| Why engineers like Tensorflow: - More code to check-in (Looks more productive) - More infrastructure, e.g. checkpoints, exporters etc. (Looks like they're doing more work) - Fancy visualizations (Allows them to look impressive while presenting loss plots) - Easier to reuse things others have implemented and still get credit for it (TF model zoo, research repo etc.) Why researchers like pytorch: - Way easier to hack together their novel idea - Looks scrappier (which somehow makes the individual look like a better researcher instead of an ordinary programmer) - Lots of other researchers release code in pytorch so if you're working off of their idea, you use pytorch to avoid re-producing their results. Open to debate on these ideas, let me know if you have a counterpoint or any other reasons to add |
With those bullet points, looks like you didn't talk to actual engineers, but rather middle-layer management people.