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FYI - i have never used a macbook in my life. I only use XPS with Fedora. So this frustration is personal. I have been party to get AMD/Intel's CUDA alternativ

FYI - i have never used a macbook in my life. I only use XPS with Fedora. So this frustration is personal. I have been party to get AMD/Intel's CUDA alternative out the door on some of the ML libraries - which one is it now ? OpenCL...SYCL ...ROCm...PlaidML ? I cant remember. All this time I was pissed at nvidia - surely they were playing subversive politics to kill all of this. With so many initiatives, surely AMD/Intel had their heart in the right place. Apple and Google are cutthroat rivals. And they worked together for a just-released chip to get fully working acceleration support. Here's where it gets sadder for me - Tensorflow has included a GPU accelerated version of Numpy ( https://twitter.com/fchollet/status/1292893864986984448?lang=en https://twitter.com/fchollet/status/1292893864986984448?lang...) . Numpy itself is only accelerated using BLAS/LAPACK which cant leverage GPU all that well. https://www.tensorflow.org/api_docs/python/tf/experimental/numpy https://www.tensorflow.org/api_docs/python/tf/experimental/n... At this point, it is basically a sealed deal - if you're even remotely dabbling in data science, you better be working on a Mac. Which makes me hate my XPS all the more :(

sangnoir 6y ago
Tensorflow is accelerated just fine on AMD thanks to ROCm - everything you're praising Apple for doing, AMD has done for their hardware. You're disparaging AMD for not doing something they have actually done. There was even a tensorflow blog article announcing upstreaming AMD support, in 2018[0] > At this point, it is basically a sealed deal - if you're even remotely dabbling in data science, you better be working on a Mac. This is hilarious. You might have missed that the benchmark was missing Nvidia (or even AMD) graphics cards; I can't think of a lower bar for comparing ML performance than against Intel GPUs - perhaps Intel CPUs? While Apple has brilliant engineering, the M1 cannot possibly outperform the obscene number of transistors Nvidia & AMD throw at the task, even in older, mid-range cards. Not to mention power dissipation. If you're dabbling, you're better off with Google's Colab[1] which has (free) hardware acceleration which is roughly on par with my 3-year-old RX580 for my Tensorflow projects. Colab will work on anything that can run a browser. 0. https://blog.tensorflow.org/2018/08/amd-rocm-gpu-support-for-tensorflow.html https://blog.tensorflow.org/2018/08/amd-rocm-gpu-support-for... 1. https://colab.research.google.com/ https://colab.research.google.com/
- neilc 6y ago
  > You might have missed that the benchmark was missing Nvidia (or even AMD) graphics cards; The post does include a benchmark for an AMD GPU (Radeon Pro Vega II Duo) on the Mac Pro. Comparing the Mac Pro GPU vs. MBP M1 results, the GPU clearly wins, although in some cases the margin isn't as large as you might expect.
  - sangnoir 6y ago
    It's hard to reconcile the performance across the 2 tests, perhaps they were set up/tuned differently. I wish they had published their methodology - I'd have loved to benchmark my long-in-the-tooth RX580 & rocm-tensorflow against their numbers

sangnoir 6y ago

Tensorflow is accelerated just fine on AMD thanks to ROCm - everything you're praising Apple for doing, AMD has done for their hardware. You're disparaging AMD for not doing something they have actually done. There was even a tensorflow blog article announcing upstreaming AMD support, in 2018[0] > At this point, it is basically a sealed deal - if you're even remotely dabbling in data science, you better be working on a Mac. This is hilarious. You might have missed that the benchmark was missing Nvidia (or even AMD) graphics cards; I can't think of a lower bar for comparing ML performance than against Intel GPUs - perhaps Intel CPUs? While Apple has brilliant engineering, the M1 cannot possibly outperform the obscene number of transistors Nvidia & AMD throw at the task, even in older, mid-range cards. Not to mention power dissipation. If you're dabbling, you're better off with Google's Colab[1] which has (free) hardware acceleration which is roughly on par with my 3-year-old RX580 for my Tensorflow projects. Colab will work on anything that can run a browser. 0. https://blog.tensorflow.org/2018/08/amd-rocm-gpu-support-for-tensorflow.html https://blog.tensorflow.org/2018/08/amd-rocm-gpu-support-for... 1. https://colab.research.google.com/ https://colab.research.google.com/

neilc 6y ago
> You might have missed that the benchmark was missing Nvidia (or even AMD) graphics cards; The post does include a benchmark for an AMD GPU (Radeon Pro Vega II Duo) on the Mac Pro. Comparing the Mac Pro GPU vs. MBP M1 results, the GPU clearly wins, although in some cases the margin isn't as large as you might expect.
- sangnoir 6y ago
  It's hard to reconcile the performance across the 2 tests, perhaps they were set up/tuned differently. I wish they had published their methodology - I'd have loved to benchmark my long-in-the-tooth RX580 & rocm-tensorflow against their numbers