本文整理汇总了Python中grpc.beta.implementations.insecure_channel方法的典型用法代码示例。如果您正苦于以下问题:Python implementations.insecure_channel方法的具体用法?Python implementations.insecure_channel怎么用?Python implementations.insecure_channel使用的例子?那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类grpc.beta.implementations
的用法示例。
在下文中一共展示了implementations.insecure_channel方法的15个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: run
# 需要导入模块: from grpc.beta import implementations [as 别名]
# 或者: from grpc.beta.implementations import insecure_channel [as 别名]
def run(host, port, test_json, model_name, signature_name):
# channel = grpc.insecure_channel('%s:%d' % (host, port))
channel = implementations.insecure_channel(host, port)
stub = prediction_service_pb2.beta_create_PredictionService_stub(channel)
with open(test_json, "r") as frobj:
content = json.load(frobj)
print(len(content), "======")
start = time.time()
for i, input_dict in enumerate(content):
request = prepare_grpc_request(model_name, signature_name, input_dict)
result = stub.Predict(request, 10.0)
print(result, i)
end = time.time()
time_diff = end - start
print('time elapased: {}'.format(time_diff))
示例2: main
# 需要导入模块: from grpc.beta import implementations [as 别名]
# 或者: from grpc.beta.implementations import insecure_channel [as 别名]
def main():
host = FLAGS.host
port = FLAGS.port
model_name = FLAGS.model_name
model_version = FLAGS.model_version
request_timeout = FLAGS.request_timeout
# Generate inference data
features = numpy.asarray(
[1, 2, 3, 4, 5, 6, 7, 8, 9])
features_tensor_proto = tf.contrib.util.make_tensor_proto(features,
dtype=tf.float32)
# Create gRPC client and request
channel = implementations.insecure_channel(host, port)
stub = prediction_service_pb2.beta_create_PredictionService_stub(channel)
request = predict_pb2.PredictRequest()
request.model_spec.name = model_name
request.model_spec.version.value = model_version
request.inputs['features'].CopyFrom(features_tensor_proto)
# Send request
result = stub.Predict(request, request_timeout)
print(result)
示例3: main
# 需要导入模块: from grpc.beta import implementations [as 别名]
# 或者: from grpc.beta.implementations import insecure_channel [as 别名]
def main():
# Generate inference data
keys = numpy.asarray([1, 2, 3, 4])
keys_tensor_proto = tf.contrib.util.make_tensor_proto(keys, dtype=tf.int32)
features = numpy.asarray(
[[1, 2, 3, 4, 5, 6, 7, 8, 9], [1, 1, 1, 1, 1, 1, 1, 1, 1],
[9, 8, 7, 6, 5, 4, 3, 2, 1], [9, 9, 9, 9, 9, 9, 9, 9, 9]])
features_tensor_proto = tf.contrib.util.make_tensor_proto(
features, dtype=tf.float32)
# Create gRPC client
channel = implementations.insecure_channel(FLAGS.host, FLAGS.port)
stub = prediction_service_pb2.beta_create_PredictionService_stub(channel)
request = predict_pb2.PredictRequest()
request.model_spec.name = FLAGS.model_name
if FLAGS.model_version > 0:
request.model_spec.version.value = FLAGS.model_version
if FLAGS.signature_name != "":
request.model_spec.signature_name = FLAGS.signature_name
request.inputs["keys"].CopyFrom(keys_tensor_proto)
request.inputs["features"].CopyFrom(features_tensor_proto)
# Send request
result = stub.Predict(request, FLAGS.request_timeout)
print(result)
示例4: main
# 需要导入模块: from grpc.beta import implementations [as 别名]
# 或者: from grpc.beta.implementations import insecure_channel [as 别名]
def main():
host = FLAGS.host
port = FLAGS.port
model_name = FLAGS.model_name
model_version = FLAGS.model_version
request_timeout = FLAGS.request_timeout
# Generate inference data
features = numpy.asarray(
[[1, 2, 3, 4], [5, 6, 7, 8]])
features_tensor_proto = tf.contrib.util.make_tensor_proto(features,
dtype=tf.float32)
# Create gRPC client and request
channel = implementations.insecure_channel(host, port)
stub = prediction_service_pb2.beta_create_PredictionService_stub(channel)
request = predict_pb2.PredictRequest()
request.model_spec.name = model_name
if model_version > 0:
request.model_spec.version.value = model_version
request.inputs['state'].CopyFrom(features_tensor_proto)
# Send request
result = stub.Predict(request, request_timeout)
print(result)
示例5: create_stub
# 需要导入模块: from grpc.beta import implementations [as 别名]
# 或者: from grpc.beta.implementations import insecure_channel [as 别名]
def create_stub(grpc_apiserver_host):
"""Creates a grpc_service.CallHandler stub.
Args:
grpc_apiserver_host: String, the host that CallHandler service listens on.
Should be in the format of hostname:port.
Returns:
A CallHandler stub.
"""
# See http://www.grpc.io/grpc/python/_modules/grpc/beta/implementations.html:
# the method insecure_channel requires explicitly two parameters (host, port)
# here our host already contain port number, so the second parameter is None.
prefix = 'http://'
if grpc_apiserver_host.startswith(prefix):
grpc_apiserver_host = grpc_apiserver_host[len(prefix):]
channel = implementations.insecure_channel(grpc_apiserver_host, None)
return grpc_service_pb2.beta_create_CallHandler_stub(channel)
示例6: _do_local_inference
# 需要导入模块: from grpc.beta import implementations [as 别名]
# 或者: from grpc.beta.implementations import insecure_channel [as 别名]
def _do_local_inference(host, port, serialized_examples, model_name):
"""Performs inference on a model hosted by the host:port server."""
channel = implementations.insecure_channel(host, int(port))
stub = prediction_service_pb2.beta_create_PredictionService_stub(channel)
request = predict_pb2.PredictRequest()
# request.model_spec.name = 'chicago_taxi'
request.model_spec.name = model_name
request.model_spec.signature_name = 'predict'
tfproto = tf.contrib.util.make_tensor_proto([serialized_examples],
shape=[len(serialized_examples)],
dtype=tf.string)
# The name of the input tensor is 'examples' based on
# https://github.com/tensorflow/tensorflow/blob/r1.9/tensorflow/python/estimator/export/export.py#L290
request.inputs['examples'].CopyFrom(tfproto)
print(stub.Predict(request, _LOCAL_INFERENCE_TIMEOUT_SECONDS))
示例7: _create_stub
# 需要导入模块: from grpc.beta import implementations [as 别名]
# 或者: from grpc.beta.implementations import insecure_channel [as 别名]
def _create_stub(server):
host, port = server.split(":")
channel = implementations.insecure_channel(host, int(port))
# TODO(bgb): Migrate to GA API.
return prediction_service_pb2.beta_create_PredictionService_stub(channel)
示例8: main
# 需要导入模块: from grpc.beta import implementations [as 别名]
# 或者: from grpc.beta.implementations import insecure_channel [as 别名]
def main():
host = FLAGS.host
port = FLAGS.port
model_name = FLAGS.model_name
model_version = FLAGS.model_version
request_timeout = FLAGS.request_timeout
# Generate inference data
keys = numpy.asarray([1, 2, 3])
keys_tensor_proto = tf.contrib.util.make_tensor_proto(keys, dtype=tf.int32)
features_tensor_proto = tf.contrib.util.make_tensor_proto(img,
dtype=tf.float32)
# Create gRPC client and request
channel = implementations.insecure_channel(host, port)
stub = prediction_service_pb2.beta_create_PredictionService_stub(channel)
request = predict_pb2.PredictRequest()
request.model_spec.name = model_name
if model_version > 0:
request.model_spec.version.value = model_version
request.inputs['inputs'].CopyFrom(features_tensor_proto)
request.model_spec.signature_name = 'predict'
#request.inputs['features'].CopyFrom(features_tensor_proto)
# Send request
result = stub.Predict(request, request_timeout)
response = numpy.array(result.outputs['outputs'].float_val)
prediction = numpy.argmax(response)
print(prediction)
示例9: do_inference
# 需要导入模块: from grpc.beta import implementations [as 别名]
# 或者: from grpc.beta.implementations import insecure_channel [as 别名]
def do_inference(hostport, work_dir, concurrency, num_tests):
"""Tests PredictionService with concurrent requests.
Args:
hostport: Host:port address of the PredictionService.
work_dir: The full path of working directory for test data set.
concurrency: Maximum number of concurrent requests.
num_tests: Number of test images to use.
Returns:
The classification error rate.
Raises:
IOError: An error occurred processing test data set.
"""
test_data_set = mnist_input_data.read_data_sets(work_dir).test
host, port = hostport.split(':')
channel = implementations.insecure_channel(host, int(port))
stub = prediction_service_pb2.beta_create_PredictionService_stub(channel)
result_counter = _ResultCounter(num_tests, concurrency)
for _ in range(num_tests):
request = predict_pb2.PredictRequest()
request.model_spec.name = 'mnist'
request.model_spec.signature_name = 'predict'
image, label = test_data_set.next_batch(1)
request.inputs['inputs'].CopyFrom(
tf.contrib.util.make_tensor_proto(image[0], shape=[1, 28, 28, 1]))
result_counter.throttle()
result_future = stub.Predict.future(request, 5.0) # 5 seconds
result_future.add_done_callback(
_create_rpc_callback(label[0], result_counter))
return result_counter.get_error_rate()
示例10: main
# 需要导入模块: from grpc.beta import implementations [as 别名]
# 或者: from grpc.beta.implementations import insecure_channel [as 别名]
def main(_):
if not FLAGS.text:
raise ValueError("No --text provided")
host, port = FLAGS.server.split(':')
channel = implementations.insecure_channel(host, int(port))
stub = prediction_service_pb2.beta_create_PredictionService_stub(channel)
request = Request(FLAGS.text, FLAGS.ngrams)
result = stub.Classify(request, 10.0) # 10 secs timeout
print(result)
示例11: run
# 需要导入模块: from grpc.beta import implementations [as 别名]
# 或者: from grpc.beta.implementations import insecure_channel [as 别名]
def run():
channel = implementations.insecure_channel('localhost', 50051)
stub = helloworld_pb2.beta_create_Greeter_stub(channel)
response = stub.SayHello(helloworld_pb2.HelloRequest(name='you'), _TIMEOUT_SECONDS)
print "Greeter client received: " + response.message
示例12: do_inference
# 需要导入模块: from grpc.beta import implementations [as 别名]
# 或者: from grpc.beta.implementations import insecure_channel [as 别名]
def do_inference(num_tests, concurrency=1):
channel = implementations.insecure_channel(host, int(port))
stub = prediction_service_pb2.beta_create_PredictionService_stub(channel)
coord = _Coordinator(num_tests, concurrency)
for _ in range(num_tests):
# dummy audio
duration, sr, n_fft, win_length, hop_length, n_mels, max_db, min_db = 4, 16000, 512, 512, 128, 80, 35, -55
filename = librosa.util.example_audio_file()
wav = read_wav(filename, sr=sr, duration=duration)
mel = wav2melspec_db(wav, sr, n_fft, win_length, hop_length, n_mels)
mel = normalize_db(mel, max_db=max_db, min_db=min_db)
mel = mel.astype(np.float32)
mel = np.expand_dims(mel, axis=0) # single batch
n_timesteps = sr / hop_length * duration + 1
# build request
request = predict_pb2.PredictRequest()
request.model_spec.name = 'voice_vector'
request.model_spec.signature_name = 'predict'
request.inputs['x'].CopyFrom(tf.contrib.util.make_tensor_proto(mel, shape=[1, n_timesteps, n_mels]))
coord.throttle()
# send asynchronous response (recommended. use this.)
result_future = stub.Predict.future(request, 10.0) # timeout
result_future.add_done_callback(_create_rpc_callback(coord))
# send synchronous response (NOT recommended)
# result = stub.Predict(request, 5.0)
coord.wait_all_done()
示例13: main
# 需要导入模块: from grpc.beta import implementations [as 别名]
# 或者: from grpc.beta.implementations import insecure_channel [as 别名]
def main(_):
host, port = FLAGS.server.split(':')
channel = implementations.insecure_channel(host, int(port))
stub = prediction_service_pb2.beta_create_PredictionService_stub(channel)
# Send request
image = tf.gfile.FastGFile(FLAGS.image, 'rb').read()
request = predict_pb2.PredictRequest()
request.model_spec.name = 'tensorflow-serving'
request.model_spec.signature_name = tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY
request.inputs['image'].CopyFrom(tf.contrib.util.make_tensor_proto(image))
#request.inputs['input'].CopyFrom()
result = stub.Predict(request, 10.0) # 10 secs timeout
print(result)
示例14: _open_tf_server_channel
# 需要导入模块: from grpc.beta import implementations [as 别名]
# 或者: from grpc.beta.implementations import insecure_channel [as 别名]
def _open_tf_server_channel(server_name, server_port):
channel = implementations.insecure_channel(
server_name,
int(server_port))
stub = prediction_service_pb2.beta_create_PredictionService_stub(channel)
return stub
示例15: connect
# 需要导入模块: from grpc.beta import implementations [as 别名]
# 或者: from grpc.beta.implementations import insecure_channel [as 别名]
def connect(self):
for i in range(self.pool_size):
channel = implementations.insecure_channel(self.host, self.port)
stub = server_pb2.beta_create_SimpleService_stub(channel)
# we need to make channels[i] == stubs[i]->channel
self.channels.append(channel)
self.stubs.append(stub)