The State of Machine Learning Frameworks
I thought the article was a good read and compared the two frameworks with only small hints of personal bias, but one point about industry changing to use pytorch because of researchers already knowing it seems like wishful thinking. Unless PyTorch addresses its mobile and serving issues it is simply not a great choice for many production situations. This article actually influenced me to stick with TF instead of learning PyTorch due to my industry needs.<p>Additionally I think tensorflow opt in by default for eager execution is fine maybe good even.
Many models are relatively simple and I doubt the gains for rewriting them to utilize the execution graph will be worth it when with the keras frontend you can just dump the h5py model and run it from there which many companies already do.<p>Rewriting will only be an issue for sufficiently complex models and at that point I imagine competent ML professionals will have baked the time for that into the estimate of the engineering costs.