Apple M1 support for TensorFlow 2.5 pluggable device API

Post by codelord » Wed, Jun 09, 2021, 2:30 AM UTC

6gvONxR4sf7o wrote: post #31

codelord wrote: post #30

Earlier quoted context omitted.

It's not really fair to compare a discrete GPU to a mobile GPU, I only provided this as a comparison for someone who maybe has one of these at home. And btw, you are talking about TF32 performance not FP32. TF32 actually uses 16 bits. A100's FP32 performance is actually lower than 3090, it's 19.5 TFLOPS: https://www.nvidia.com/content/dam/en-zz/Solutions/Data-Cent... FP16 performance is also relevant as a lot of peop…

I’ve been mostly using TPUs lately and non-TF32 GPUs at work, so I don’t have any practical experience with TF32, but the sales pitch seems pretty good. Do you have any personal experience on whether it’s as much of a drop in replacement for fp32 as they suggest?

Post by sillysaurusx » Wed, Jun 09, 2021, 3:20 AM UTC

liprais wrote: post #33

sillysaurusx wrote: post #16

Anyone know if this shows up as an actual GPU device? The last tensorflow-macOS thing did not. If you list devices using that, you’ll see only one: CPU:0. Does this give a GPU:0 device? You can check via: import tensorflow as tf2 from pprint import pprint as pp tf = tf2.compat.v1 sess = tf.InteractiveSession() pp(sess.list_devices()) I’d check myself, but I’ve been so burnt by tensorflow 2 and M1 problems that I just…

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…

Post by macintux » Wed, Jun 09, 2021, 3:45 AM UTC

dragonwriter wrote: post #28

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)

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.)

Post by rubatuga » Wed, Jun 09, 2021, 4:00 AM UTC

codelord wrote: post #30

6gvONxR4sf7o wrote: post #25

Earlier quoted context omitted.

> Just for reference Nvidia's flagship GPU(3090)'s FP32 performance is 35.5 TFLOPS. In context of ML, nvidia’s flagship is the A100, which has 312 TFLOPS. You can also compare with a TPU device which has 180 TFLOPS (v2) or 420 TFLOPS (v3). You can use at least the TPU v2 reliably on colab for free.

It's not really fair to compare a discrete GPU to a mobile GPU, I only provided this as a comparison for someone who maybe has one of these at home. And btw, you are talking about TF32 performance not FP32. TF32 actually uses 16 bits. A100's FP32 performance is actually lower than 3090, it's 19.5 TFLOPS: https://www.nvidia.com/content/dam/en-zz/Solutions/Data-Cent... FP16 performance is also relevant as a lot of peop…

Post by jra101 » Wed, Jun 09, 2021, 4:17 AM UTC

codelord wrote: post #23

jra101 wrote: post #18

Earlier quoted context omitted.

Vega 20 should be ~13.8 TFLOPs (single precision): https://www.anandtech.com/show/13923/the-amd-radeon-vii-revi...

Seems like AMD has been using Vega 20 to refer to two different things. I was talking about the mobile GPU on MacBook Pros which is based on a 14nm chip. The full name is Radeon Pro Vega 20: https://www.amd.com/en/graphics/radeon-pro-vega-20-pro-vega-... https://www.techpowerup.com/gpu-specs/radeon-pro-vega-20.c32... Vega 20 seems to also refer to a discrete GPU. This has been later rebranded to Radeon VII (maybe bec…