[high_derivative]

I disagree, I don't think it's unfair to say at all. It is simply mismanagement to input resources into a language rework that is dead on arrival, community wise. This is of course par for the course for big tech research orgs where big names get a lot of free rein, but that does not mean it's not a strategic mistake here.

This is simply about focus as an org, and this is the reason why PyTorch is getting so popular.

There seems to be a massive lack of focus and direction in the TF org, too many egos wanting to put their stamp all over the APIs and subsystems (tf.keras anyone?).

TensorFlow eager with autograph or Pytorch solve all differentiation problems as far as researchers and practitioners are concerned.

[marmaduke]

> It is simply mismanagement to input resources into a language rework that is dead on arrival, community wise

how exactly might other features have a community of users prior to the feature being implemented?

> TensorFlow eager with autograph or Pytorch solve all differentiation problems as far as researchers and practitioners are concerned

I think this is a pretty narrow view of the world. From autograd to Stan to the cornucopia of implementations in Julia it's worth considering not everyone's going to be able to shoehorn their problem into the TF/PyTorch way of doing things.

[ninjin]

First of all, excellent points and thank you for the perspective on the direction of TensorFlow development. I usually settle for “It is opaque”, because that is as much as I know at this stage. Looking at all of your points, I agree with almost all of them. I would even add that I think a major reason for TensorFlow’s initial success was the fact that the machine learning community at large preferred (and was more familiar with) the overall Python echo system over the lackluster one in Lua land. However, I still feel that it is unfair to call Swift “dead on arrival” as I can imagine a future where its ecosystem becomes superior to that of Python – I would bet against it, as my comment history would suggest, but I can imagine it with some reasonably larger than zero probability.

Lastly, I would somewhat object to your statement that “TensorFlow eager with [AutoGraph] or [PyTorch] solve all differentiation problems as far as researchers and practitioners are concerned”. Yes, this is a very true statement at this exact point in time. Every single one of my PhD students prefer PyTorch over anything else on the market and I support them in their decision to pick the tool they see as best to accomplish their goals. However, my experience tells me that once you give researchers more powerful tools to express their models, they will find interesting ways to use that increase in expressive power to push the envelope in terms of what is possible. So, yes, as far as researchers and practitioners are currently concerned, what we have is sufficient, but what about the models of the 2020s? I am not so sure.