Running tensorflow model training from dataflow
There is nothing planned at this time.
If you can run the Tensorflow training on a single machine (it sounds like this is what you were doing with Spark) then it should be possible to do the training within a DoFn of a Dataflow pipeline.
I am playing around with tensorflow and today I have noticed that google also open-sourced Python SDK for their dataflow.
Currently when I need to train and evaluate several networks in parallel I usually use either luigi and run one model training after another or I use spark and I am performing each model training within the map step.
Whole this data processing is just a part of the pipeline.
I am wondering if there is or if there is planned something like perform tensorflow model training step inside of the dataflow pipeline? 
Is there currently some best practice around this? 
Or do I have to run each model setting within the map step?
I went through the documentation and for now it seems to be really vague, so I'm asking here if someone has some experience with this.