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I really enjoyed this write up. Thank you for putting it together. Even as a TF user, I feel it's a really fair assessment of TF vs PyTorch.

A quick observation that may not be 100% accurate but still worth mentioning: in some ways TF feels like it was written to solve large scale issues on day one. For example, when I started playing with the new TF 2.0 distribution strategies and dataset pipeline, I quickly got the sense that this thing was meant to move and ingest bucketloads of data across hundreds/thousands of vm instances. In a way, I suppose it's a reflection of Google culture where there's a strong emphasis on not doing things that don't scale to Google Scale.

As a result of this, I sort of feel that you should start with PyTorch and eventually graduate to TF if/when the scale requires it. This is sort of like starting with Rails/Django/Node, and migrating to a Go/JVM/[Insert Your Favorite Static Language Here] stack when the traffic load warrants it.