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Unlike Keras and Tensorflow, Gluon is Define-by-run Deeplearning framework like Pytorch, Chainer. Network definition/debugging/flexibility are really better with dynamic network (define-by-run). That's why Facebook seem to use Pytorch for research and caffe2 for deployment. Gluon/Mxnet can do both define-by-run with Gluon API and "standard" define-and-run with it's Module API.

I think you're both right here. Competition will force the other to innovate. I don't think end users lose by there being multiple interfaces even if fragmentation is ultimately what happens here.

Standard formats and interop will help fix that.

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