_regions[0]
i += 1
if "OUTPUT1" in outputs:
if precreated_shm_regions is None:
shm_op1_handle = shm.create_shared_memory_region(
shm_region_names[2 + i] + '_data',
'/' + shm_region_names[2 + i], output1_byte_size)
else:
shm_op1_handle = precreated_shm_regions[i]
shm.set_shared_memory_region(shm_ip0_handle, input0_list)
shm.set_shared_memory_region(shm_ip1_handle, input1_list)
if use_cuda_shared_memory:
shm_ip0_handle = cudashm.create_shared_memory_region(
shm_region_names[0] + '_data', input0_byte_size, 0)
shm_ip1_handle = cudashm.create_shared_memory_region(
shm_region_names[1] + '_data', input1_byte_size, 0)
i = 0
if "OUTPUT0" in outputs:
if precreated_shm_regions is None:
shm_op0_handle = cudashm.create_shared_memory_region(
shm_region_names[2] + '_data', output0_byte_size, 0)
else:
shm_op0_handle = precreated_shm_regions[0]
i += 1
if "OUTPUT1" in outputs:
if precreated_shm_regions is None:
shm_op1_handle = cudashm.create_shared_memory_region(
shm_region_names[2 + i] + '_data', output1_byte_size, 0)
else:
shm_op1_handle = precreated_shm_regions[i]
cudashm.set_shared_memory_region(shm_ip0_handle, input0_list)
cudashm.set_shared_memory_region(shm_ip1_handle, input1_list)
return shm_region_names, [
shm_ip0_handle, shm_ip1_handle, shm_op0_handle, shm_op1_handle
]
def check_sequence(self,
trial,
model_name,
input_dtype,
correlation_id,
sequence_thresholds,
values,
expected_result,
protocol,
batch_size=1,
sequence_name="<unknown>",
tensor_shape=(1,)):
"""Perform sequence of inferences. The 'values' holds a list of
tuples, one for each inference with format:
(flag_str, value, (ls_ms, gt_ms), (pre_delay_ms, post_delay_ms)
"""
if (("savedmodel" not in trial) and ("graphdef" not in trial) and
("custom" not in trial) and ("onnx" not in trial) and
("libtorch" not in trial) and ("plan" not in trial)):
self.assertFalse(True, "unknown trial type: " + trial)
# Can only send the request exactly once since it is a
# sequence model with state, so can have only a single config.
configs = []
if protocol == "http":
configs.append((f"{_tritonserver_ipaddr}:8000", "http", False))
if protocol == "grpc":
configs.append((f"{_tritonserver_ipaddr}:8001", "grpc", False))
if protocol == "streaming":
configs.append((f"{_tritonserver_ipaddr}:8001", "grpc", True))
self.assertFalse(
_test_system_shared_memory and _test_cuda_shared_memory,
"Cannot set both System and CUDA shared memory flags to 1")
self.assertEqual(len(configs), 1)
full_shape = tensor_shape if "nobatch" in trial else (
batch_size,) + tensor_shape
# create and register shared memory output region in advance,
# knowing that this function will not be called concurrently.
if _test_system_shared_memory or _test_cuda_shared_memory:
self.triton_client_.unregister_system_shared_memory()
self.triton_client_.unregister_cuda_shared_memory()
output_byte_size = 512
if _test_system_shared_memory:
shm_op_handle = shm.create_shared_memory_region(
"output_data", "/output", output_byte_size)
self.triton_client_.register_system_shared_memory(
"output_data", "/output", output_byte_size)
elif _test_cuda_shared_memory:
shm_op_handle = cudashm.create_shared_memory_region(
"output_data", output_byte_size, 0)
self.triton_client_.register_cuda_shared_memory(
"output_data", cudashm.get_raw_handle(shm_op_handle), 0,
output_byte_size)
shm_ip_handles = []
for config in configs:
client_utils = grpcclient if config[1] == "grpc" else httpclient
triton_client = client_utils.InferenceServerClient(config[0],
verbose=True)
if config[2]:
user_data = UserData()
triton_client.start_stream(
partial(completion_callback, user_data))
# Execute the sequence of inference...
try:
seq_start_ms = int(round(time.time() * 1000))
INPUT = "INPUT__0" if trial.startswith("libtorch") else "INPUT"
OUTPUT = "OUTPUT__0" if trial.startswith(
"libtorch") else "OUTPUT"
for flag_str, value, thresholds, delay_ms in values:
if _test_valgrind or _test_jetson:
if delay_ms is not None:
delay_ms[0] = max(_valgrind_delay_ms, delay_ms[0])
delay_ms[1] = max(_valgrind_delay_ms, delay_ms[1])
else:
delay_ms = (_valgrind_delay_ms, _valgrind_delay_ms)
if delay_ms is not None:
time.sleep(delay_ms[0] / 1000.0)
seq_start = False
seq_end = False
if flag_str is not None:
seq_start = ("start" in flag_str)
seq_end = ("end" in flag_str)
# Construct request IOs
inputs = []
outputs = []
inputs.append(
client_utils.InferInput(
INPUT, full_shape, np_to_triton_dtype(input_dtype)))
outputs.append(client_utils.InferRequestedOutput(OUTPUT))
if input_dtype == np.object_:
in0 = np.full(full_shape, value, dtype=np.int32)
in0n = np.array([str(x) for x in in0.reshape(in0.size)],
dtype=object)
in0 = in0n.reshape(full_shape)
else:
in0 = np.full(full_shape, value, dtype=input_dtype)
# create input shared memory and copy input data values into it
if _test_system_shared_memory or _test_cuda_shared_memory:
if input_dtype == np.object_:
input_list_tmp = iu.serialize_byte_tensor_list(
[in0])
input_byte_size = sum([
serialized_byte_size(i0)
for i0 in input_list_tmp
])
else:
input_list_tmp = [in0]
input_byte_size = sum(
[i0.nbytes for i0 in input_list_tmp])
ip_name = "ip{}".format(len(shm_ip_handles))
if _test_system_shared_memory:
shm_ip_handles.append(
shm.create_shared_memory_region(
ip_name, "/" + ip_name, input_byte_size))
shm.set_shared_memory_region(
shm_ip_handles[-1], input_list_tmp)
triton_client.register_system_shared_memory(
ip_name, "/" + ip_name, input_byte_size)
elif _test_cuda_shared_memory:
shm_ip_handles.append(
cudashm.create_shared_memory_region(
ip_name, input_byte_size, 0))
cudashm.set_shared_memory_region(
shm_ip_handles[-1], input_list_tmp)
triton_client.register_cuda_shared_memory(
ip_name,
cudashm.get_raw_handle(shm_ip_handles[-1]), 0,
input_byte_size)
inputs[0].set_shared_memory(ip_name, input_byte_size)
outputs[0].set_shared_memory("output_data",
output_byte_size)
else:
inputs[0].set_data_from_numpy(in0)
start_ms = int(round(time.time() * 1000))
if config[2]:
triton_client.async_stream_infer(
model_name,
inputs,
outputs=outputs,
sequence_id=correlation_id,
sequence_start=seq_start,
sequence_end=seq_end)
(results, error) = user_data._completed_requests.get()
if error is not None:
raise error
else:
results = triton_client.infer(
model_name,
inputs,
outputs=outputs,
sequence_id=correlation_id,
sequence_start=seq_start,
sequence_end=seq_end)
end_ms = int(round(time.time() * 1000))
# Get value of "OUTPUT", for shared memory, need to get it via
# shared memory utils
if (not _test_system_shared_memory) and (
not _test_cuda_shared_memory):
out = results.as_numpy(OUTPUT)
else:
output = results.get_output(OUTPUT)
if config[1] == "http":
output_shape = output["shape"]
else:
output_shape = output.shape
output_type = input_dtype
if _test_system_shared_memory:
out = shm.get_contents_as_numpy(
shm_op_handle, output_type, output_shape)
else:
out = cudashm.get_contents_as_numpy(
shm_op_handle, output_type, output_shape)
result = out[0] if "nobatch" in trial else out[0][0]
print("{}: {}".format(sequence_name, result))
if thresholds is not None:
lt_ms = thresholds[0]
gt_ms = thresholds[1]
if lt_ms is not None:
self.assertTrue((end_ms - start_ms) < lt_ms,
"expected less than " + str(lt_ms) +
"ms response time, got " +
str(end_ms - start_ms) + " ms")
if gt_ms is not None:
self.assertTrue(
(end_ms - start_ms) > gt_ms,
"expected greater than " + str(gt_ms) +
"ms response time, got " +
str(end_ms - start_ms) + " ms")
if delay_ms is not None:
time.sleep(delay_ms[1] / 1000.0)
seq_end_ms = int(round(time.time() * 1000))
if input_dtype == np.object_:
self.assertEqual(int(result), expected_result)
else:
self.assertEqual(result, expected_result)
if sequence_thresholds is not None:
lt_ms = sequence_thresholds[0]
gt_ms = sequence_thresholds[1]
if lt_ms is not None:
if _test_jetson:
lt_ms *= _jetson_slowdown_factor
self.assertTrue((seq_end_ms - seq_start_ms) < lt_ms,
"sequence expected less than " +
str(lt_ms) + "ms response time, got " +
str(seq_end_ms - seq_start_ms) + " ms")
if gt_ms is not None:
self.assertTrue((seq_end_ms - seq_start_ms) > gt_ms,
"sequence expected greater than " +
str(gt_ms) + "ms response time, got " +
str(seq_end_ms - seq_start_ms) + " ms")
except Exception as ex:
self.add_deferred_exception(ex)
if config[2]:
triton_client.stop_stream()
if _test_system_shared_memory or _test_cuda_shared_memory:
self.triton_client_.unregister_system_shared_memory()
self.triton_client_.unregister_cuda_shared_memory()
destroy_func = shm.destroy_shared_memory_region if _test_system_shared_memory else cudashm.destroy_shared_memory_region
destroy_func(shm_op_handle)
for shm_ip_handle in shm_ip_handles:
destroy_func(shm_ip_handle)
def precreate_register_regions(self,
value_list,
dtype,
i,
batch_size=1,
tensor_shape=(1,)):
if _test_system_shared_memory or _test_cuda_shared_memory:
shm_region_handles = []
for j, value in enumerate(value_list):
# For string we can't know the size of the output
# so we conservatively assume 64 bytes for each
# element of the output
if dtype == np.object_:
output_byte_size = 4 # size of empty string
else:
output_byte_size = 0
# create data
input_list = list()
for b in range(batch_size):
if dtype == np.object_:
in0 = np.full(tensor_shape, value, dtype=np.int32)
in0n = np.array([
str(x).encode('utf-8')
for x in in0.reshape(in0.size)
],
dtype=object)
in0 = in0n.reshape(tensor_shape)
output_byte_size += 64 * in0.size
else:
in0 = np.full(tensor_shape, value, dtype=dtype)
output_byte_size += np.dtype(dtype).itemsize * in0.size
input_list.append(in0)
if dtype == np.object_:
input_list_tmp = iu.serialize_byte_tensor_list(input_list)
input_byte_size = sum(
[serialized_byte_size(i0) for i0 in input_list_tmp])
else:
input_list_tmp = input_list
input_byte_size = sum([i0.nbytes for i0 in input_list_tmp])
# create shared memory regions and copy data for input values
ip_name = 'ip{}{}'.format(i, j)
op_name = 'op{}{}_data'.format(i, j)
if _test_system_shared_memory:
shm_ip_handle = shm.create_shared_memory_region(
ip_name, '/' + ip_name, input_byte_size)
shm_op_handle = shm.create_shared_memory_region(
op_name, '/' + op_name, output_byte_size)
shm.set_shared_memory_region(shm_ip_handle, input_list_tmp)
self.triton_client_.register_system_shared_memory(
ip_name, '/' + ip_name, input_byte_size)
self.triton_client_.register_system_shared_memory(
op_name, '/' + op_name, output_byte_size)
elif _test_cuda_shared_memory:
shm_ip_handle = cudashm.create_shared_memory_region(
ip_name, input_byte_size, 0)
shm_op_handle = cudashm.create_shared_memory_region(
op_name, output_byte_size, 0)
cudashm.set_shared_memory_region(shm_ip_handle,
input_list_tmp)
self.triton_client_.register_cuda_shared_memory(
ip_name, cudashm.get_raw_handle(shm_ip_handle), 0,
input_byte_size)
self.triton_client_.register_cuda_shared_memory(
op_name, cudashm.get_raw_handle(shm_op_handle), 0,
output_byte_size)
shm_region_handles.append(
(ip_name, input_byte_size, shm_ip_handle))
shm_region_handles.append(
(op_name, output_byte_size, shm_op_handle))
return shm_region_handles
else:
return []
# 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"
model_version = ""
# 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)
input1_data = np.ones(shape=16, dtype=np.int32)
input_byte_size = input0_data.size * input0_data.itemsize
output_byte_size = input_byte_size
# Create Output0 and Output1 in Shared Memory and store shared memory handles
shm_op0_handle = cudashm.create_shared_memory_region(
"output0_data", output_byte_size, 0)
shm_op1_handle = cudashm.create_shared_memory_region(
"output1_data", output_byte_size, 0)
# Register Output0 and Output1 shared memory with Triton Server
triton_client.register_cuda_shared_memory(
"output0_data", cudashm.get_raw_handle(shm_op0_handle), 0,
output_byte_size)
triton_client.register_cuda_shared_memory(
"output1_data", cudashm.get_raw_handle(shm_op1_handle), 0,
output_byte_size)
# Create Input0 and Input1 in Shared Memory and store shared memory handles
shm_ip0_handle = cudashm.create_shared_memory_region(
"input0_data", input_byte_size, 0)
shm_ip1_handle = cudashm.create_shared_memory_region(
def test_buffer_attributes(self):
model_name = 'bls'
# Infer
clients = [
httpclient.InferenceServerClient(url='localhost:8000'),
grpcclient.InferenceServerClient(url='localhost:8001')
]
triton_clients = [httpclient, grpcclient]
for i, client in enumerate(clients):
# To make sure no shared memory regions are registered with the
# server.
client.unregister_system_shared_memory()
client.unregister_cuda_shared_memory()
triton_client = triton_clients[i]
inputs = []
outputs = []
inputs.append(
triton_client.InferInput('INPUT0', [1, 1000], "INT32"))
input0_data = np.arange(start=0, stop=1000, dtype=np.int32)
input0_data = np.expand_dims(input0_data, axis=0)
input_byte_size = input0_data.size * input0_data.itemsize
output_byte_size = input_byte_size
shm_ip0_handle = cudashm.create_shared_memory_region(
"input0_data", input_byte_size, 0)
shm_op0_handle = cudashm.create_shared_memory_region(
"output0_data", output_byte_size, 0)
client.register_cuda_shared_memory(
"input0_data", cudashm.get_raw_handle(shm_ip0_handle), 0,
input_byte_size)
client.register_cuda_shared_memory(
"output0_data", cudashm.get_raw_handle(shm_op0_handle), 0,
input_byte_size)
cudashm.set_shared_memory_region(shm_ip0_handle, [input0_data])
inputs[0].set_shared_memory("input0_data", input_byte_size)
if triton_client is grpcclient:
outputs.append(triton_client.InferRequestedOutput('OUTPUT0'))
outputs[0].set_shared_memory("output0_data", output_byte_size)
else:
outputs.append(
triton_client.InferRequestedOutput('OUTPUT0',
binary_data=True))
outputs[0].set_shared_memory("output0_data", output_byte_size)
results = client.infer(model_name=model_name,
inputs=inputs,
outputs=outputs)
output0 = results.get_output("OUTPUT0")
self.assertIsNotNone(output0)
if triton_client is grpcclient:
output0_data = cudashm.get_contents_as_numpy(
shm_op0_handle, triton_to_np_dtype(output0.datatype),
output0.shape)
else:
output0_data = cudashm.get_contents_as_numpy(
shm_op0_handle, triton_to_np_dtype(output0['datatype']),
output0['shape'])
self.assertTrue(np.all(output0_data == input0_data))
