FYI, downgrading to CUDA 7.5 does not solve the issue.
GW
Hello,
I’m training a Neural Network and its taking a lot of time. So, in my NetTrain[] function, I set an additional parameter TargetDevice->“GPU”. I got this error message.
I’m using a MacBook Pro with “Intel Iris Graphics 6100 1536 MB” Graphics card.
Is it possible to set this as my GPU and train my Neural Network? Is there any alternate way to connect to a cloud to train my Neural Network?
This issue seems to be resolved in version 11.1 (at least for my GTX 1050)!
Gijsbert
We only support NVIDIA GPU’s at the moment. So your Intel GPU won’t work.
Wolfram Cloud also doesn’t have any GPU’s at the moment. You would need to use EC2 or Google Cloud yourself to make use of their GPU’s.
Yes, 11.1 should work on all recent NVIDIA GPU’s now.
It’s working great for NetChain on my Nvidia 1080. However, doesn’t look like they built GPU support for Classify and Predict with
Classify[trainingData, {Method -> "NeuralNetwork",
PerformanceGoal -> "Quality", TargetDevice -> "GPU"}]
Wish it did though! Put that on the list.
@Sabastian, Is there a way to use Google Cloud GPU’s for training NetChain other than having an extremely expensive Enterprise Network license?
Seems that the GPU ability of NeuralNetwork is not supported in 11.1, this works fine on 11.0 on OS X
It is not available on my Macbook Pro because it doesn’t have a GPU graphics card. My Nvidia 1080 card on my PC works well.
I suppose you are running a custom Linux machine rather than a normal out of the box Mac. I guess that is one way to get a GPU on Mac OS but it won’t solve your CUDA SDK problem. Probably something you should disclose rather than making it seem like anyone who thinks GPU support wouldn’t normally be available on Mac’s is an idiot.
That is very cool that you had it working on 11.0. Congrats on that. Last summer, I was not able to use GPU on either my iMac nor my Macbook Pro. Wolfram Support told me that most Mac’s don’t generally have GPU’s so I figured it was a general truth. I found a couple external GPU options but none were really more cost effective over just buying a new Alienware with 1080 GPU, which I did.
After Apple failed to provide any new Macs with NVIDIA GPU’s in its latest update round, we made the decision that it would not be worth the development time for us to continue supporting GPU training for the few older Mac models that have NVIDIA GPU’s (I have one myself), when none of the last 3 generations of Mac have any NVIDIA GPU’s. So we have unfortunately deprecated GPU support for neural networks on OSX.
Apologies for the inconvenience this has caused.
Another cloud option everyone can use is this.
Running Mathematica on the Cloud with Rescale.
Understand the challenges, but a rather unfortunate way to announce this. Strung people on in the betas, no official statement before or at 11.1 release, your premier support portal was down yesterday after 11.1 release so no way to ask as paid customer and Apple and NVIDIA still fully support a set of NVIDIA GPU’s on OS X.
Does this mean that OSX users who previously used nVidia cards with an eGPU over Thunderbolt (e.g. Akitio Node or BizonBox) will no longer be able to do so in v11.1? I was planning on purchasing such a set-up myself (c. $550 for Akitio Node and a used GeForce 980Ti 6GB). Update: Nevermind, I see now from the context that the answer is likely ‘yes’ this won’t work anymore.
Unfortunately correct, you can cross Thunderbolt GPU under MMA 11.1 off the list. I am not saying Apple and NVIDIA will release a Pascal GPU on Thunderbolt, but I’ve not seen a more elegant/functional scientific workstation architecture than a 5K Retina iMac with Thunderbolt GPUs running Mathematica 11.0.1. This pictured little Mac Mini’s USD150 Geforce 950 GPU in a external Thunderbolt enclosure outperforms the unit’s Intel i7 by 60 fold on Mathematica 11.0 MXNet computations. Yes, I have 1070 GPU in a remote kernel on Linux providing similar but far less elegant and with far more systems integration costs. Not Wolfram’s fault for the Apple/NVIDIA relationship rocks, but abandoning the platform that enabled MMA to expand as successfully as it did is disappointing and short sighted (not fully supporting neural networks on a platform is endgame for platform).
NVIDIA Thunderbolt GPU’s seem to be a solid solution and direction under OS X for deep learning and other GPU accelerated computations, just remove Mathematica from the equation 
… The chart in the lower right is hopefully understood at Apple.
Oh, what an unfortunate choice! I just bought 4 BizonBoxes at the beginning of this year only because of the Neural Network framework of Mathematica. It worked really nicely for MMA 11.0.1 on OSX and opened up the large and powerful new expansion of the Wolfram Language. This decisions cuts all OSX users off a large chunk of the new development in the Wolfram Language.
I also do not think that it is just the few old OSX legacy devices with GPUs that you stop supporting, but it is cutting off OSX users from using other techniques to access the neural network framework. It appears that much of the development of the Wolfram Language goes in the direction of machine learning. GPUs are crucial for that. Training times on my networks went down from a day to a couple of minutes.
I believe that there were several solutions available so that OSX users could make use of this exciting new development in the Wolfram Language, but this decision seems to cut all of us off, which is unfortunate I think.
Best wishes,
Marco
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@Marco Thiel, @David Proffer, @Arno Bosse: We are looking into a solution to this. I will have an update for you within a week.
Just having updated to MM 11.1 and finding out that GPU training on Mac OS X fails without any announcement or description in the Documentation of Nettrain[]. As for other users in the forum also for our organization this makes it difficult for us to use Mathematica as training platform for MachineLearning. In our algorithm development department, we have mainly 15" MacBook Pro Mid/Late 2013 notebooks deployed, all with GPUs, also for CUDA/OpenCL Programming within Mathematica (still have to check that CUDALink and OpenCLLink both function in MM 11.1). A consistent GPU support across all platforms, including CUDA where available and OpenCL (works fine with many core CPUs or AMD GPUs) is a great plus for Mathematica.
@Marco Thiel, @David Proffer, @Arno Bosse, @Tobias Kramer : we have decided to resume GPU support for OSX. We are working on a paclet update that we are hoping to release soon. Apologies again for the inconvenience caused!