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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

#41

Post by ksec »

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.

Re: Apple M1 support for TensorFlow 2.5 pluggable device API

#42

Post by dragonwriter »

macintux wrote: post #36
dragonwriter wrote: post #28

Earlier quoted context omitted.

Presumably actually: python -m pip install --upgrade pip just: pip install --upgrade pip Is “break pip if there is a newer version available”, at least last I checked. (It uninstalls the existing pip but can’t complete the install of the new one.)

I’ve used the latter several times a month over the last year or so, but only since about pip 19.x, didn’t use Python much before a couple of years ago.

It may only be older pip, or windows, or a combination. I've broken pip in quite a few venvs that way in the past though.

Re: Apple M1 support for TensorFlow 2.5 pluggable device API

#45

Post by Joeri »

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.

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

Re: Apple M1 support for TensorFlow 2.5 pluggable device API

#46

Post by bnt »

notatoad wrote: post #24
rozim wrote: post #7

I have found the M1 air fine for web browsing but kind of hard to install software on. Following the instructions: ----- python -m pip install tensorflow-macos ... ERROR: Failed building wheel for numpy Failed to build numpy ERROR: Could not build wheels for numpy which use PEP 517 and cannot be installed directly ----- (base) dave@daves-air ~ % uname -a Darwin daves-air.lan 20.5.0 Darwin Kernel Version 20.5.0: Sat M…

`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.

Re: Apple M1 support for TensorFlow 2.5 pluggable device API

#47

Post by mtgx »

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.

Doesn't 3090 use well north of 400W of power?

Re: Apple M1 support for TensorFlow 2.5 pluggable device API

#48

Post by rewgs »

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.

For real. I enjoy the language but I avoid it like the plague due to...everything else.

Re: Apple M1 support for TensorFlow 2.5 pluggable device API

#50

Post by giansegato »

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.

Respectfully disagree. Being an experienced Python dev, and a total JavaScript noob, I feel exactly the same with npm.

It's ultimately just a matter of experience I guess

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