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Analyzing the performance of Tensorflow training on M1 Mac Mini and Nvidia V100

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Re: Analyzing the performance of Tensorflow training on M1 Mac Mini and Nvidia V100

#11

Post by nvarsj »

JacobSuperslav wrote: post #7
fxtentacle wrote: post #5

"trainable_params 12,810" laughs (for comparison, GPT3: 175,000,000,000 parameters) Can Apple's M1 help you train tiny toy examples with no real-world relevance? You bet it can! Plus it looks like they are comparing Apples to Oranges ;) This seems to be 16 bit precision on the M1 and 32 bit on the V100. So the M1-trained model will most likely yield worse or unusable results, due to lack of precision. And lastly, the…

thanks for the thorough comment. the article is, unfortunately, just clickbait.

It seems like a common trend with M1 articles on HN lately.

Re: Analyzing the performance of Tensorflow training on M1 Mac Mini and Nvidia V100

#12

Post by apl »

fxtentacle wrote: post #5

"trainable_params 12,810" laughs (for comparison, GPT3: 175,000,000,000 parameters) Can Apple's M1 help you train tiny toy examples with no real-world relevance? You bet it can! Plus it looks like they are comparing Apples to Oranges ;) This seems to be 16 bit precision on the M1 and 32 bit on the V100. So the M1-trained model will most likely yield worse or unusable results, due to lack of precision. And lastly, the…

Hard disagree. V100s are a perfectly valid comparison point. They're usually what's available at scale (on AWS, in private clusters, etc.) because nobody's rolled out enough A100s at this point. If you look at any paper from OpenAI et al. (basically: not Google), you'll see performance numbers for large V100 clusters.

Re: Analyzing the performance of Tensorflow training on M1 Mac Mini and Nvidia V100

#13

Post by fxtentacle »

helsinkiandrew wrote: post #8

Can someone with more knowledge of Nvidia GPU's please say how much the V100 costs ($5-10K?) compared with the $900 mac mini.

You would instead buy a used 1080 (no ti) for similar performance.

The special thing about the V100 is that it's driver EULA allows data center usage. If you don't need that, there are other much cheaper options.

Re: Analyzing the performance of Tensorflow training on M1 Mac Mini and Nvidia V100

#14

Post by fxtentacle »

apl wrote: post #12
fxtentacle wrote: post #5

"trainable_params 12,810" laughs (for comparison, GPT3: 175,000,000,000 parameters) Can Apple's M1 help you train tiny toy examples with no real-world relevance? You bet it can! Plus it looks like they are comparing Apples to Oranges ;) This seems to be 16 bit precision on the M1 and 32 bit on the V100. So the M1-trained model will most likely yield worse or unusable results, due to lack of precision. And lastly, the…

Hard disagree. V100s are a perfectly valid comparison point. They're usually what's available at scale (on AWS, in private clusters, etc.) because nobody's rolled out enough A100s at this point. If you look at any paper from OpenAI et al. (basically: not Google), you'll see performance numbers for large V100 clusters.

Yes and you'll see parameters tuned for V100, not parameters tuned for m1 somehow limping along on a V100 in emulation mode.

I wouldn't complain about a benchmark executing any real world SOTA model on m1 and V100, but those will most likely not even run on the M1 due to memory constraints.

So this article is like using an ios game to evaluate a Mac pro. You can do it, but it's not really useful.

Re: Analyzing the performance of Tensorflow training on M1 Mac Mini and Nvidia V100

#15

Post by whywhywhywhy »

>We can see better performance gains with the m1 when there are fewer weights to train likely due to the superior memory architecture of the M1.

Wasn't this whole "M1 memory" thing decided to be a myth now some more technical people have dissected it?

Re: Analyzing the performance of Tensorflow training on M1 Mac Mini and Nvidia V100

#17

Post by jbverschoor »

fxtentacle wrote: post #5

"trainable_params 12,810" laughs (for comparison, GPT3: 175,000,000,000 parameters) Can Apple's M1 help you train tiny toy examples with no real-world relevance? You bet it can! Plus it looks like they are comparing Apples to Oranges ;) This seems to be 16 bit precision on the M1 and 32 bit on the V100. So the M1-trained model will most likely yield worse or unusable results, due to lack of precision. And lastly, the…

Even the RTX 3090 is double the price of an M1 for just 1 card.

The V100 is almost 5-10x the price of an M1.

Re: Analyzing the performance of Tensorflow training on M1 Mac Mini and Nvidia V100

#18

Post by sillysaurusx »

lopuhin wrote: post #6

> I chose MobileNetV2 to make iteration faster. When I tried ResNet50 or other larger models the gap between the M1 and Nvidia grew wider. (and that's on CIFAR-10). But why not report these results and also test on a more realistic datasets? The internet is full of M1 TF brenchmarks on CIFAR or MNIST, has anyone seen something different?

Hehe. That criticism could be applied to ML itself. :)

I wish ML used more than CIFNISTNet, but unfortunately there's not a lot of standard datasets yet. (Even Imagenet is an absolute pain to set up.)

Re: Analyzing the performance of Tensorflow training on M1 Mac Mini and Nvidia V100

#19

Post by littlestymaar »

fxtentacle wrote: post #13
helsinkiandrew wrote: post #8

Can someone with more knowledge of Nvidia GPU's please say how much the V100 costs ($5-10K?) compared with the $900 mac mini.

You would instead buy a used 1080 (no ti) for similar performance. The special thing about the V100 is that it's driver EULA allows data center usage. If you don't need that, there are other much cheaper options.

> The special thing about the V100 is that it's driver EULA allows data center usage.

Wait what? Is it the only thing?

That sounds hard to believe: if true, using the open driver (Nouveau) instead of Nvidia's proprietary one would be a massive money saver for datacenters operators (and even if Nouveau doesn't support the features you'd want already, supporting their development would be much cheaper for a company like Amazon than paying a premium on every GPU they buy)

Re: Analyzing the performance of Tensorflow training on M1 Mac Mini and Nvidia V100

#20

Post by sillysaurusx »

fxtentacle wrote: post #13
helsinkiandrew wrote: post #8

Can someone with more knowledge of Nvidia GPU's please say how much the V100 costs ($5-10K?) compared with the $900 mac mini.

You would instead buy a used 1080 (no ti) for similar performance. The special thing about the V100 is that it's driver EULA allows data center usage. If you don't need that, there are other much cheaper options.

Don't buy hardware in general for AI work, IMO. It'll be out of date in a year and you'll end up training in the cloud anyway.
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