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Apple M1 support for TensorFlow 2.5 pluggable device API

developer.apple.com

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Re: Apple M1 support for TensorFlow 2.5 pluggable device API

#81

Post by dharma1 »

Joeri wrote: post #45
ksec wrote: post #41

Earlier quoted context omitted.

So Apple would need 16x its GPU Core, or 128 GPU Core to reach Nvidia 3090 Desktop Performance. Or roughly 480mm2 Die Size, 192W TDP excluding memory controller and interconnect. Doesn't look too bad for Nvidia, especially when you consider 3090 is still on Samsung 8nm, which is equivalent to TSMC 10nm, compared to 5nm on Apple M1.

The rumors say they’re going to have a high end of 128 gpu cores by using 4 32 gpu core chiplets.

wouldn't that require pretty hefty active cooling, which doesn't fit so well with Apple devices?

It would be great if they did it the eGPU route with TB4, in a slick package

Re: Apple M1 support for TensorFlow 2.5 pluggable device API

#82

Post by machello13 »

jonathanstrange wrote: post #74
okamiueru wrote: post #71

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 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.

Surely, with the M1s having been actually publicly available for many months now, there's enough data and benchmarks out there that we don't need to take Apple's performance claims with any salt?

Re: Apple M1 support for TensorFlow 2.5 pluggable device API

#83

Post by dahfizz »

Joeri wrote: post #45
ksec wrote: post #41

Earlier quoted context omitted.

So Apple would need 16x its GPU Core, or 128 GPU Core to reach Nvidia 3090 Desktop Performance. Or roughly 480mm2 Die Size, 192W TDP excluding memory controller and interconnect. Doesn't look too bad for Nvidia, especially when you consider 3090 is still on Samsung 8nm, which is equivalent to TSMC 10nm, compared to 5nm on Apple M1.

The rumors say they’re going to have a high end of 128 gpu cores by using 4 32 gpu core chiplets.

Does that mean they will actually produce a laptop with adequate cooling? Might be worth looking at.

Re: Apple M1 support for TensorFlow 2.5 pluggable device API

#84

Post by defaultname »

andy_ppp wrote: post #75
codelord wrote: post #5

M1 and AMD GPU support. I'm personally more interested in the latter as I haven't yet upgraded my MacBook Pro and I expect that my Vega 20 to be faster than M1 at ML training. The raw compute power of M1's GPU seems to be 2.6 TFLOPS (single precision) vs 3.2 TFLOPS for Vega 20. This can give you an estimate of how fast it would be for training. Just for reference Nvidia's flagship desktop GPU(3090)'s FP32 performance…

Interesting that AMD is supported, could this mean Apple Silicon 16inch MBP with an AMD GPU?

A large number of Apple devices in the field, and even still for sale by Apple, have AMD GPUs. It's most likely legacy support driving its inclusion -- if Apple abstracted the pluggable device, appropriately branching it for their existing AMD and new chips seems a given.

It's extremely doubtful any new Apple Silicon device will come out with AMD GPUs.

Re: Apple M1 support for TensorFlow 2.5 pluggable device API

#85

Post by whynotminot »

okamiueru wrote: post #71
ojosilva wrote: post #52

I'm still trying to find a way to monitor the Neural Engine on my Macbook air M1, but the APIs are non-existent, there's barely anything in the docs and no answer from Apple. My models train fast, 3x faster than most i7 computers with GPU, which is excellent for a fanless ultraportable computer but I wish Apple would treat the NE as a 1st class citizen on these machines, with Mac SDK APIs and usage visualization in t…

> 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…

> That the M1 is computationally powerful is a myth started out by exceedingly misleading marketing and reinforced with hard-to-compare benchmarks

It feels like you've constructed quite the straw-man to tear down.

Praise for the M1 is in the context of the form factors it exists in and the efficiency it works at.

Of course you can find more powerful hardware in larger form factors drawing 10x the power from the mains. The M1 runs in an iPad for crying out loud.

> The constraints for comparison are already stated: computation power being the main priority.

Then don't buy a bloody M1. The M1 has always been Apple's entry-level efficiency-first processor. We haven't even seen the "Pro" stuff yet.

Re: Apple M1 support for TensorFlow 2.5 pluggable device API

#86

Post by tbrock »

bnt wrote: post #46
notatoad wrote: post #24

Earlier quoted context omitted.

`pip install --upgrade pip` fixed this for me. (not in tensorflow directly, but while installing something else on my M1 last week which required numpy)

OK, so we’re in mid-2021, why is installing Python THAT HARD? I think the only reason Node is so popular is because it JUST WORKS. Windows, Mac, doesn’t matter. One-click installer and you got NPM as well and access to thousands of packages.

I consider myself a novice but competent python programmer (not particularly skilled or expert) and everytime i have to use something made in python i cringe because i know it will require 30 minutes of futzing with things to even run it.

Python’s ecosystem is the worst for get-up-and-go usage.

Nvm or nodenv make managing node environments trivial compared to virtualenv, pip, and co. Same with rustup or rbenv… in fact python is the only language i have the problem with.

Re: Apple M1 support for TensorFlow 2.5 pluggable device API

#87

Post by defaultname »

ksec wrote: post #41
codelord wrote: post #5

M1 and AMD GPU support. I'm personally more interested in the latter as I haven't yet upgraded my MacBook Pro and I expect that my Vega 20 to be faster than M1 at ML training. The raw compute power of M1's GPU seems to be 2.6 TFLOPS (single precision) vs 3.2 TFLOPS for Vega 20. This can give you an estimate of how fast it would be for training. Just for reference Nvidia's flagship desktop GPU(3090)'s FP32 performance…

So Apple would need 16x its GPU Core, or 128 GPU Core to reach Nvidia 3090 Desktop Performance. Or roughly 480mm2 Die Size, 192W TDP excluding memory controller and interconnect. Doesn't look too bad for Nvidia, especially when you consider 3090 is still on Samsung 8nm, which is equivalent to TSMC 10nm, compared to 5nm on Apple M1.

If Apple could just scale up their GPU and trounce a 430B market cap competitor's premiere product at 1/2 the power, 60% of the die size, that actually looks pretty bad for nvidia, doesn't it? Scaling is more difficult than that, and who knows if they could so easily, but who thought Apple would render both Intel and nvidia irrelevant?

Regardless, Apple's threat to vendors like that is their complete vertical integration. Ran some of the new object capture code (photogrammetry) on my M1 Mac yesterday and in no time at all the 11 trillion op neural engine blasted through and generated a remarkable model. We've seen Apple respond to discovered performance needs by plugging in a matrix engine, a neural engine, and scaling appropriately, dedicating cores and silicon to the greatest needs. They are in a very unique position relative to someone like nvidia who effectively throws something over a fence.

Re: Apple M1 support for TensorFlow 2.5 pluggable device API

#88

Post by Ly5846N929Rm6bb »

dahfizz wrote: post #83
Joeri wrote: post #45

Earlier quoted context omitted.

The rumors say they’re going to have a high end of 128 gpu cores by using 4 32 gpu core chiplets.

Does that mean they will actually produce a laptop with adequate cooling? Might be worth looking at.

I think the 128 core GPU is rumored for desktop Mac Pro.

Re: Apple M1 support for TensorFlow 2.5 pluggable device API

#89

Post by dekhn »

sillysaurusx wrote: post #34
liprais wrote: post #33

Earlier quoted context omitted.

Python 3.9.4 | packaged by conda-forge | (default, May 10 2021, 22:10:52) [Clang 11.1.0 ] on darwin Type "help", "copyright", "credits" or "license" for more information. >>> import tensorflow Init Plugin Init Graph Optimizer Init Kernel >>> tensorflow.config.list_physical_devices >>> tensorflow.config.list_physical_devices() [PhysicalDevice(name='/physical_device:CPU:0', device_type='CPU'), PhysicalDevice(name='/phy…

Ohhhh snapppppp! Excitement level just shot way up. Thank you. God, it’ll be nice having a gpu for tensorflow. I’ve dreamed about this for like… a long time.

... and it worked on linux and windows for years already.

Re: Apple M1 support for TensorFlow 2.5 pluggable device API

#90

Post by rrss »

qiqitori wrote: post #49

https://github.com/tensorflow/tensorflow/releases/tag/v2.5.0 (Linked from Apple's article) Wow, that list of CVEs is 110 lines.

the majority of these are "an attacker can craft a model that causes problems."

Are people actually using tensorflow to run untrusted models?

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