Google Storage (gs) wrapper file input/out for Cloud ML?

Do it like this:

from tensorflow.python.lib.io import file_io

with file_io.FileIO('gs://.....', mode='w+') as f:
    cPickle.dump(self.words, f)

Or you can read pickle file in like this:

file_stream = file_io.FileIO(train_file, mode='r')
x_train, y_train, x_test, y_test  = pickle.load(file_stream)

One solution is to copy all of the data to local disk when the program starts up. You can do that using gsutil inside the Python script that gets run, something like:

vocab_file = 'vocab.pickled'
subprocess.check_call(['gsutil', '-m' , 'cp', '-r',
                       os.path.join('gs://path/to/', vocab_file), '/tmp'])

with open(os.path.join('/tmp', vocab_file), 'wb') as f:
  cPickle.dump(self.words, f)

And if you have any outputs, you can write them to local disk and gsutil rsync them. (But, be careful to handle restarts correctly, because you may be put on a different machine).

The other solution is to monkey patch open (Note: untested):

import __builtin__

# NB: not all modes are compatible; should handle more carefully.
# Probably should be reported on
# https://github.com/tensorflow/tensorflow/issues/4357
def new_open(name, mode='r', buffering=-1):
  return file_io.FileIO(name, mode)

__builtin__.open = new_open

Just be sure to do that before any module actually tries to read from GCS.

apache_beam has the gcsio module which can be used to return a standard Python file object to read/write GCS objects. You can use this object with any method that works with Python file objects. For example

def open_local_or_gcs(path, mode):
  """Opens the given path."""
  if path.startswith('gs://'):
    try:
      return gcsio.GcsIO().open(path, mode)
    except Exception as e:  # pylint: disable=broad-except
      # Currently we retry exactly once, to work around flaky gcs calls.
      logging.error('Retrying after exception reading gcs file: %s', e)
      time.sleep(10)
      return gcsio.GcsIO().open(path, mode)
  else:
    return open(path, mode)

 with open_local_or_gcs(vocab_file, 'wb') as f:
   cPickle.dump(self.words, f)