CoreML supports Keras but not TensorFlow because Keras models form a well-structured subset of all possible TensorFlow graphs. It would be quite difficult to support completely arbitrary TensorFlow graphs, but supporting every Keras layer is relatively straightforward.

To answer your question: I had no knowledge of this ONIX project before the public announcement today. Speaking purely for myself, if I wanted to develop a universal model exchange format, the first step I would take would be to get in touch with the makers of the frameworks that sum to 80-90% of the market share -- TF, Keras, MXNet. But maybe such a strategy was thought to be superfluous in this case -- for instance, because ONIX may not actually be intended as a universal model exchange format.

To be fair, CNTK (BrainScript) has quite impressive list of features to support dynamic control structure (in a symbolic fashion, comparing to PyTorch which delegated much of the dynamic control structure to underlying language Python). I think Tensorflow and CNTK probably the only two frameworks pursued such implementation strategy. IMHO, looking back, supporting control structures may not be that useful (see the recent attention based models, all of them can be unroll'ed to ordinary graphs), but it is so interesting to implement!

Thanks! Haven't yet, but our TPMs are going to reach out for collaborations. I wish we were grad school mode where latency is <1 hour, but it pays to get things proper across multiple companies. Kindly stay tuned.