That's extremely well said. While it certainly beats the alternative of rolling something like it yourself, I've struggled a bit trying to get in the flow of tf

That's extremely well said. While it certainly beats the alternative of rolling something like it yourself, I've struggled a bit trying to get in the flow of tf, pardon the expression. My impression is that it's an ecosystem that's developing incredibly rapidly with all the associated growing pains. Even for such a simple-on-the-surface task as feeding very large datasets into the graphs, there are many different ways, some deprecated already, some about to be deprecated by functionality not in 1.0, etc. It's also quite new, but the distributed execution, while awesome, still requires a huge amount of hand coding and tuning the machinery, reminding me quite a lot of just rolling the damn thing in MPI yourself. I'm very excited to see where distributed tf will be in a year or two, but it's a chore today. I'm really bullish on their Cloud ML product, a tool to auto-distribute the execution graphs and associated data ingestion, but the documentation and examples for that are a bit all over the place at the moment, and require writing models to conform to the newish Experiment API, which is also a bit underdocumented and underexampled. My frustration is likely only because of how awesome tensorflow actually is. It's new, but so promising that I want to use it for everything, regardless of the development roadmap!