def test_batch_request_for_batching_model(self):
input_size = 16
# graphdef_nobatch_int32_int8_int8 is non batching version.
# The server should return an error if the batch size dimension
# is included in the shape
tensor_shape = (1, 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)
results = triton_client.infer(model_name,
inputs,
outputs=outputs)
def setUp(self):
self.model_name_ = "repeat_int32"
self.inputs_ = []
self.inputs_.append(grpcclient.InferInput('IN', [1, 1], "INT32"))
self.inputs_.append(grpcclient.InferInput('DELAY', [1, 1], "UINT32"))
self.inputs_.append(grpcclient.InferInput('WAIT', [1, 1], "UINT32"))
self.outputs_ = []
self.outputs_.append(grpcclient.InferRequestedOutput('OUT'))
def infer(self, _need_tensor_check=False, **_input_tensor):
inputs = []
assert _input_tensor.keys() == set(self.all_inputs.keys(
)), f'{self.model_name} the input tensor not match'
for m_name, m_tensor_info in self.all_inputs.items():
m_tensor = _input_tensor[m_name]
if not (isinstance(m_tensor, np.ndarray)
and m_tensor.dtype.name in self.numpy_data_type_mapper):
raise InferenceTensorCheckFailException(
f'tensor {m_name} is available numpy array')
if _need_tensor_check:
check_status, check_result = m_tensor_info.tensor_check(
m_tensor, 3 * 10 * 1024 * 1024)
if not check_status:
raise InferenceTensorCheckFailException(check_result)
m_normalized_tensor = m_tensor_info.normalize(
m_tensor, _tensor_format='chw').astype(m_tensor.dtype)
m_infer_input = tritongrpcclient.InferInput(
m_name, m_normalized_tensor.shape,
self.numpy_data_type_mapper[m_normalized_tensor.dtype.name])
m_infer_input.set_data_from_numpy(m_normalized_tensor)
inputs.append(m_infer_input)
results = self.triton_client.infer(model_name=self.model_name,
model_version=self.model_version,
inputs=inputs)
to_return_result = dict()
for m_result_name in self.all_outputs.keys():
to_return_result[m_result_name] = results.as_numpy(m_result_name)
return to_return_result
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(
tritongrpcclient.InferInput(input_name, batched_image_data.shape, dtype))
inputs[0].set_data_from_numpy(batched_image_data)
else:
inputs.append(
tritonhttpclient.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(
tritongrpcclient.InferRequestedOutput(output_name,
class_count=FLAGS.classes))
else:
outputs.append(
tritonhttpclient.InferRequestedOutput(output_name,
binary_data=False,
class_count=FLAGS.classes))
yield inputs, outputs, FLAGS.model_name, FLAGS.model_version
def detector(self, frames):
infer_inputs = [
triton.InferInput('input_1', (len(frames), 3, *self.resize[::-1]),
"FP32")
]
frames = np.array(frames, dtype=np.float32)
frames = np.transpose(frames, (0, 3, 1, 2))
infer_inputs[0].set_data_from_numpy(frames)
result = self.triton_client.infer('retinanet', infer_inputs)
scores = result.as_numpy('scores').reshape((-1, 100))
boxes = result.as_numpy('boxes').reshape((-1, 100, 4))
classes = result.as_numpy('classes').reshape((-1, 100))
# Calculate embeddings for all the detected subjects
embs = []
scores_filtered = []
boxes_filters = []
for i in range(len(frames)):
mask = (scores[i] > 0.4) & (
classes[i] == 0) # only care about 'person' with score > 0.4
scores_i = scores[i, mask]
boxes_i = boxes[i, mask]
scores_i, boxes_i = self.bbox_filter(scores_i, boxes_i)
img = frames[i].astype(np.uint8) # (3, 800, 1280)
embs_i = []
boxes_i = boxes_i.astype(int)
for j in range(len(boxes_i)):
imp = img[:, boxes_i[j, 1]:boxes_i[j, 3],
boxes_i[j, 0]:boxes_i[j, 2]]
imp = np.transpose(imp, (1, 2, 0))
imp = Image.fromarray(imp)
data = [
np.asarray(transforms.Resize(size=(256, 128))(imp)).astype(
np.float32)
]
inputs = []
inputs.append(
tritongrpcclient.InferInput('image',
[len(data), 256, 128, 3],
"FP32"))
# Initialize the data
inputs[0].set_data_from_numpy(np.asarray(data))
outputs = []
outputs.append(
tritongrpcclient.InferRequestedOutput('features'))
results = self.triton_client.infer('osnet_ensemble',
inputs,
outputs=outputs)
emb = np.squeeze(results.as_numpy('features'))
embs_i.append(emb / np.linalg.norm(emb))
embs.append(embs_i)
scores_filtered.append(scores_i)
boxes_filters.append(boxes_i)
return np.asarray(scores_filtered), np.asarray(
boxes_filters), np.asarray(embs)
def setUp(self):
self.trials_ = [("repeat_int32", None), ("simple_repeat", None),
("sequence_repeat", None),
("repeat_square", self._nested_validate),
("nested_square", self._nested_validate)]
self.model_name_ = "repeat_int32"
self.inputs_ = []
self.inputs_.append(grpcclient.InferInput('IN', [1], "INT32"))
self.inputs_.append(grpcclient.InferInput('DELAY', [1], "UINT32"))
self.inputs_.append(grpcclient.InferInput('WAIT', [1], "UINT32"))
self.outputs_ = []
self.outputs_.append(grpcclient.InferRequestedOutput('OUT'))
self.outputs_.append(grpcclient.InferRequestedOutput('IDX'))
# Some trials only expect a subset of outputs
self.requested_outputs_ = self.outputs_
def _initialize_model(self):
input_cfg = self.model_config['config']['input']
output_cfg = self.model_config['config']['output']
input_names = [i['name'] for i in input_cfg]
output_names = [o['name'] for o in output_cfg]
print('Input layers: ', output_names)
print('Output layers: ', output_names)
input_dims = [[int(dim) for dim in input_cfg[i]['dims']]
for i in range(len(input_cfg))]
output_dims = [[int(dim) for dim in output_cfg[i]['dims']]
for i in range(len(output_cfg))]
self.input_shape = input_dims[0]
self.output_dims = output_dims
if self.triton_cfg['model']['precision'] == "FP32":
mult = 4
elif self.triton_cfg['model']['precision'] == "FP16":
mult = 2 # TODO: Fix this
elif self.triton_cfg['model']['precision'] == "INT8":
mult = 1 # TODO: Fix this
else:
print("unsupported precision in config file: " +
str(self.triton_cfg['model']['precision']))
sys.exit()
input_byte_sizes_list = [
self._prod(dims) * mult for dims in input_dims
]
output_byte_sizes_list = [
self._prod(dims) * mult for dims in output_dims
]
for i in range(len(input_cfg)):
shm_region_name = self.model_name + "_input" + str(i)
self._register_system_shm_regions(shm_region_name,
self.input_handles,
input_byte_sizes_list[i],
input_names[i])
self.input_layers.append(
tritongrpcclient.InferInput(
input_names[i],
[1, input_dims[i][0], input_dims[i][1], input_dims[i][2]],
"FP32"))
self.input_layers[-1].set_shared_memory(shm_region_name,
input_byte_sizes_list[i])
for i in range(len(output_cfg)):
shm_region_name = self.model_name + "_output" + str(i)
self._register_system_shm_regions(shm_region_name,
self.output_handles,
output_byte_sizes_list[i],
output_names[i])
self.output_layers.append(
tritongrpcclient.InferRequestedOutput(output_names[i]))
self.output_layers[-1].set_shared_memory(shm_region_name,
output_byte_sizes_list[i])
def main():
FLAGS = parse_args()
try:
triton_client = tritongrpcclient.InferenceServerClient(url=FLAGS.url, verbose=FLAGS.verbose)
except Exception as e:
print("channel creation failed: " + str(e))
sys.exit(1)
model_name = FLAGS.model_name
model_version = -1
print("Loading images")
image_data, labels = load_images(FLAGS.img_dir if FLAGS.img_dir is not None else FLAGS.img)
image_data = array_from_list(image_data)
print("Images loaded, inferring")
# Infer
outputs = []
input_name = "INPUT"
output_name = "OUTPUT"
input_shape = list(image_data.shape)
outputs.append(tritongrpcclient.InferRequestedOutput(output_name))
img_idx = 0
for batch in batcher(image_data, FLAGS.batch_size):
print("Input mean before backend processing:", np.mean(batch))
input_shape[0] = np.shape(batch)[0]
print("Batch size: ", input_shape[0])
inputs = [tritongrpcclient.InferInput(input_name, input_shape, "UINT8")]
# Initialize the data
inputs[0].set_data_from_numpy(batch)
# Test with outputs
results = triton_client.infer(model_name=model_name,
inputs=inputs,
outputs=outputs)
# Get the output arrays from the results
output0_data = results.as_numpy(output_name)
print("Output mean after backend processing:", np.mean(output0_data))
print("Output shape: ", np.shape(output0_data))
maxs = np.argmax(output0_data, axis=1)
for i in range(len(maxs)):
print("Sample ", i, " - label: ", maxs[i], " ~ ", output0_data[i, maxs[i]])
if maxs[i] != labels[img_idx]:
sys.exit(1)
else:
print("pass")
img_idx += 1
statistics = triton_client.get_inference_statistics(model_name=model_name)
if len(statistics.model_stats) != 1:
print("FAILED: Inference Statistics")
sys.exit(1)
def main():
FLAGS = parse_args()
try:
triton_client = tritongrpcclient.InferenceServerClient(
url=FLAGS.url, verbose=FLAGS.verbose)
except Exception as e:
print("channel creation failed: " + str(e))
sys.exit(1)
model_name = FLAGS.model_name
model_version = -1
input_data = [
randint(0, 255, size=randint(100), dtype='uint8')
for _ in range(randint(100) * FLAGS.batch_size)
]
input_data = array_from_list(input_data)
# Infer
outputs = []
input_name = "DALI_INPUT_0"
output_name = "DALI_OUTPUT_0"
input_shape = list(input_data.shape)
outputs.append(tritongrpcclient.InferRequestedOutput(output_name))
for batch in batcher(input_data, FLAGS.batch_size):
print("Input mean before backend processing:", np.mean(batch))
input_shape[0] = np.shape(batch)[0]
print("Batch size: ", input_shape[0])
inputs = [
tritongrpcclient.InferInput(input_name, input_shape, "UINT8")
]
# Initialize the data
inputs[0].set_data_from_numpy(batch)
# Test with outputs
results = triton_client.infer(model_name=model_name,
inputs=inputs,
outputs=outputs)
# Get the output arrays from the results
output0_data = results.as_numpy(output_name)
print("Output mean after backend processing:", np.mean(output0_data))
print("Output shape: ", np.shape(output0_data))
if not math.isclose(np.mean(output0_data), np.mean(batch)):
print("Pre/post average does not match")
sys.exit(1)
else:
print("pass")
statistics = triton_client.get_inference_statistics(model_name=model_name)
if len(statistics.model_stats) != 1:
print("FAILED: Inference Statistics")
sys.exit(1)
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
def _prepare_request(self, protocol):
if (protocol == "grpc"):
self.inputs_ = []
self.inputs_.append(grpcclient.InferInput('INPUT0', [1, 1],
"INT32"))
self.outputs_ = []
self.outputs_.append(grpcclient.InferRequestedOutput('OUTPUT0'))
else:
self.inputs_ = []
self.inputs_.append(httpclient.InferInput('INPUT0', [1, 1],
"INT32"))
self.outputs_ = []
self.outputs_.append(httpclient.InferRequestedOutput('OUTPUT0'))
self.inputs_[0].set_data_from_numpy(self.input0_data_)
def request_eval(hit_data,row_splits, triton_client, model_name):
np_rs_type = 'int64'
tr_rs_type = 'INT64'
inputs = []
outputs = []
#print(hit_data.shape)
#print(row_splits.shape)
inputs.append(tritongrpcclient.InferInput('input_1', hit_data.shape, 'FP32'))
inputs.append(tritongrpcclient.InferInput('input_2', row_splits.shape, tr_rs_type)) #INT64
inputs[0].set_data_from_numpy(hit_data)
inputs[1].set_data_from_numpy(row_splits)
outputs.append(tritongrpcclient.InferRequestedOutput('output'))
outputs.append(tritongrpcclient.InferRequestedOutput('output_1'))
#outputs.append(tritongrpcclient.InferRequestedOutput('predicted_final_condensates'))
#outputs.append(tritongrpcclient.InferRequestedOutput('output_row_splits'))
# predicted_final_1 doesn't matter
results = triton_client.infer(
model_name=model_name,
inputs=inputs,
outputs=outputs
)
condensates = results.as_numpy('output')
#condensates = results.as_numpy('predicted_final_condensates')
#rs = results.as_numpy('output_row_splits')
#print('output',condensates,condensates.shape)
return condensates
def crashing_client(model_name,
dtype,
tensor_shape,
shm_name,
triton_client,
input_name="INPUT0"):
in0 = np.random.random(tensor_shape).astype(dtype)
if "libtorch" in model_name:
input_name = "INPUT__0"
inputs = [
grpcclient.InferInput(input_name, tensor_shape,
np_to_triton_dtype(dtype)),
]
inputs[0].set_data_from_numpy(in0)
# Run in a loop so that it is guaranteed that
# the inference will not have completed when being terminated.
while True:
existing_shm = shared_memory.SharedMemory(shm_name)
count = np.ndarray((1,), dtype=np.int32, buffer=existing_shm.buf)
count[0] += 1
existing_shm.close()
results = triton_client.infer(model_name, inputs)
FLAGS = parser.parse_args()
# We use model that takes 1 input tensor containing the delay number of cycles
# 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),
def main(_):
"""
Ask a question of context on Triton.
:param context: str
:param question: str
:param question_id: int
:return:
"""
os.environ[
"TF_XLA_FLAGS"] = "--tf_xla_enable_lazy_compilation=false" #causes memory fragmentation for bert leading to OOM
tf.compat.v1.logging.info("***** Configuaration *****")
for key in FLAGS.__flags.keys():
tf.compat.v1.logging.info(' {}: {}'.format(key, getattr(FLAGS, key)))
tf.compat.v1.logging.info("**************************")
tokenizer = tokenization.FullTokenizer(vocab_file=FLAGS.vocab_file,
do_lower_case=FLAGS.do_lower_case)
# Get the Data
if FLAGS.question and FLAGS.context:
input_data = [{
"paragraphs": [{
"context": FLAGS.context,
"qas": [{
"id": 0,
"question": FLAGS.question
}]
}]
}]
eval_examples = read_squad_examples(
input_file=None,
is_training=False,
version_2_with_negative=FLAGS.version_2_with_negative,
input_data=input_data)
elif FLAGS.predict_file:
eval_examples = read_squad_examples(
input_file=FLAGS.predict_file,
is_training=False,
version_2_with_negative=FLAGS.version_2_with_negative)
else:
raise ValueError(
"Either predict_file or question+answer need to defined")
# Get Eval Features = Preprocessing
eval_features = []
def append_feature(feature):
eval_features.append(feature)
convert_examples_to_features(examples=eval_examples,
tokenizer=tokenizer,
max_seq_length=FLAGS.max_seq_length,
doc_stride=FLAGS.doc_stride,
max_query_length=FLAGS.max_query_length,
is_training=False,
output_fn=append_feature)
protocol_str = 'grpc' # http or grpc
url = FLAGS.triton_server_url
verbose = False
model_name = FLAGS.triton_model_name
model_version = str(FLAGS.triton_model_version)
batch_size = FLAGS.predict_batch_size
triton_client = tritongrpcclient.InferenceServerClient(url, verbose)
model_metadata = triton_client.get_model_metadata(
model_name=model_name, model_version=model_version)
model_config = triton_client.get_model_config(model_name=model_name,
model_version=model_version)
user_data = UserData()
max_outstanding = 20
# Number of outstanding requests
outstanding = 0
sent_prog = tqdm.tqdm(desc="Send Requests", total=len(eval_features))
recv_prog = tqdm.tqdm(desc="Recv Requests", total=len(eval_features))
def process_outstanding(do_wait, outstanding):
if (outstanding == 0 or do_wait is False):
return outstanding
# Wait for deferred items from callback functions
(result, error, idx, start_time,
inputs) = user_data._completed_requests.get()
if (result is None):
return outstanding
stop = time.time()
if (error is not None):
raise ValueError(
"Context returned null for async id marked as done")
outstanding -= 1
time_list.append(stop - start_time)
batch_count = len(inputs[label_id_key])
if FLAGS.trt_engine:
cls_squad_logits = result.as_numpy("cls_squad_logits")
try: #when batch size > 1
start_logits_results = np.array(
cls_squad_logits.squeeze()[:, :, 0])
end_logits_results = np.array(cls_squad_logits.squeeze()[:, :,
1])
except:
start_logits_results = np.expand_dims(np.array(
cls_squad_logits.squeeze()[:, 0]),
axis=0)
end_logits_results = np.expand_dims(np.array(
cls_squad_logits.squeeze()[:, 1]),
axis=0)
else:
start_logits_results = result.as_numpy("start_logits")
end_logits_results = result.as_numpy("end_logits")
for i in range(batch_count):
unique_id = int(inputs[label_id_key][i][0])
start_logits = [float(x) for x in start_logits_results[i].flat]
end_logits = [float(x) for x in end_logits_results[i].flat]
all_results.append(
RawResult(unique_id=unique_id,
start_logits=start_logits,
end_logits=end_logits))
recv_prog.update(n=batch_count)
return outstanding
all_results = []
time_list = []
print("Starting Sending Requests....\n")
all_results_start = time.time()
idx = 0
for inputs_dict in batch(eval_features, batch_size):
present_batch_size = len(inputs_dict[label_id_key])
if not FLAGS.trt_engine:
label_ids_data = np.stack(inputs_dict[label_id_key])
input_ids_data = np.stack(inputs_dict['input_ids'])
input_mask_data = np.stack(inputs_dict['input_mask'])
segment_ids_data = np.stack(inputs_dict['segment_ids'])
inputs = []
inputs.append(
tritongrpcclient.InferInput('input_ids', input_ids_data.shape,
"INT32"))
inputs[0].set_data_from_numpy(input_ids_data)
inputs.append(
tritongrpcclient.InferInput('input_mask', input_mask_data.shape,
"INT32"))
inputs[1].set_data_from_numpy(input_mask_data)
inputs.append(
tritongrpcclient.InferInput('segment_ids', segment_ids_data.shape,
"INT32"))
inputs[2].set_data_from_numpy(segment_ids_data)
if not FLAGS.trt_engine:
inputs.append(
tritongrpcclient.InferInput(label_id_key, label_ids_data.shape,
"INT32"))
inputs[3].set_data_from_numpy(label_ids_data)
outputs = []
if FLAGS.trt_engine:
outputs.append(
tritongrpcclient.InferRequestedOutput('cls_squad_logits'))
else:
outputs.append(
tritongrpcclient.InferRequestedOutput('start_logits'))
outputs.append(tritongrpcclient.InferRequestedOutput('end_logits'))
start_time = time.time()
triton_client.async_infer(model_name,
inputs,
partial(completion_callback, user_data, idx,
start_time, inputs_dict),
request_id=str(idx),
model_version=model_version,
outputs=outputs)
outstanding += 1
idx += 1
sent_prog.update(n=present_batch_size)
# Try to process at least one response per request
outstanding = process_outstanding(outstanding >= max_outstanding,
outstanding)
tqdm.tqdm.write(
"All Requests Sent! Waiting for responses. Outstanding: {}.\n".format(
outstanding))
# Now process all outstanding requests
while (outstanding > 0):
outstanding = process_outstanding(True, outstanding)
all_results_end = time.time()
all_results_total = (all_results_end - all_results_start) * 1000.0
print("-----------------------------")
print("Total Time: {} ms".format(all_results_total))
print("-----------------------------")
print("-----------------------------")
print("Total Inference Time = %0.2f for"
"Sentences processed = %d" % (sum(time_list), len(eval_features)))
print("Throughput Average (sentences/sec) = %0.2f" %
(len(eval_features) / all_results_total * 1000.0))
print("-----------------------------")
if FLAGS.output_dir and FLAGS.predict_file:
# When inferencing on a dataset, get inference statistics and write results to json file
time_list.sort()
avg = np.mean(time_list)
cf_95 = max(time_list[:int(len(time_list) * 0.95)])
cf_99 = max(time_list[:int(len(time_list) * 0.99)])
cf_100 = max(time_list[:int(len(time_list) * 1)])
print("-----------------------------")
print("Summary Statistics")
print("Batch size =", FLAGS.predict_batch_size)
print("Sequence Length =", FLAGS.max_seq_length)
print("Latency Confidence Level 95 (ms) =", cf_95 * 1000)
print("Latency Confidence Level 99 (ms) =", cf_99 * 1000)
print("Latency Confidence Level 100 (ms) =", cf_100 * 1000)
print("Latency Average (ms) =", avg * 1000)
print("-----------------------------")
output_prediction_file = os.path.join(FLAGS.output_dir,
"predictions.json")
output_nbest_file = os.path.join(FLAGS.output_dir,
"nbest_predictions.json")
output_null_log_odds_file = os.path.join(FLAGS.output_dir,
"null_odds.json")
write_predictions(eval_examples, eval_features, all_results,
FLAGS.n_best_size, FLAGS.max_answer_length,
FLAGS.do_lower_case, output_prediction_file,
output_nbest_file, output_null_log_odds_file,
FLAGS.version_2_with_negative, FLAGS.verbose_logging)
else:
# When inferencing on a single example, write best answer to stdout
all_predictions, all_nbest_json, scores_diff_json = get_predictions(
eval_examples, eval_features, all_results, FLAGS.n_best_size,
FLAGS.max_answer_length, FLAGS.do_lower_case,
FLAGS.version_2_with_negative, FLAGS.verbose_logging)
print(
"Context is: %s \n\nQuestion is: %s \n\nPredicted Answer is: %s" %
(FLAGS.context, FLAGS.question, all_predictions[0]))
def req_loop(self):
client = grpcclient.InferenceServerClient(self._server_url)
inputs = [
grpcclient.InferInput("INPUT0", self._shape,
np_to_triton_dtype(self._dtype))
]
self._inflight_requests = 0
start_stat = client.get_inference_statistics(
model_name=self._model_name)
global _exit_signal
while not _exit_signal:
input_numpy = np.random.random_sample(self._shape).astype(
self._dtype)
inputs[0].set_data_from_numpy(input_numpy)
self._input_data.append(input_numpy)
with self._sync:
def _check_can_send():
return self._inflight_requests < _inference_concurrency
can_send = self._sync.wait_for(_check_can_send,
timeout=_response_wait_time_s)
self._tester.assertTrue(
can_send,
"client didn't receive a response within {}s".format(
_response_wait_time_s))
callback = functools.partial(AsyncGrpcRunner._on_result, self)
client.async_infer(
model_name=self._model_name,
inputs=inputs,
request_id="{}".format(self._num_sent_request),
callback=callback,
)
self._inflight_requests += 1
self._num_sent_request += 1
if (self._num_sent_request == _inference_count):
_exit_signal = True
time.sleep(self._delay_ms / 1000.0)
# wait till receive all requested data
with self._sync:
def _all_processed():
return self._inflight_requests == 0
self._processed_all = self._sync.wait_for(_all_processed,
_finish_wait_time_s)
self._tester.assertTrue(
self._processed_all,
"the processing didn't complete even after waiting for {}s".
format(_finish_wait_time_s))
end_stat = client.get_inference_statistics(model_name=self._model_name)
self._processed_request_count = end_stat.model_stats[
0].inference_stats.success.count - start_stat.model_stats[
0].inference_stats.success.count
# 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(grpcclient.InferInput('INPUT0', [1, 16], "BYTES"))
inputs[-1].set_shared_memory("input0_data", input0_byte_size)
inputs.append(grpcclient.InferInput('INPUT1', [1, 16], "BYTES"))
inputs[-1].set_shared_memory("input1_data", input1_byte_size)
outputs = []
outputs.append(grpcclient.InferRequestedOutput('OUTPUT0'))
outputs[-1].set_shared_memory("output0_data", output0_byte_size)
outputs.append(grpcclient.InferRequestedOutput('OUTPUT1'))
outputs[-1].set_shared_memory("output1_data", output1_byte_size)
results = triton_client.infer(model_name=model_name,
inputs=inputs,
outputs=outputs)
FLAGS = parser.parse_args()
try:
triton_client = tritongrpcclient.InferenceServerClient(
url=FLAGS.url, verbose=FLAGS.verbose)
except Exception as e:
print("channel creation failed: " + str(e))
sys.exit()
model_name = "yolov4"
# Infer
inputs = []
outputs = []
# the built engine with input NCHW
inputs.append(tritongrpcclient.InferInput("data", [1, 3, 608, 608],
"FP32"))
# Initialize the data
image_obj = Image("image_id", raw_image_path=FLAGS.img)
ori_w, ori_h = image_obj.pil_image_obj.size
image_frame, scale_ratio = preprocess(image_obj.pil_image_obj,
input_image_shape=(608, 608))
inputs[0].set_data_from_numpy(image_frame)
outputs.append(tritongrpcclient.InferRequestedOutput("prob"))
# Test with outputs
results = triton_client.infer(model_name=model_name,
inputs=inputs,
outputs=outputs,
headers={"test": "1"})
for npzfile in sorted(glob.glob('/hgcal_testdata/*.npz')):
print(npzfile)
inputs = []
outputs = []
with np.load(npzfile) as data:
x = data['X'].astype(np.float32)
edge_index = build_edge_index(x.shape[0], data['Ri_rows'],
data['Ri_cols'], data['Ro_rows'],
data['Ro_cols'])
print(x.shape, edge_index.shape)
nnodes = x.shape[0]
nedges = edge_index.shape[1]
inputs.append(tritongrpcclient.InferInput('x__0', [nnodes, 5], 'FP32'))
inputs.append(
tritongrpcclient.InferInput('edge_index__1', [2, nedges], "INT64"))
inputs[0].set_data_from_numpy(x)
inputs[1].set_data_from_numpy(edge_index)
outputs.append(tritongrpcclient.InferRequestedOutput('output__0'))
results = triton_client.infer(model_name=model_name,
inputs=inputs,
outputs=outputs)
output0_data = results.as_numpy('output__0')
print(output0_data)
del output0_data
def check_sequence_async(client_metadata,
trial,
model_name,
input_dtype,
steps,
timeout_ms=DEFAULT_TIMEOUT_MS,
sequence_name="<unknown>"):
"""Perform sequence of inferences using async run. The 'steps' holds
a list of tuples, one for each inference with format:
(flag_str, value, expected_result, delay_ms)
"""
if (("savedmodel" in trial) or ("graphdef" in trial) or
("custom" in trial) or ("plan" in trial)):
tensor_shape = (
1,
1,
)
else:
assert False, "unknown trial type: " + trial
triton_client = client_metadata[0]
sequence_id = client_metadata[1]
# Execute the sequence of inference...
seq_start_ms = int(round(time.time() * 1000))
user_data = UserData()
# Ensure there is no running stream
triton_client.stop_stream()
triton_client.start_stream(partial(completion_callback, user_data))
sent_count = 0
for flag_str, value, expected_result, delay_ms in steps:
seq_start = False
seq_end = False
if flag_str is not None:
seq_start = ("start" in flag_str)
seq_end = ("end" in flag_str)
if input_dtype == np.object_:
in0 = np.full(tensor_shape, value, dtype=np.int32)
in0n = np.array([str(x) for x in in0.reshape(in0.size)],
dtype=object)
in0 = in0n.reshape(tensor_shape)
else:
in0 = np.full(tensor_shape, value, dtype=input_dtype)
inputs = [
grpcclient.InferInput("INPUT", tensor_shape,
np_to_triton_dtype(input_dtype)),
]
inputs[0].set_data_from_numpy(in0)
triton_client.async_stream_infer(model_name,
inputs,
sequence_id=sequence_id,
sequence_start=seq_start,
sequence_end=seq_end)
sent_count += 1
if delay_ms is not None:
time.sleep(delay_ms / 1000.0)
# Process the results in order that they were sent
result = None
processed_count = 0
while processed_count < sent_count:
(results, error) = user_data._completed_requests.get()
if error is not None:
raise error
(_, value, expected, _) = steps[processed_count]
processed_count += 1
if timeout_ms != None:
now_ms = int(round(time.time() * 1000))
if (now_ms - seq_start_ms) > timeout_ms:
raise TimeoutException(
"Timeout expired for {}".format(sequence_name))
result = results.as_numpy("OUTPUT")[0][0]
if FLAGS.verbose:
print("{} {}: + {} = {}".format(sequence_name, sequence_id, value,
result))
if expected is not None:
if input_dtype == np.object_:
assert int(
result
) == expected, "{}: expected result {}, got {}".format(
sequence_name, expected, int(result))
else:
assert result == expected, "{}: expected result {}, got {}".format(
sequence_name, expected, result)
triton_client.stop_stream()
action="store_true",
default=False,
help="Use fp16 precision for input data",
)
FLAGS = parser.parse_args()
triton_client = tritongrpcclient.InferenceServerClient(
url=FLAGS.triton_server_url, verbose=FLAGS.verbose
)
dataloader = get_data_loader(FLAGS.batch_size, data_path=FLAGS.inference_data)
inputs = []
inputs.append(
tritongrpcclient.InferInput(
"input__0",
[FLAGS.batch_size, 3, 224, 224],
"FP16" if FLAGS.fp16 else "FP32",
)
)
outputs = []
outputs.append(tritongrpcclient.InferRequestedOutput("output__0"))
all_img = 0
cor_img = 0
result_prev = None
for image, target in tqdm(dataloader):
if FLAGS.fp16:
image = image.half()
inputs[0].set_data_from_numpy(image.numpy())
