[sja]

I think that part is misleading. Vijay Vasudevan (a member of the TensorFlow team) has repeatedly put down the notion that the internal TensorFlow code is significantly different than what we see:

https://www.reddit.com/r/MachineLearning/comments/696dzy/d_i...

Obviously, this is taking the word of someone who is incentivized to get as many people using TF as possible, but I haven't been given a reason to not believe him.

I believe that the public TensorFlow repo is very close to what they use internally. That said, I'm sure there is a huge amount of internal tooling (for easily spinning up clusters internally, profiling, probably an automatic device placer) that we don't get to see. But that has more to do with the fact that its designed with Google's specific infrastructure in mind than it has to do with "hoarding the good stuff".

[vrv]

Thanks for mentioning that Sam, I appreciate it. Speaking for just myself:

I personally don't care what tools or frameworks people use to get work done and have repeatedly suggested people use whatever works best for them.

It also wouldn't make any sense to hoard any good stuff internally if we wanted to provide a useful framework that people wanted to use externally.

We actually don't have a huge amount of tooling internally that we hold back. The only tooling I really use is the [timeline](https://github.com/tensorflow/tensorflow/blob/f488419cd6d925...) for debugging performance (not the EEG tool in the whitepaper, I've never used it). That's available externally though, as you can see.

On some of the comments in this thread in general, I'm pretty sad at the lack of scientific rigor in the community, and that goes for any person who publishes code that differs from the results they claim, regardless of affiliation. I am happy about projects like OpenAI's RL baselines, as I know others are too.

Most of the papers and articles comparing performance of frameworks have lots of bugs and aren't even comparing the same model computation between frameworks. In fact, CNTK's article points to an external benchmark showing CNTK in a good light, but those benchmarks have bugs in them rendering the comparison incorrect (we've been sending PRs to fix them). I find it disappointing that the culture of the organization promotes calling others out for being 'irresponsible' except when it suits them.

The TensorFlow team hasn't published many articles comparing performance directly to others because it's honestly a lot of hard work to verify that you are comparing fairly. Of course, I do think TensorFlow needs to improve performance out of the box for people, and the team is working on that.

[sja]

Thanks for the response, Vijay. I didn't mean to insinuate that I thought the team was holding back tooling for the public (rather that such tooling wouldn't make sense to release), but it's reassuring to hear that the total TensorFlow experience is pretty much the same internally and externally.

I think part of the conspiracy theorizing is due to the misconceived notion that Google has some "secret sauce" that allows it to do what it does, as opposed to many talented engineers spending a lot of man-hours on a problem. There has also been a fair amount of negative Google sentiment in the community recently, and the story that Google is holding out on developers feeds into this narrative.

Benchmarking has always been low-hanging fruit for community members to latch onto for the sake of attacking/defending a particular framework. However, the practical difference between these frameworks (assuming each is configured properly) seem to be within a margin of error and are constantly changing (not to mention the inconsistencies you mentioned), so choosing a framework solely on its benchmarking scores is narrow-minded.

From what I've seen, benchmarking has been more useful as a discovery mechanism for areas in a codebase that can be improved. The TensorFlow team has done an excellent job of using various benchmarks to guide development, and I imagine other frameworks are doing the same.