title: Google Cloud ML and GCS Bucket issues
body: <p>I'm using open source Tensorflow implementations of research papers, for example <a href="https://github.com/carpedm20/DCGAN-tensorflow" rel="nofollow noreferrer">DCGAN-tensorflow</a>. Most of the libraries I'm using are configured to train the model locally, but I want to use Google Cloud ML to train the model since I don't have a GPU on my laptop. I'm finding it difficult to change the code to support GCS buckets. At the moment, I'm saving my logs and models to /tmp and then running a 'gsutil' command to copy the directory to gs://my-bucket at the end of training (<a href="https://github.com/GoogleCloudPlatform/cloudml-samples/blob/master/flowers/trainer/task.py#L432" rel="nofollow noreferrer">example here</a>). If I try saving the model directly to gs://my-bucket it never shows up.</p>
  
  <p>As for training data, one of the tensorflow samples copies data from GCS to /tmp for training (<a href="https://github.com/GoogleCloudPlatform/cloudml-samples/blob/master/flowers/trainer/task.py#L463" rel="nofollow noreferrer">example here</a>), but this only works when the dataset is small. I want to use celebA, and it is too large to copy to /tmp every run. Is there any documentation or guides for how to go about updating code that trains locally to use Google Cloud ML?</p>
  
  <p>The implementations are running various versions of Tensorflow, mainly .11 and .12</p>