If Apple can work with...Google to get their framework changed for the M1, then there is absolutely no excuse for Intel/AMD. They had a decade to fix this.
They deserve their fate.
AMD had been working with Google to get their framework changed for AMD's GPU for more than two years [1], and all their work are upstreamed. Oh, and AMD's ROCm/OpenCL support is really for general computing, i.e. CUDA alternative, unlike the ML Compute here. ML Compute is something Apple created specifically for running neural networks, nothing more, and roughly equivalent to TensorRT / Android NN if you want to compare with other platforms. And it was here because the wall of Apple's walled garden is too high that nobody other than them can effectively optimize NN inference/training on their chip.
Are they getting competitive results?
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...) . Numpy itself is only accelerated using BLAS/LAPACK which cant leverage GPU all that well.
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 :(
> 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...
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.
NVidia cared to make CUDA into a polyglot GPGPU programming model, with nice debugging tools where you can do everything like CPU graphical debuggers, then thanks to PTX it was a matter to just add a new backend to your compiler.
Hence why, even though it doesn't get that much press, you can even use flavours of Java and .NET on CUDA.
Meanwhile Khronos kept driving their C only agenda, and when they realized the mistake, came up with SPIR (then SPIR-V after Vulkan was introduced), tried to also cater to the C++ devs (with printf like debugging tools).
All this effort was largely ignored by OEMs, with their lousy tools, thus ending with OpenCL 3.0 being effectively OpenCL 1.2 renamed to sound cool, and the C++ efforts (SYSCL) are now focusing on compute agnostic backends.
The problem wasn't NVidia, rather Intel and AMD did not deliver and all their alternatives to OpenCL are even worse, half backed attempts that always loose steam half way through.