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I think the notion that there's a "deep learning framework war raging" is pretty mistaken. It's not like there are different clans that don't talk to each other. The majority of people use multiple tools, often within the same project.
And what they use now may not be what they use in 6 months. This is especially true in research, where each project starts from scratch and lasts 2-3 months. For production applications, there is more of a need to settle on one framework and accrete capabilities over time.
So don't believe the FUD. It's typically just a marketing ploy to drive a message. Conflict sells I suppose.
Also becoming easier to use one for something like training and switching to another for inference within a production env. Exporting models has become much more mature recently.
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