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Apple M1 support for TensorFlow 2.5 pluggable device API

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Re: Apple M1 support for TensorFlow 2.5 pluggable device API

#121

Earlier quoted context omitted.

> 3x faster than most i7 computers with GPU Can you back that statement up with anything, or at least clarify it? You seem to suggesting a non-mac i7 with a separate GPU. Also, just an FYI, "i7" says pretty much nothing. The i7s have existed since 2009. I don't know. The statement is just so vague and ridiculous. The M1 is probably the worst hardware you could have picked in 2020-2021 if computational power was your…

I meant to write most i7 machines with a GPU where I trained my models . My post was about the lack of adequate SDKs for the NE on the M1. I don't know why the fuck you grabbed that tiny fraction and just ran off with it like I was writing an appleboy marketing manifesto on the M1 prowess.

I mean... you are doing what I find more than a few always seem to do on threads discussing the M1. Which is to subtly suggest that it is a computational powerhouse, with vague and non-verifiable metrics (your post that I originally replied to is a good example). Agreeing to disagree should be possible, and the request you got for a follow up to give some more specific details was polite, and should not have been that hard to deal with. It was also genuinely interested in an answer, so I question the appropriateness of the langue your responded with. And, I might also remind you that as far as answering the question, it did not.

If you haven't already, I'd suggest looking at https://news.ycombinator.com/newsguidelines.html. And I mean that too in a polite way, as it is easy to get carried away and assume the worst in anonymous conversations with strangers.

So, to summarize. Apple originally made highly misleading statements on the computational power of the M1, and since then, this misconception is repeated quite often. After a while, it gets annoying to see new posts on HN, week after week. Now, alluding that an M1 is 3x faster at training models than discrete GPUs can only be true if it is a decade old hardware. And as that stands, your original comment was asked to be clarified. After all, it's not unheard of for M1 benchmarks to disable the discrete GPU of the PC it is compared to, for "fairness". Limiting the training to a single CPU core for "even more fairness" would be par for the course.

So, I did not suggest you were an "appleboy", as you put it. I asked what kind of hardware you actually experienced a 3x speedup on, as the i7 goes back to 2009, and "GPU", well, further back.

In any case, and this might come as a surprise, feel free to not answer. But, then, I can only wish that you refrain from making the effort at a rude response.

Re: Apple M1 support for TensorFlow 2.5 pluggable device API

#122
post #76

Earlier quoted context omitted.

I don't know about the M1, to be honest, but I used to be an Apple customer and ardent fanboy (so embarrassing!) during the golden PPC age and remember very well Apple's inflated claims about performance, which all turned out to be false the minute they switched to Intel. So I agree with your comment, it's advisable to always take miraculous performance claims with a grain of salt.

Were the performance claims false, or did Intel just catch up, while PowerPC stagnated?

They were misleading to false, depending on how you interpret it. The claims were true for certain specialized microbenchmarks and false in general. PPC programs running tasks that could be optimized for using Altivec instructions could be much faster than on comparable Intel chips, but those tasks were rare and normal programs were slower than on Intel.

Apple themselves changed their tune about the difference almost overnight.

Re: Apple M1 support for TensorFlow 2.5 pluggable device API

#123
post #104

Earlier quoted context omitted.

> 3x faster than most i7 computers with GPU Can you back that statement up with anything, or at least clarify it? You seem to suggesting a non-mac i7 with a separate GPU. Also, just an FYI, "i7" says pretty much nothing. The i7s have existed since 2009. I don't know. The statement is just so vague and ridiculous. The M1 is probably the worst hardware you could have picked in 2020-2021 if computational power was your…

> The M1 is probably the worst hardware you could have picked in 2020-2021 if computational power was your main concern. For highly > parallelizable work tasks, the top end GPU alone has 10x the computation power than the M1, and a top end CPU has around 4x the > computation power than the M1. Not to mention a rather limiting 16GB of memory. This statement is so banal that I am not sure how to comment on it. I never…

> This statement is so banal that I am not sure how to comment on it. ...

> M1 is obviously a terrible choice if you are looking for a deskbound HPC workstation ...

So, in one breath you say my statement is banal, in the next you agree with it. The thing I take issue with is having to deal with the misconception that it is a replacement for a HPC workstation, because many (not you obviously) think it actually is a powerhouse.

You go on to repeat the stuff about power performance, which I've already listed, and you could have spared yourself the trouble.

Re: Apple M1 support for TensorFlow 2.5 pluggable device API

#124

Earlier quoted context omitted.

> 3x faster than most i7 computers with GPU Can you back that statement up with anything, or at least clarify it? You seem to suggesting a non-mac i7 with a separate GPU. Also, just an FYI, "i7" says pretty much nothing. The i7s have existed since 2009. I don't know. The statement is just so vague and ridiculous. The M1 is probably the worst hardware you could have picked in 2020-2021 if computational power was your…

> The constraints for comparison are already stated: computation power being the main priority Who compares ultrabook to desktop for the multithreaded computation power? Assume you only want that without any regards to power or space or cost, by your logic you could buy 1000 mac mini and the computational power will be more than any desktop computer.

See 3.
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