Tensorflow服务重新训练开始

Question

我正在尝试按照本指南提供我的再培训初始模型(您可能还会看到本指南,它解释了如何重新训练开始).我已经修改了retrain.py来导出我的模型,如下所示:

... # Same as in the original script:
# Set up the pre-trained graph.
maybe_download_and_extract()
graph, bottleneck_tensor, jpeg_data_tensor, resized_image_tensor = (create_inception_graph())
... # Same as in the original script:
# Add the new layer that we'll be training.
(train_step, cross_entropy, bottleneck_input, ground_truth_input, final_tensor) = add_final_training_ops(len(image_lists.keys()),
                                         FLAGS.final_tensor_name,
                                         bottleneck_tensor)
... # Added at the end of the original script:
# Export model
with graph.as_default():
    export_path = sys.argv[-1]
    print('Exporting trained model to', export_path)
    saver = tf.train.Saver(sharded=True)
    model_exporter = exporter.Exporter(saver)
    signature = exporter.classification_signature(input_tensor=jpeg_data_tensor, scores_tensor=final_tensor)
    model_exporter.init(sess.graph.as_graph_def(), default_graph_signature=signature)
    model_exporter.export(export_path, tf.constant(FLAGS.export_version), sess)
    print('Done exporting!')

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

导出我的模型后,我开始运行服务器:

/serving/bazel-bin/tensorflow_serving/example/inception_inference --port=9000 EXPORT_DIR &> inception_log &

服务器日志文件(inception_log)包含:

I tensorflow_serving/core/basic_manager.cc:190] Using InlineExecutor for BasicManager.
I tensorflow_serving/example/inception_inference.cc:384] Waiting for models to be loaded...
I tensorflow_serving/sources/storage_path/file_system_storage_path_source.cc:147] File-system polling found servable version {name: default version: 1} at path /tf_files/scope/export/00000001
I external/org_tensorflow/tensorflow/contrib/session_bundle/session_bundle.cc:129] Attempting to load a SessionBundle from: /tf_files/scope/export/00000001
I tensorflow_serving/example/inception_inference.cc:384] Waiting for models to be loaded...
I tensorflow_serving/sources/storage_path/file_system_storage_path_source.cc:147] File-system polling found servable version {name: default version: 1} at path /tf_files/scope/export/00000001
I external/org_tensorflow/tensorflow/contrib/session_bundle/session_bundle.cc:106] Running restore op for SessionBundle
I external/org_tensorflow/tensorflow/contrib/session_bundle/session_bundle.cc:203] Done loading SessionBundle
I tensorflow_serving/example/inception_inference.cc:350] Running...
I tensorflow_serving/sources/storage_path/file_system_storage_path_source.cc:147] File-system polling found servable version {name: default version: 1} at path /tf_files/scope/export/00000001
I tensorflow_serving/sources/storage_path/file_system_storage_path_source.cc:147] File-system polling found servable version {name: default version: 1} at path /tf_files/scope/export/00000001
I tensorflow_serving/sources/storage_path/file_system_storage_path_source.cc:147] File-system polling found servable version {name: default version: 1} at path /tf_files/scope/export/00000001
... 

最后,我运行客户端,我收到以下错误:

/serving/bazel-bin/tensorflow_serving/example/inception_client --server=localhost:9000 --image=TEST_IMG
D0805 09:10:46.208704633     200 ev_posix.c:101]             Using polling engine: poll
Traceback (most recent call last):
  File "/serving/bazel-bin/tensorflow_serving/example/inception_client.runfiles/tensorflow_serving/example/inception_client.py", line 53, in <module>
    tf.app.run()
  File "/serving/bazel-bin/tensorflow_serving/example/inception_client.runfiles/external/org_tensorflow/tensorflow/python/platform/app.py", line 30, in run
    sys.exit(main(sys.argv))
  File "/serving/bazel-bin/tensorflow_serving/example/inception_client.runfiles/tensorflow_serving/example/inception_client.py", line 48, in main
    result = stub.Classify(request, 10.0)  # 10 secs timeout
  File "/usr/local/lib/python2.7/dist-packages/grpc/beta/_client_adaptations.py", line 300, in __call__
    self._request_serializer, self._response_deserializer)
  File "/usr/local/lib/python2.7/dist-packages/grpc/beta/_client_adaptations.py", line 198, in _blocking_unary_unary
    raise _abortion_error(rpc_error_call)
    grpc.framework.interfaces.face.face.AbortionError: AbortionError(code=StatusCode.INTERNAL, details="FetchOutputs node : not found")
E0805 09:10:47.129263239     200 chttp2_transport.c:1810]    close_transport: {"created":"@1470388247.129230608","description":"FD shutdown","file":"src/core/lib/iomgr/ev_poll_posix.c","file_line":427}

任何有关此事的建议或指导都将不胜感激.

Answer 1

因此，根据我的经验，tensorflow 网站上的链接只是完全服务于该模型的一种方式。提供模型的更好方法是从 Flask 和 Kubernetes 提供服务，因为它是比所有张量流服务基础设施更轻的客户端，但这假设您拥有的数据量不是很大（>100） QPS），尽管您可以使用 Flask 和 Kubernetes 来提供具有该负载的 Inception，但按照这个速度，我会选择内联解决方案。

您可以从远程服务提供它，这可行，但根据您的基础设施，您“也可以”在流作业中提供该模型，该作业通过 apache_beam.DoFn 推送您的请求，然后将其输出回 MQ你的工作就是倾听。这只是另一种解决方案。希望这可以帮助。

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import logging 
import tensorflow as tf
import numpy as np
import apache_beam as beam


class InferenceFn(beam.DoFn):

  def __init__(self, model_dict):
    super(InferenceFn, self).__init__()
    self.model_dict = model_dict
    self.graph = None
    self.create_graph()


  def create_graph(self):
    if not tf.gfile.FastGFile(self.model_dict['model_full_path']):
      self.download_model_file()
    with tf.Graph().as_default() as graph:
      with tf.gfile.FastGFile(self.model_dict['model_full_path'], 'rb') as f:
        graph_def = tf.GraphDef()
        graph_def.ParseFromString(f.read())
        _ = tf.import_graph_def(graph_def, name='')
    self.graph = graph

  def start_bundle(self):
    """Prevents graph object serialization until serving. Required for GCP Serving"""
    self.create_graph()

  def process(self, element):
    """Core Processing Fn for Apache Beam."""
    try:
      with tf.Session(graph=self.graph) as sess:
        if not tf.gfile.Exists(element):
          tf.logging.fatal('File does not exist %s', element)
          raise ReferenceError("Couldnt Find the image {}".format(element))
        data = tf.gfile.FastGFile(element, 'rb').read()
        output_tensor = sess.graph.get_tensor_by_name(self.model_dict['output_tensor_name'])
        predictions = sess.run(softmax_tensor, {self.model_dict['input_tensor_name']: data})
        predictions = np.squeeze(predictions)
        yield str(predictions)
    except Exception:
      logging.error("We hit an error in inference on {}".format(element))