P.S. this is a mandatory step anyway when they eventually have to build an ARM-based Mac version. Same goes for a full iPad app.

This is my concern as well. Wolfram was there at the WWDC Keynote when the switch to Intel Macs was announced. (There are videos with Theo and Rob showing how the 'little switch' worked.) Since then, support for new Apple technologies has lagged behind -- they just made the deadline for 64 bit apps with the front end. Although ARM-based Macs are presently just rumors, I think that it is almost certain that this will happen.

I understand the desire to have a common code base for different platforms. In my opinion, leveraging the work that Apple has already done to make code hardwire independent -- Specifically Metal in all its implementations -- outweighs this, and will provide a superior program for Mac users, certainly, and possibly also for Windows users.

Thank you, Marco and Mike, this is extremely useful.

I fully agree that a desirable solution would be for Mathematica to support Apple GPU installations out of the box - one of the reasons that I love Mathematica is that I do not have to install a thousand libraries (or even compile them from scratch) but that things work straight away.

However, I would be quite happy if there was just decent support for cloud-based GPUs. By this, I don't mean support for Wolfram Script (at least a step in the right direction) but support for full interactive Mathematica. I would love for Mathematica online to offer the option to chose a GPU backend. I'd happily pay for it as long as the charges are reasonably (ie comparable to EC2 or Google).

Ultimately, if I had to choose between cloud GPU support and desktop GPU support, I would probably opt for the cloud. After all, I can't run a compute job that takes several days several days on my laptop!

Indeed and the iPad Pro today is already more powerful than quite a few notebooks... I don't think Mac users are a minority for Wolfram, it's a substantial percentage of the userbase. Even Mathematica 1 was released for the Mac first.

Currently Tensorflow and other popular machine learning frameworks do not support Metal (aside from Apple's CoreML). This means that in Jupyter notebooks for example you are forced to use the cloud to compute using a GPU. Given that Wolfram wants to increase the usage of its language in data science, Metal support should be a no brainer so I'm surprised Wolfram hasn't prioritized this.

I agree 100%.

Apple, which has a lot more resources than Wolfram, has already done the heavy lifting in abstracting the hardware for the GPUs. Further, I doubt that the open-source framework that Wolfram is using for GPU acceleration of neural networks will ever escape the thumb of NVIDIA.

I realize that macOS users are a minor component of Mathematica users, but they are a significant minority. If you add in iOS users (I hope that a native Mathematica for iOS will eventually be released), the number of people who could benefit by fully integrating Metal into Mathematica would be significant.

Wolfram has already made a good start by using Metal for graphics. It is time to take the next step.

Right. But this is Metal for graphics rendering, not Metal for Machine Learning, etc. Apple expanded the domain for their 'Metal' technology. So, although graphics rendering is much improved, Mathematica does not make use of any of the neural networks stuff.

Since Nvidia drivers are no longer supported in MacOS 10.15, I assume this no longer works, or am I mistaken?

Set the environment variables LDLIBRARYPATH, LIBRARY_PATH and CPATH to the directory extracted from the download.

If needed, separate multiple directories with : as in the PATH environment variable.

export CUDNN_ROOT=/home/YourUserName/libs/cudnn
export LD_LIBRARY_PATH=$CUDNN_ROOT/lib64:$LD_LIBRARY_PATH
export CPATH=$CUDNN_ROOT/include:$CPATH
export LIBRARY_PATH=$CUDNN_ROOT/lib64:$LD_LIBRARY_PATH

To uninstall CUDA, run:

/usr/local/cuda/bin/uninstallxxx

Marco, thank you for all of this very usable information. Following these instructions I was easily able to get a similar setup working, the main difference is that my GPU is an NVIDIA GTX 1080 Ti. I'm posting some benchmarks for GPU comparison (and some details about the setup the end).

The network training task came in at about 2 minutes 17 seconds withthe 1080 Ti:

The ImageConvolve was still slower with CUDA, but not a lot slower:

I was not successful in growing a Mandelbulb, but I didn't put any real effort into trying to troubleshoot this.

Thanks again, I never would have attempted this had you not documented your setup... best wishes... Jan

PS: On a related note, Apple appears to be moving towards supporting external GPU's in the upcoming High Sierra OS release, but apparently only with computers that support Thunderbolt 3.

PPS: Some technical details:

Computer: Model Name: MacBook Pro Model Identifier: MacBookPro11,4 Processor Name: Intel Core i7 Processor Speed: 2.2 GHz

eGPU: I have a BizonBox 2S connected by Thunderbolt 2. I first downgraded the Command Line Tools. Then I followed the BizonBox instructions (including having an external monitor plugged into the GPU via HDMI). Then I followed Marco's instructions for the Wolfram setup. I only experienced one minor setback, which was that the CUDAResourcesInstall[] crashed the Wolfram kernel the first time I tried it, but worked fine after launching a new kernel.

Hi Eric,

yes, it sounds as if this might get easier. I suppose the problem with the Command Line Tools would persist though. As soon as I can get a final version of High Sierra, I will try it out and report back to this Community.

Cheers,

Marco

Dear Mike,

thank you for your nice words. I am glad if some of what I wrote helped.

You are right that sometimes it takes a bit of rerunning bits several times, and some rebooting to make it work. On a "clean" Mac the instructions appeared to work, but after trying this on many Macs now, there is often some rebooting required. Also, when you run an update of the OS you might have to perform some of the instructions again.

On the bright side, I got the GPU to work on all Macs we have tried so far. The script on this page: https://github.com/goalque/automate-eGPU sometimes seemed to make a difference, particularly after an OS update. Also OSX regularly wants to update the downgraded Command Line Tools, which is a bit annoying.

Best wishes,

Marco

Has anyone set up an eGPU with Windows?

Dear Wolfram Team,

I am very glad and thankful that you reacted so quickly to the comments about GPU access on Macs. Having access to this framework opens up many possibilities in research and teaching. I appreciate it that you sorted this out so swiftly and efficiently.

Thank you,

Marco

Marco,

Awesome post! I was just looking into doing this.

What is the reason for downgrading the command line tools? If you do not downgrade can you still run the built in Neural net functions (without using the compiler)?

Thanks

Dear Marco, Thank you for a very informative post. You had responded to a question about downgrading the command line tools with:

"the downgrading is strictly speaking not necessary if you only want the Wolfram Language's Machine Learning and functions that do not require compilation."

I was wondering if you were had tried NetTrain without the downgrade of command line tools to 7.2. Thanks..Jan

Hi Sander,

Yes, I've got the TitanX. I do not have comparative benchmarks with the other ones though.

For me it was definitely worth buying the boxes - and I am lucky that Wolfram reintroduced the support for them. I wouldn't say that I am particularly good at CUDA (quite the opposite), but I could make some code run substantially faster, which was really important for a project I have.

Note, that you can also buy the BizonBox without the GPU, so if you have a spare one flying around you can (most likely) use that one.

Cheers,

Marco