def infer_zero(tester, pf, batch_size, tensor_dtype, input_shapes, output_shapes,
model_version=None, use_http=True, use_grpc=True,
use_http_json_tensors=True, use_streaming=True, shm_region_name_prefix=None,
use_system_shared_memory=False, use_cuda_shared_memory=False,
priority=0, timeout_us=0):
tester.assertTrue(
use_http or use_grpc or use_http_json_tensors or use_streaming)
configs = []
if use_http:
configs.append(("localhost:8000", "http", False, True))
if use_http_json_tensors and (tensor_dtype != np.float16):
configs.append(("localhost:8000", "http", False, False))
if use_grpc:
configs.append(("localhost:8001", "grpc", False, False))
if use_streaming:
configs.append(("localhost:8001", "grpc", True, False))
tester.assertEqual(len(input_shapes), len(output_shapes))
io_cnt = len(input_shapes)
if shm_region_name_prefix is None:
shm_region_name_prefix = ["input", "output"]
input_dict = {}
expected_dict = {}
shm_ip_handles = list()
shm_op_handles = list()
for io_num in range(io_cnt):
if pf == "libtorch" or pf == "libtorch_nobatch":
input_name = "INPUT__{}".format(io_num)
output_name = "OUTPUT__{}".format(io_num)
else:
input_name = "INPUT{}".format(io_num)
output_name = "OUTPUT{}".format(io_num)
input_shape = input_shapes[io_num]
output_shape = output_shapes[io_num]
rtensor_dtype = _range_repr_dtype(tensor_dtype)
if (rtensor_dtype != np.bool):
input_array = np.random.randint(low=np.iinfo(rtensor_dtype).min,
high=np.iinfo(rtensor_dtype).max,
size=input_shape, dtype=rtensor_dtype)
else:
input_array = np.random.choice(a=[False, True], size=input_shape)
if tensor_dtype != np.object:
input_array = input_array.astype(tensor_dtype)
expected_array = np.ndarray.copy(input_array)
else:
expected_array = np.array([unicode(str(x), encoding='utf-8')
for x in input_array.flatten()], dtype=object)
input_array = np.array([str(x) for x in input_array.flatten()],
dtype=object).reshape(input_array.shape)
expected_array = expected_array.reshape(output_shape)
expected_dict[output_name] = expected_array
output_byte_size = expected_array.nbytes
if batch_size == 1:
input_list = [input_array]
else:
input_list = [x for x in input_array]
# Serialization of string tensors in the case of shared memory must be done manually
if tensor_dtype == np.object:
input_list_tmp = serialize_byte_tensor_list(input_list)
else:
input_list_tmp = input_list
input_byte_size = sum([ip.nbytes for ip in input_list_tmp])
# create and register shared memory region for inputs and outputs
shm_io_handles = su.create_set_either_shm_region([shm_region_name_prefix[0]+str(io_num),
shm_region_name_prefix[1]+str(io_num)],
input_list_tmp, input_byte_size, output_byte_size,
use_system_shared_memory, use_cuda_shared_memory)
if len(shm_io_handles) != 0:
shm_ip_handles.append(shm_io_handles[0])
shm_op_handles.append(shm_io_handles[1])
input_dict[input_name] = input_array
if model_version is not None:
model_version = str(model_version)
else:
model_version = ""
# Run inference and check results for each config
for config in configs:
model_name = tu.get_zero_model_name(pf, io_cnt, tensor_dtype)
if config[1] == "http":
triton_client = httpclient.InferenceServerClient(
config[0], verbose=True)
else:
triton_client = grpcclient.InferenceServerClient(
config[0], verbose=True)
inputs = []
output_req = []
for io_num, (input_name, output_name) in enumerate(zip(input_dict.keys(), expected_dict.keys())):
input_data = input_dict[input_name]
input_byte_size = input_data.nbytes
output_byte_size = expected_dict[output_name].nbytes
if config[1] == "http":
inputs.append(httpclient.InferInput(
input_name, input_data.shape, np_to_triton_dtype(tensor_dtype)))
output_req.append(httpclient.InferRequestedOutput(
output_name, binary_data=config[3]))
else:
inputs.append(grpcclient.InferInput(
input_name, input_data.shape, np_to_triton_dtype(tensor_dtype)))
output_req.append(
grpcclient.InferRequestedOutput(output_name))
if not (use_cuda_shared_memory or use_system_shared_memory):
if config[1] == "http":
inputs[-1].set_data_from_numpy(input_data, binary_data=config[3])
else:
inputs[-1].set_data_from_numpy(input_data)
else:
# Register necessary shared memory regions/handles
su.register_add_either_shm_regions(inputs, output_req, shm_region_name_prefix,
(shm_ip_handles, shm_op_handles), io_num, input_byte_size, output_byte_size,
use_system_shared_memory, use_cuda_shared_memory, triton_client)
if config[2]:
user_data = UserData()
triton_client.start_stream(partial(completion_callback, user_data))
try:
results = triton_client.async_stream_infer(model_name,
inputs,
model_version=model_version,
outputs=output_req,
request_id=str(_unique_request_id()),
priority=priority, timeout=timeout_us)
except Exception as e:
triton_client.stop_stream()
raise e
triton_client.stop_stream()
(results, error) = user_data._completed_requests.get()
if error is not None:
raise error
else:
results = triton_client.infer(model_name,
inputs,
model_version=model_version,
outputs=output_req,
request_id=str(_unique_request_id()),
priority=priority, timeout=timeout_us)
last_response = results.get_response()
if config[1] == "http":
response_model_name = last_response["model_name"]
if model_version != "":
response_model_version = last_response["model_version"]
response_outputs = last_response["outputs"]
else:
response_model_name = last_response.model_name
if model_version != "":
response_model_version = last_response.model_version
response_outputs = last_response.outputs
tester.assertEqual(response_model_name, model_name)
if model_version != "":
tester.assertEqual(response_model_version, model_version)
tester.assertEqual(len(response_outputs), io_cnt)
for result in response_outputs:
if config[1] == "http":
result_name = result["name"]
else:
result_name = result.name
tester.assertTrue(result_name in expected_dict)
if use_system_shared_memory or use_cuda_shared_memory:
if pf == "libtorch" or pf == "libtorch_nobatch":
io_num = int(result_name.split("OUTPUT__")[1])
else:
io_num = int(result_name.split("OUTPUT")[1])
shm_handle = shm_op_handles[io_num]
output = results.get_output(result_name)
if config[1] == "http":
output_datatype = output['datatype']
output_shape = output['shape']
else:
output_datatype = output.datatype
output_shape = output.shape
output_dtype = triton_to_np_dtype(output_datatype)
if use_system_shared_memory:
output_data = shm.get_contents_as_numpy(
shm_handle, output_dtype, output_shape)
elif use_cuda_shared_memory:
output_data = cudashm.get_contents_as_numpy(
shm_handle, output_dtype, output_shape)
else:
output_data = results.as_numpy(result_name)
if (output_data.dtype == np.object) and (config[3] == False):
output_data = output_data.astype(np.bytes_)
expected = expected_dict[result_name]
tester.assertEqual(output_data.shape, expected.shape)
tester.assertTrue(np.array_equal(output_data, expected),
"{}, {}, expected: {}, got {}".format(
model_name, result_name, expected, output_data))
if len(shm_ip_handles) != 0:
for io_num in range(io_cnt):
if use_cuda_shared_memory:
triton_client.unregister_cuda_shared_memory(
shm_region_name_prefix[0]+str(io_num)+'_data')
triton_client.unregister_cuda_shared_memory(
shm_region_name_prefix[0]+str(io_num)+'_data')
cudashm.destroy_shared_memory_region(shm_ip_handles[io_num])
cudashm.destroy_shared_memory_region(shm_op_handles[io_num])
else:
triton_client.unregister_system_shared_memory(
shm_region_name_prefix[1]+str(io_num)+'_data')
triton_client.unregister_system_shared_memory(
shm_region_name_prefix[1]+str(io_num)+'_data')
shm.destroy_shared_memory_region(shm_ip_handles[io_num])
shm.destroy_shared_memory_region(shm_op_handles[io_num])
return results
def infer_exact(tester, pf, tensor_shape, batch_size,
input_dtype, output0_dtype, output1_dtype,
output0_raw=True, output1_raw=True,
model_version=None, swap=False,
outputs=("OUTPUT0", "OUTPUT1"), use_http=True, use_grpc=True,
use_http_json_tensors=True, skip_request_id_check=False, use_streaming=True,
correlation_id=0, shm_region_names=None, precreated_shm_regions=None,
use_system_shared_memory=False, use_cuda_shared_memory=False,
priority=0, timeout_us=0):
tester.assertTrue(
use_http or use_http_json_tensors or use_grpc or use_streaming)
configs = []
if use_http:
configs.append(("localhost:8000", "http", False, True))
if output0_raw == output1_raw:
# Float16 not supported for Input and Output via JSON
if use_http_json_tensors and (input_dtype != np.float16) and \
(output0_dtype != np.float16) and (output1_dtype != np.float16):
configs.append(("localhost:8000", "http", False, False))
if use_grpc:
configs.append(("localhost:8001", "grpc", False, False))
if use_streaming:
configs.append(("localhost:8001", "grpc", True, False))
# outputs are sum and difference of inputs so set max input
# values so that they will not overflow the output. This
# allows us to do an exact match. For float types use 8, 16,
# 32 int range for fp 16, 32, 64 respectively. When getting
# class outputs the result value/probability is returned as a
# float so must use fp32 range in that case.
rinput_dtype = _range_repr_dtype(input_dtype)
routput0_dtype = _range_repr_dtype(
output0_dtype if output0_raw else np.float32)
routput1_dtype = _range_repr_dtype(
output1_dtype if output1_raw else np.float32)
val_min = max(np.iinfo(rinput_dtype).min,
np.iinfo(routput0_dtype).min,
np.iinfo(routput1_dtype).min) / 2
val_max = min(np.iinfo(rinput_dtype).max,
np.iinfo(routput0_dtype).max,
np.iinfo(routput1_dtype).max) / 2
num_classes = 3
input0_array = np.random.randint(low=val_min, high=val_max,
size=tensor_shape, dtype=rinput_dtype)
input1_array = np.random.randint(low=val_min, high=val_max,
size=tensor_shape, dtype=rinput_dtype)
if input_dtype != np.object:
input0_array = input0_array.astype(input_dtype)
input1_array = input1_array.astype(input_dtype)
if not swap:
output0_array = input0_array + input1_array
output1_array = input0_array - input1_array
else:
output0_array = input0_array - input1_array
output1_array = input0_array + input1_array
if output0_dtype == np.object:
output0_array = np.array([unicode(str(x), encoding='utf-8')
for x in (output0_array.flatten())], dtype=object).reshape(output0_array.shape)
else:
output0_array = output0_array.astype(output0_dtype)
if output1_dtype == np.object:
output1_array = np.array([unicode(str(x), encoding='utf-8')
for x in (output1_array.flatten())], dtype=object).reshape(output1_array.shape)
else:
output1_array = output1_array.astype(output1_dtype)
if input_dtype == np.object:
in0n = np.array([str(x)
for x in input0_array.reshape(input0_array.size)], dtype=object)
input0_array = in0n.reshape(input0_array.shape)
in1n = np.array([str(x)
for x in input1_array.reshape(input1_array.size)], dtype=object)
input1_array = in1n.reshape(input1_array.shape)
# prepend size of string to output string data
if output0_dtype == np.object:
if batch_size == 1:
output0_array_tmp = serialize_byte_tensor_list([output0_array])
else:
output0_array_tmp = serialize_byte_tensor_list(output0_array)
else:
output0_array_tmp = output0_array
if output1_dtype == np.object:
if batch_size == 1:
output1_array_tmp = serialize_byte_tensor_list([output1_array])
else:
output1_array_tmp = serialize_byte_tensor_list(output1_array)
else:
output1_array_tmp = output1_array
OUTPUT0 = "OUTPUT0"
OUTPUT1 = "OUTPUT1"
INPUT0 = "INPUT0"
INPUT1 = "INPUT1"
if pf == "libtorch" or pf == "libtorch_nobatch":
OUTPUT0 = "OUTPUT__0"
OUTPUT1 = "OUTPUT__1"
INPUT0 = "INPUT__0"
INPUT1 = "INPUT__1"
output0_byte_size = sum([o0.nbytes for o0 in output0_array_tmp])
output1_byte_size = sum([o1.nbytes for o1 in output1_array_tmp])
if batch_size == 1:
input0_list = [input0_array]
input1_list = [input1_array]
else:
input0_list = [x for x in input0_array]
input1_list = [x for x in input1_array]
# Serialization of string tensors in the case of shared memory must be done manually
if input_dtype == np.object:
input0_list_tmp = serialize_byte_tensor_list(input0_list)
input1_list_tmp = serialize_byte_tensor_list(input1_list)
else:
input0_list_tmp = input0_list
input1_list_tmp = input1_list
input0_byte_size = sum([i0.nbytes for i0 in input0_list_tmp])
input1_byte_size = sum([i1.nbytes for i1 in input1_list_tmp])
# Create system/cuda shared memory regions if needed
shm_regions, shm_handles = su.create_set_shm_regions(input0_list_tmp, input1_list_tmp, output0_byte_size,
output1_byte_size, outputs, shm_region_names, precreated_shm_regions,
use_system_shared_memory, use_cuda_shared_memory)
if model_version is not None:
model_version = str(model_version)
else:
model_version = ""
# Run inference and check results for each config
for config in configs:
model_name = tu.get_model_name(
pf, input_dtype, output0_dtype, output1_dtype)
if config[1] == "http":
triton_client = httpclient.InferenceServerClient(
config[0], verbose=True)
else:
triton_client = grpcclient.InferenceServerClient(
config[0], verbose=True)
inputs = []
if config[1] == "http":
inputs.append(httpclient.InferInput(
INPUT0, tensor_shape, np_to_triton_dtype(input_dtype)))
inputs.append(httpclient.InferInput(
INPUT1, tensor_shape, np_to_triton_dtype(input_dtype)))
else:
inputs.append(grpcclient.InferInput(
INPUT0, tensor_shape, np_to_triton_dtype(input_dtype)))
inputs.append(grpcclient.InferInput(
INPUT1, tensor_shape, np_to_triton_dtype(input_dtype)))
if not (use_cuda_shared_memory or use_system_shared_memory):
if config[1] == "http":
inputs[0].set_data_from_numpy(
input0_array, binary_data=config[3])
inputs[1].set_data_from_numpy(
input1_array, binary_data=config[3])
else:
inputs[0].set_data_from_numpy(input0_array)
inputs[1].set_data_from_numpy(input1_array)
else:
# Register necessary shared memory regions/handles
su.register_add_shm_regions(inputs, outputs, shm_regions, precreated_shm_regions, shm_handles,
input0_byte_size, input1_byte_size, output0_byte_size, output1_byte_size,
use_system_shared_memory, use_cuda_shared_memory, triton_client)
if batch_size == 1:
expected0_sort_idx = [np.flip(np.argsort(x.flatten()), 0)
for x in output0_array.reshape((1,) + tensor_shape)]
expected1_sort_idx = [np.flip(np.argsort(x.flatten()), 0)
for x in output1_array.reshape((1,) + tensor_shape)]
else:
expected0_sort_idx = [np.flip(np.argsort(x.flatten()), 0)
for x in output0_array.reshape(tensor_shape)]
expected1_sort_idx = [np.flip(np.argsort(x.flatten()), 0)
for x in output1_array.reshape(tensor_shape)]
# Force binary_data = False for shared memory and class
output_req = []
i = 0
if "OUTPUT0" in outputs:
if len(shm_regions) != 0:
if config[1] == "http":
output_req.append(httpclient.InferRequestedOutput(
OUTPUT0, binary_data=config[3]))
else:
output_req.append(grpcclient.InferRequestedOutput(OUTPUT0))
output_req[-1].set_shared_memory(
shm_regions[2]+'_data', output0_byte_size)
else:
if output0_raw:
if config[1] == "http":
output_req.append(httpclient.InferRequestedOutput(
OUTPUT0, binary_data=config[3]))
else:
output_req.append(
grpcclient.InferRequestedOutput(OUTPUT0))
else:
if config[1] == "http":
output_req.append(httpclient.InferRequestedOutput(
OUTPUT0, binary_data=config[3], class_count=num_classes))
else:
output_req.append(grpcclient.InferRequestedOutput(
OUTPUT0, class_count=num_classes))
i += 1
if "OUTPUT1" in outputs:
if len(shm_regions) != 0:
if config[1] == "http":
output_req.append(httpclient.InferRequestedOutput(
OUTPUT1, binary_data=config[3]))
else:
output_req.append(grpcclient.InferRequestedOutput(OUTPUT1))
output_req[-1].set_shared_memory(
shm_regions[2+i]+'_data', output1_byte_size)
else:
if output1_raw:
if config[1] == "http":
output_req.append(httpclient.InferRequestedOutput(
OUTPUT1, binary_data=config[3]))
else:
output_req.append(
grpcclient.InferRequestedOutput(OUTPUT1))
else:
if config[1] == "http":
output_req.append(httpclient.InferRequestedOutput(
OUTPUT1, binary_data=config[3], class_count=num_classes))
else:
output_req.append(grpcclient.InferRequestedOutput(
OUTPUT1, class_count=num_classes))
if config[2]:
user_data = UserData()
triton_client.start_stream(partial(completion_callback, user_data))
try:
results = triton_client.async_stream_infer(model_name,
inputs,
model_version=model_version,
outputs=output_req,
request_id=str(_unique_request_id()))
except Exception as e:
triton_client.stop_stream()
raise e
triton_client.stop_stream()
(results, error) = user_data._completed_requests.get()
if error is not None:
raise error
else:
results = triton_client.infer(model_name,
inputs,
model_version=model_version,
outputs=output_req,
request_id=str(_unique_request_id()))
last_response = results.get_response()
if not skip_request_id_check:
global _seen_request_ids
if config[1] == "http":
request_id = int(last_response["id"])
else:
request_id = int(last_response.id)
tester.assertFalse(request_id in _seen_request_ids,
"request_id: {}".format(request_id))
_seen_request_ids.add(request_id)
if config[1] == "http":
response_model_name = last_response["model_name"]
if model_version != "":
response_model_version = last_response["model_version"]
response_outputs = last_response["outputs"]
else:
response_model_name = last_response.model_name
if model_version != "":
response_model_version = last_response.model_version
response_outputs = last_response.outputs
tester.assertEqual(response_model_name, model_name)
if model_version != "":
tester.assertEqual(str(response_model_version), model_version)
tester.assertEqual(len(response_outputs), len(outputs))
for result in response_outputs:
if config[1] == "http":
result_name = result["name"]
else:
result_name = result.name
if ((result_name == OUTPUT0 and output0_raw) or
(result_name == OUTPUT1 and output1_raw)):
if use_system_shared_memory or use_cuda_shared_memory:
if result_name == OUTPUT0:
shm_handle = shm_handles[2]
else:
shm_handle = shm_handles[3]
output = results.get_output(result_name)
if config[1] == "http":
output_datatype = output['datatype']
output_shape = output['shape']
else:
output_datatype = output.datatype
output_shape = output.shape
output_dtype = triton_to_np_dtype(output_datatype)
if use_system_shared_memory:
output_data = shm.get_contents_as_numpy(
shm_handle, output_dtype, output_shape)
elif use_cuda_shared_memory:
output_data = cudashm.get_contents_as_numpy(
shm_handle, output_dtype, output_shape)
else:
output_data = results.as_numpy(result_name)
if (output_data.dtype == np.object) and (config[3] == False):
output_data = output_data.astype(np.bytes_)
if result_name == OUTPUT0:
tester.assertTrue(np.array_equal(output_data, output0_array),
"{}, {} expected: {}, got {}".format(
model_name, OUTPUT0, output0_array, output_data))
elif result_name == OUTPUT1:
tester.assertTrue(np.array_equal(output_data, output1_array),
"{}, {} expected: {}, got {}".format(
model_name, OUTPUT1, output1_array, output_data))
else:
tester.assertTrue(
False, "unexpected raw result {}".format(result_name))
else:
for b in range(batch_size):
# num_classes values must be returned and must
# match expected top values
if "nobatch" in pf:
class_list = results.as_numpy(result_name)
else:
class_list = results.as_numpy(result_name)[b]
tester.assertEqual(len(class_list), num_classes)
if batch_size == 1:
expected0_flatten = output0_array.flatten()
expected1_flatten = output1_array.flatten()
else:
expected0_flatten = output0_array[b].flatten()
expected1_flatten = output1_array[b].flatten()
for idx, class_label in enumerate(class_list):
# can't compare indices since could have different
# indices with the same value/prob, so check that
# the value of each index equals the expected value.
# Only compare labels when the indices are equal.
if type(class_label) == str:
ctuple = class_label.split(':')
else:
ctuple = "".join(chr(x)
for x in class_label).split(':')
cval = float(ctuple[0])
cidx = int(ctuple[1])
if result_name == OUTPUT0:
tester.assertEqual(cval, expected0_flatten[cidx])
tester.assertEqual(
cval, expected0_flatten[expected0_sort_idx[b][idx]])
if cidx == expected0_sort_idx[b][idx]:
tester.assertEqual(ctuple[2], 'label{}'.format(
expected0_sort_idx[b][idx]))
elif result_name == OUTPUT1:
tester.assertEqual(cval, expected1_flatten[cidx])
tester.assertEqual(
cval, expected1_flatten[expected1_sort_idx[b][idx]])
else:
tester.assertTrue(
False, "unexpected class result {}".format(result_name))
# Unregister system/cuda shared memory regions if they exist
su.unregister_cleanup_shm_regions(shm_regions, shm_handles, precreated_shm_regions, outputs,
use_system_shared_memory, use_cuda_shared_memory)
return results
request_count = 2
try:
# Need to specify large enough concurrency to issue all the
# inference requests to the server in parallel.
triton_client = tritonhttpclient.InferenceServerClient(
url=FLAGS.url, verbose=FLAGS.verbose, concurrency=request_count)
except Exception as e:
print("context creation failed: " + str(e))
sys.exit()
model_name = 'simple'
# Infer
inputs = []
outputs = []
inputs.append(tritonhttpclient.InferInput('INPUT0', [1, 16], "INT32"))
inputs.append(tritonhttpclient.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)
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, binary_data=True)
inputs[1].set_data_from_numpy(input1_data, binary_data=True)
outputs.append(
tritonhttpclient.InferRequestedOutput('OUTPUT0', binary_data=True))
outputs.append(
def _test_unicode_bytes(self, model_name):
# We use a simple model that takes an input tensor of 8 byte strings
# and returns an output tensors of 8 strings. The output tensor
# is the same as the input tensor.
# Create the inference server client for the model.
triton_client = tritonhttpclient.InferenceServerClient(
"localhost:8000", verbose=True)
# Create the data for the input tensor. Initialize the tensor to 8
# byte strings. (dtype of np.bytes_)
# Sample string that should no longer cause failure
in0 = np.array([
[
b'\nF\n\'\n\x01a\x12"\x1a \n\x1e\xfa\x03\x94\x01\x0f\xd7\x02\xf1\x05\xdf\x01\x82\x03\xb5\x05\xc1\x07\xba\x06\xff\x06\xc7\x07L\xf5\x03\xe2\x07\xa9\x03\n\x0c\n\x01b\x12\x07\x1a\x05\n\x03\x89\xcc=\n\r\n\x01c\x12\x08\x12\x06\n\x04\xdf\\\xcb\xbf'
],
[
b'\n:\n\x1a\n\x01a\x12\x15\x1a\x13\n\x11*\xe3\x05\xc5\x06\xda\x07\xcb\x06~\xb1\x05\xb3\x01\xa9\x02\x15\n\r\n\x01b\x12\x08\x1a\x06\n\x04\xf6\xa2\xc5\x01\n\r\n\x01c\x12\x08\x12\x06\n\x04\xbb[\n\xbf'
],
[
b'\nL\n-\n\x01a\x12(\x1a&\n$\x87\x07\xce\x01\xe7\x06\xee\x04\xe1\x03\xf1\x03\xd7\x07\xbe\x02\xb8\x05\xe0\x05\xe4\x01\x88\x06\xb6\x03\xb9\x05\x83\x06\xf8\x04\xe2\x04\xf4\x06\n\x0c\n\x01b\x12\x07\x1a\x05\n\x03\x89\xcc=\n\r\n\x01c\x12\x08\x12\x06\n\x04\xbc\x99+@'
],
[
b'\n2\n\x12\n\x01a\x12\r\x1a\x0b\n\t\x99\x02\xde\x04\x9f\x04\xc5\x053\n\r\n\x01b\x12\x08\x1a\x06\n\x04\xf6\xa2\xc5\x01\n\r\n\x01c\x12\x08\x12\x06\n\x04\x12\x07\x83\xbe'
],
[
b'\nJ\n\r\n\x01b\x12\x08\x1a\x06\n\x04\x9b\x94\xad\x04\n\r\n\x01c\x12\x08\x12\x06\n\x04\xc3\x8a\x08\xbf\n*\n\x01a\x12%\x1a#\n!\x9c\x02\xb2\x02\xcd\x02\x9d\x07\x8d\x01\xb6\x05a\xf1\x01\xf0\x05\xdb\x02\xac\x04\xbd\x05\xe0\x04\xd2\x06\xaf\x02\xa8\x01\x8b\x04'
],
[
b'\n3\n\x13\n\x01a\x12\x0e\x1a\x0c\n\n<\xe2\x05\x8a\x01\xb3\x07?\xfd\x01\n\r\n\x01b\x12\x08\x1a\x06\n\x04\xf6\xa2\xc5\x01\n\r\n\x01c\x12\x08\x12\x06\n\x04\x1b\x931\xbf'
],
[
b'\n&\n\x07\n\x01a\x12\x02\x1a\x00\n\x0c\n\x01b\x12\x07\x1a\x05\n\x03\x89\xcc=\n\r\n\x01c\x12\x08\x12\x06\n\x04{\xbc\x0e>'
],
[
b'\nF\n\'\n\x01a\x12"\x1a \n\x1e\x97\x01\x93\x02\x9e\x01\xac\x06\xff\x01\xd8\x05\xe1\x07\xd8\x04g]\x9a\x05\xff\x06\xde\x07\x8f\x04\x97\x04\xda\x03\n\x0c\n\x01b\x12\x07\x1a\x05\n\x03\x9a\xb7I\n\r\n\x01c\x12\x08\x12\x06\n\x04\xfb\x87\x83\xbf'
]
],
dtype='|S78').flatten()
# Send inference request to the inference server. Get results for
# both output tensors.
inputs = []
outputs = []
inputs.append(tritonhttpclient.InferInput('INPUT0', in0.shape,
"BYTES"))
inputs[0].set_data_from_numpy(in0)
outputs.append(tritonhttpclient.InferRequestedOutput('OUTPUT0'))
results = triton_client.infer(model_name=model_name,
inputs=inputs,
outputs=outputs)
out0 = results.as_numpy('OUTPUT0')
# We expect there to be 1 results (with batch-size 1). Verify
# that all 8 result elements are the same as the input.
self.assertTrue(np.array_equal(in0, out0))
# Same test but for np.object_
in0_object = in0.astype(np.object)
inputs = []
outputs = []
inputs.append(
tritonhttpclient.InferInput('INPUT0', in0_object.shape, "BYTES"))
inputs[0].set_data_from_numpy(in0_object)
outputs.append(tritonhttpclient.InferRequestedOutput('OUTPUT0'))
results = triton_client.infer(model_name=model_name,
inputs=inputs,
outputs=outputs)
output0_object = results.as_numpy('OUTPUT0')
# We expect there to be 1 results (with batch-size 1). Verify
# that all 8 result elements are the same as the input.
self.assertTrue(np.array_equal(in0_object, output0_object))
# Verify that np.bytes_ and np.object_ are the same.
self.assertTrue(np.array_equal(out0, output0_object))
# Same test but for np.bytes_
in0_bytes = in0.astype(np.bytes_)
inputs = []
outputs = []
inputs.append(
tritonhttpclient.InferInput('INPUT0', in0_bytes.shape, "BYTES"))
inputs[0].set_data_from_numpy(in0_object)
outputs.append(tritonhttpclient.InferRequestedOutput('OUTPUT0'))
results = triton_client.infer(model_name=model_name,
inputs=inputs,
outputs=outputs)
output0_byte = results.as_numpy('OUTPUT0')
# We expect there to be 1 results (with batch-size 1). Verify
# that all 8 result elements are the same as the input.
self.assertTrue(np.array_equal(in0_bytes, output0_byte))
# Verify that the output is the same as before
self.assertTrue(np.array_equal(out0, output0_byte))
# to occupy an SM
model_name = FLAGS.model
model_version = "1"
# Create the data for the input tensor.
input_data = np.array([FLAGS.delay], dtype=np.int32)
# Create the inference context for the model.
if FLAGS.protocol.lower() == "grpc":
triton_client = tritongrpcclient.InferenceServerClient(
FLAGS.url, verbose=FLAGS.verbose)
inputs = [tritongrpcclient.InferInput('in', input_data.shape, "INT32")]
else:
triton_client = tritonhttpclient.InferenceServerClient(
FLAGS.url, verbose=FLAGS.verbose)
inputs = [tritonhttpclient.InferInput('in', input_data.shape, "INT32")]
inputs[0].set_data_from_numpy(input_data)
# Send N inference requests to the inference server. Time the inference for both
# requests
start_time = time()
for i in range(FLAGS.count):
triton_client.async_infer(model_name,
inputs,
partial(completion_callback),
model_version=model_version,
request_id=str(i),
headers=FLAGS.http_headers)
def test_nobatch_request_for_batching_model(self):
input_size = 16
# graphdef_int32_int8_int8 has a batching version with max batch size of 8.
# The server should return an error if the batch size is not included in the
# input shapes.
tensor_shape = (input_size, )
for protocol in ["http", "grpc"]:
model_name = tu.get_model_name("graphdef", np.int32, np.int8,
np.int8)
in0 = np.random.randint(low=0,
high=100,
size=tensor_shape,
dtype=np.int32)
in1 = np.random.randint(low=0,
high=100,
size=tensor_shape,
dtype=np.int32)
inputs = []
outputs = []
if protocol == "http":
triton_client = tritonhttpclient.InferenceServerClient(
url='localhost:8000', verbose=True)
inputs.append(
tritonhttpclient.InferInput('INPUT0', tensor_shape,
"INT32"))
inputs.append(
tritonhttpclient.InferInput('INPUT1', tensor_shape,
"INT32"))
outputs.append(
tritonhttpclient.InferRequestedOutput('OUTPUT0'))
outputs.append(
tritonhttpclient.InferRequestedOutput('OUTPUT1'))
else:
triton_client = tritongrpcclient.InferenceServerClient(
url='localhost:8001', verbose=True)
inputs.append(
tritongrpcclient.InferInput('INPUT0', tensor_shape,
"INT32"))
inputs.append(
tritongrpcclient.InferInput('INPUT1', tensor_shape,
"INT32"))
outputs.append(
tritongrpcclient.InferRequestedOutput('OUTPUT0'))
outputs.append(
tritongrpcclient.InferRequestedOutput('OUTPUT1'))
# Initialize the data
inputs[0].set_data_from_numpy(in0)
inputs[1].set_data_from_numpy(in1)
try:
results = triton_client.infer(model_name,
inputs,
outputs=outputs)
self.assertTrue(
False,
"expected failure with no batch request for batching model"
)
except InferenceServerException as ex:
pass
requests = []
responses = []
result_filenames = []
request_ids = []
image_idx = 0
last_request = False
user_data = UserData()
# Holds the handles to the ongoing HTTP async requests.
async_requests = []
sent_count = 0
try:
for image in image_data:
sent_count += 1
inputs = [tritonhttpclient.InferInput(input_name, image.shape, dtype)]
outputs = [tritonhttpclient.InferRequestedOutput(output_name)]
inputs[0].set_data_from_numpy(image, binary_data=True)
responses.append(
triton_client.infer(model_name,
inputs,
request_id=str(sent_count),
model_version=model_version,
outputs=outputs))
except InferenceServerException as e:
print("inference failed: " + str(e))
sys.exit(1)
for response in responses:
this_id = response.get_response()["id"]
]],
dtype='float32') #np.random.rand(*shape).astype(np.float32)
input1_data = np.array([[
123, 630, 1741, 169492, 439138, 549150, 549420, 559916, 561648, 562203,
595960, 617230, 785371, 951890, 954587, 961209, 1127998, 1268021,
1272637, 1273122, 1274952, 1284808, 1599234, 1599246, 1661028, 1679074,
1713689
]],
dtype='uint32')
input2_data = np.array([[
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
20, 21, 22, 23, 24, 25, 26
]],
dtype='int32')
inputs = [
httpclient.InferInput("DES", input0_data.shape,
np_to_triton_dtype(input0_data.dtype)),
httpclient.InferInput("CATCOLUMN", input1_data.shape,
np_to_triton_dtype(input1_data.dtype)),
httpclient.InferInput("ROWINDEX", input2_data.shape,
np_to_triton_dtype(input2_data.dtype)),
]
inputs[0].set_data_from_numpy(input0_data)
inputs[1].set_data_from_numpy(input1_data)
inputs[2].set_data_from_numpy(input2_data)
outputs = [httpclient.InferRequestedOutput("OUTPUT0")]
response = client.infer(model_name,
inputs,
request_id=str(1),
outputs=outputs)
for idx in range(batch_size):
input_filenames.append(filenames[idx])
repeated_image_data.append(image_data[idx])
batched_image_data = np.stack(repeated_image_data, axis=0)
# Set the input data
inputs = []
if FLAGS.protocol.lower() == "grpc":
inputs.append(
tritongrpcclient.InferInput(input_name, batched_image_data.shape,
"BYTES"))
inputs[0].set_data_from_numpy(batched_image_data)
else:
inputs.append(
tritonhttpclient.InferInput(input_name, batched_image_data.shape,
"BYTES"))
inputs[0].set_data_from_numpy(batched_image_data, binary_data=True)
outputs = []
if FLAGS.protocol.lower() == "grpc":
outputs.append(
tritongrpcclient.InferRequestedOutput(output_name,
class_count=FLAGS.classes))
else:
outputs.append(
tritonhttpclient.InferRequestedOutput(output_name,
binary_data=False,
class_count=FLAGS.classes))
# Send request
result = triton_client.infer(model_name, inputs, outputs=outputs)
# Put input data values into shared memory
shm.set_shared_memory_region(shm_ip0_handle, [input0_data_serialized])
shm.set_shared_memory_region(shm_ip1_handle, [input1_data_serialized])
# Register Input0 and Input1 shared memory with Triton Server
triton_client.register_system_shared_memory("input0_data",
"/input0_simple",
input0_byte_size)
triton_client.register_system_shared_memory("input1_data",
"/input1_simple",
input1_byte_size)
# Set the parameters to use data from shared memory
inputs = []
inputs.append(httpclient.InferInput('INPUT0', [1, 16], "BYTES"))
inputs[-1].set_shared_memory("input0_data", input0_byte_size)
inputs.append(httpclient.InferInput('INPUT1', [1, 16], "BYTES"))
inputs[-1].set_shared_memory("input1_data", input1_byte_size)
outputs = []
outputs.append(httpclient.InferRequestedOutput('OUTPUT0',
binary_data=True))
outputs[-1].set_shared_memory("output0_data", output0_byte_size)
outputs.append(httpclient.InferRequestedOutput('OUTPUT1',
binary_data=True))
outputs[-1].set_shared_memory("output1_data", output1_byte_size)
results = triton_client.infer(model_name=model_name,
from tritonclientutils import *
import tritongrpcclient as grpcclient
import tritonhttpclient as httpclient
import numpy as np
model_name = "python_float32_float32_float32"
shape = [4]
with httpclient.InferenceServerClient("localhost:8000") as client:
input0_data = np.random.rand(*shape).astype(np.float32)
input1_data = np.random.rand(*shape).astype(np.float32)
inputs = [
httpclient.InferInput("INPUT0", input0_data.shape,
np_to_triton_dtype(input0_data.dtype)),
httpclient.InferInput("INPUT1", input1_data.shape,
np_to_triton_dtype(input1_data.dtype)),
]
inputs[0].set_data_from_numpy(input0_data)
inputs[1].set_data_from_numpy(input1_data)
outputs = [
httpclient.InferRequestedOutput("OUTPUT0"),
httpclient.InferRequestedOutput("OUTPUT1"),
]
response = client.infer(model_name,
inputs,
request_id=str(1),
outputs=outputs)
