I'm using open source Tensorflow implementations of research papers, for example DCGAN-tensorflow. 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 (example here). If I try saving the model directly to gs://my-bucket it never shows up.
As for training data, one of the tensorflow samples copies data from GCS to /tmp for training (example here), 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?
The implementations are running various versions of Tensorflow, mainly .11 and .12
See Question&Answers more detail:
os 与恶龙缠斗过久,自身亦成为恶龙；凝视深渊过久,深渊将回以凝视…
