As I've been reading about tensorflow lately I feel like I'm missing something regarding distributed processing. How can Tensorflow 'scale up' easily if you are outside of Google? We have big datasets that I want to run learning on but it seems awkward to do with tensorflow. We're big enough that the team managing our cluster is separate than development and it is a huge pain if we need them to go install tools on each node. Even with Spark support it seems like the tensorflow python libraries need to be set up on each machine in the cluster ahead of time.
Am I missing something?