Question

使用tensorflow服务时遇到了一些问题。

我使用张量流服务将我的tensorflow模型部署为RESTful API。但我怀疑服务器服务器是否支持多线程。我做过一些实验，但似乎没有。

我还注意到tensorflow_model_server有--tensorflow_session_parallelism选项，但使用该选项会使我的服务器更慢......

是否有使用tensorflow服务多线程的参考？

Answer 1

详细说明@ReInvent_IO提供的链接的内容，以防万一该链接将来无法使用。

相同的代码如下所示：

"""A client that talks to tensorflow_model_server loaded with mnist model.
The client downloads test images of mnist data set, queries the service with
such test images to get predictions, and calculates the inference error rate.
Typical usage example:
    mnist_client.py --num_tests=100 --server=localhost:9000
"""

from __future__ import print_function

import sys
import threading

# This is a placeholder for a Google-internal import.

import grpc
import numpy
import tensorflow as tf

from tensorflow_serving.apis import predict_pb2
from tensorflow_serving.apis import prediction_service_pb2_grpc
import mnist_input_data

将并发性的值设置为5，要求服务器运行5个不同的线程

tf.app.flags.DEFINE_integer('concurrency', 5,
                            'maximum number of concurrent inference requests')
tf.app.flags.DEFINE_integer('num_tests', 100, 'Number of test images')
tf.app.flags.DEFINE_string('server', '', 'PredictionService host:port')
tf.app.flags.DEFINE_string('work_dir', '/tmp', 'Working directory. ')
FLAGS = tf.app.flags.FLAGS


class _ResultCounter(object):
  """Counter for the prediction results."""

  def __init__(self, num_tests, concurrency):
    self._num_tests = num_tests
    self._concurrency = concurrency
    self._error = 0
    self._done = 0
    self._active = 0
    self._condition = threading.Condition()

  def inc_error(self):
    with self._condition:
      self._error += 1

  def inc_done(self):
    with self._condition:
      self._done += 1
      self._condition.notify()

  def dec_active(self):
    with self._condition:
      self._active -= 1
      self._condition.notify()

  def get_error_rate(self):
    with self._condition:
      while self._done != self._num_tests:
        self._condition.wait()
      return self._error / float(self._num_tests)

  def throttle(self):
    with self._condition:
      while self._active == self._concurrency:
        self._condition.wait()
      self._active += 1


def _create_rpc_callback(label, result_counter):
  """Creates RPC callback function.
  Args:
    label: The correct label for the predicted example.
    result_counter: Counter for the prediction result.
  Returns:
    The callback function.
  """
  def _callback(result_future):
    """Callback function.
    Calculates the statistics for the prediction result.
    Args:
      result_future: Result future of the RPC.
    """
    exception = result_future.exception()
    if exception:
      result_counter.inc_error()
      print(exception)
    else:
      sys.stdout.write('.')
      sys.stdout.flush()
      response = numpy.array(
          result_future.result().outputs['scores'].float_val)
      prediction = numpy.argmax(response)
      if label != prediction:
        result_counter.inc_error()
    result_counter.inc_done()
    result_counter.dec_active()
  return _callback


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
  channel = grpc.insecure_channel(hostport)
  stub = prediction_service_pb2_grpc.PredictionServiceStub(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_images'
    image, label = test_data_set.next_batch(1)
    request.inputs['images'].CopyFrom(
        tf.contrib.util.make_tensor_proto(image[0], shape=[1, image[0].size]))
    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()


def main(_):
  if FLAGS.num_tests > 10000:
    print('num_tests should not be greater than 10k')
    return
  if not FLAGS.server:
    print('please specify server host:port')
    return
  error_rate = do_inference(FLAGS.server, FLAGS.work_dir,
                            FLAGS.concurrency, FLAGS.num_tests)
  print('\nInference error rate: %s%%' % (error_rate * 100))


if __name__ == '__main__':
  tf.app.run()

tensorflow-serving是否支持多线程？

1 个答案: