[turinturambar]

Both points are true: TF is obviously designed for scalable, distributed training, but it also heavily tied to Google's compute infrastructure (less so all the time, of course, but now it's also being closely tied to Google Cloud). So while I disagree with my colleague* that TF is "slow" or "not designed for distributed training," I support the slightly different (and implicit) argument that there are some settings (often in enterprise, I am learning) where it might not be as good a fit as other frameworks (e.g., DL4J, Caffe, whatever).

* Disclosure: I work with Skymind and contribute to DL4J, and I also use TensorFlow/Theano/keras heavily in my PhD research. I am an equal opportunity framework guy. ;)

[Chronic9q]

To all these Skymind kool-aid drinkers, I won't bother arguing with you. I'll let the tensorflow vs DL4J usage numbers tell the story.

Spoiler: Tensorflow wins.

[vonnik]

Spoiler: 95% of them are Udacity students without experience or budgets.