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

Question

Google recently announced the Clould ML, https://cloud.google.com/ml/ and it's very useful. However, one limitation is that the input/out of a Tensorflow program should support gs://.

If we use all tensorflow APIS to read/write files, it should OK, since these APIs support gs://.

However, if we use native file IO APIs such as open, it does not work, because they don't understand gs://

For example:

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

This code won't work in Google Cloud ML.

However, modifying all native file IO APIs to tensorflow APIs or Google Storage Python APIs is really tedious. Is there any simple way to do this? Any wrappers to support google storage systems, gs:// on top of the native file IO?

As suggested here Pickled scipy sparse matrix as input data?, perhaps we can use file_io.read_file_to_string('gs://...'), but still this requrements significant code modifcation.

Answer 1

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)