Using Training TFRecords that are stored on Google Cloud

问题

My goal is to use training data (format: tfrecords) stored on Google Cloud storage when I run my Tensorflow Training App, locally. (Why locally? : I am testing before I turn it into a training package for Cloud ML)

Based on this thread I shouldn't have to do anything since the underlying Tensorflow API's should be able to read a gs://(url)

However thats not the case and the errors I see are of the format:

2017-06-06 15:38:55.589068: I tensorflow/core/platform/cloud/retrying_utils.cc:77] The operation failed and will be automatically retried in 1.38118 seconds (attempt 1 out of 10), caused by: Unavailable: Error executing an HTTP request (HTTP response code 0, error code 6, error message 'Couldn't resolve host 'metadata'')

2017-06-06 15:38:56.976396: I tensorflow/core/platform/cloud/retrying_utils.cc:77] The operation failed and will be automatically retried in 1.94469 seconds (attempt 2 out of 10), caused by: Unavailable: Error executing an HTTP request (HTTP response code 0, error code 6, error message 'Couldn't resolve host 'metadata'')

2017-06-06 15:38:58.925964: I tensorflow/core/platform/cloud/retrying_utils.cc:77] The operation failed and will be automatically retried in 2.76491 seconds (attempt 3 out of 10), caused by: Unavailable: Error executing an HTTP request (HTTP response code 0, error code 6, error message 'Couldn't resolve host 'metadata'')

I'm not able to follow where I have to begin debugging this error.

Here is a snippet that reproduced the problem and also shows the tensorflow API's that I am using.

def _preprocess_features(features):
        """Function that returns preprocessed images"""

def _parse_single_example_from_tfrecord(value):
    features = (
        tf.parse_single_example(value,
                                features={'image_raw': tf.FixedLenFeature([], tf.string),
                                          'label': tf.FixedLenFeature([model_config.LABEL_SIZE], tf.int64)
                                          })
        )
    return features

def _read_and_decode_tfrecords(filename_queue):
    reader = tf.TFRecordReader()
    # Point it at the filename_queue
    _, value = reader.read(filename_queue)
    features = _parse_single_example_from_tfrecord(value)
    # decode the binary string image data
    image, label = _preprocess_features(features)
    return image, label

def test_tfread(filelist):
  train_filename_queue = (
    tf.train.string_input_producer(filelist,
                                   num_epochs=None,
                                   shuffle=True))
  image, label = (
    _read_and_decode_tfrecords(train_filename_queue))
  return image

images= test_tfread(["gs://test-bucket/t.tfrecords"])
sess = tf.Session(config=tf.ConfigProto(
                allow_soft_placement=True,
                log_device_placement=True))
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
try:
  for step in range(model_config.MAX_STEPS):
      _ = sess.run([images])
finally:
  # When done, ask the threads to stop.
  coord.request_stop()
# Finally, wait for them to join (i.e. cleanly shut down)
coord.join(threads)

回答1:

Try executing the following command

gcloud auth application-default login

来源：https://stackoverflow.com/questions/44401088/using-training-tfrecords-that-are-stored-on-google-cloud