Not sure where the downvote came from - maybe I can explain a bit. If it was from my last part - I'm actually asking for clarification because we've often dis

Not sure where the downvote came from - maybe I can explain a bit. If it was from my last part - I'm actually asking for clarification because we've often discovered this when people say they've tried us. A lot of folks don't spend too much time on a framework and I don't blame them. They are looking for a tool to get the job done and move on 99% of the time. That's more than fair. The bulk of these folks also tend to be people who are mildly curious coming from python, where most deep learning practitioners are. That being said: I was serious about each node being independent for spark. Our linear algebra library runs as a spark job on each executor/slave node. We've built in our own resource management via javacpp that handles memory allocation for the cuda buffers as well as cudnn. The driver for spark then tracks the coefficients across spark rdd partitions which allows us to synchronize updates. Look out for a blog post from us on parallelforall (nvidia's blog) here soon that explains how this works.