Running Google Dataflow locally for Image Recognition - machine-learning

Question

I am currently following this tutorial for transfer learning using tensorflow and Google Cloud Platform.
https://cloud.google.com/blog/big-data/2016/12/how-to-train-and-classify-images-using-google-cloud-machine-learning-and-cloud-dataflow
It works perfectly on the cloud with my own data when I use their sample code
# Preprocess the eval set.
python trainer/preprocess.py \
--input_dict "$DICT_FILE" \
--input_path "gs://cloud-ml-data/img/flower_photos/eval_set.csv" \
--output_path "${GCS_PATH}/preproc/eval" \
--cloud
I get all the preprocessing, training and deployment done.
However, I would like to be able to run it locally so that I can make changes in the code and debug it more effieciently:
In the code it states that
To run this pipeline locally run the above command without --cloud.
So it would read:
# Preprocess the eval set.
python trainer/preprocess.py \
--input_dict "$DICT_FILE" \
--input_path "gs://cloud-ml-data/img/flower_photos/eval_set.csv" \
--output_path "${GCS_PATH}/preproc/eval"
I tried to run this code with input_dict, input_path and output_path set to a cloud storage path, as well as being a path to a file on my local machine.
However I get the error:
tensorflow/core/platform/cloud/google_auth_provider.cc:151] All attempts to get a Google authentication bearer token failed, returning an empty token. Retrieving token from files failed with "Unavailable: libcurl failed with error code 23: Failed writing body (91 != 196)". Retrieving token from GCE failed with "Unavailable: Unexpected response code 0".
So it seems to be an authentication issue:
The thing is that I do not have authentication problems when copying files from google cloud storage manually.
I already tried:
$ gcloud auth application-default login
but it doesn't change anything.
Does anyone have a solution for that?