Can you post your full example that you think should be working? Its impossible to debug this without more information.

Again, can you post the code that is failing for your example?

Also, what is the failure message with your "I first tried the following example available on the Wolfram website" example?

The documentation is doing something slightly confusing. It is reassigning to the value "lenet".

Try the following. Rename "lenet" to "trainedlenet" after it has been trained. You will want to re-run everything from the top after doing this:

lenet = NetChain[{ConvolutionLayer[20, 5], Ramp, PoolingLayer[2, 2], 
   ConvolutionLayer[50, 5], Ramp, PoolingLayer[2, 2], FlattenLayer[], 
   500, Ramp, 10, SoftmaxLayer[]}, 
  "Output" -> NetDecoder[{"Class", Range[0, 9]}], 
  "Input" -> NetEncoder[{"Image", {28, 28}, "Grayscale"}]];

trainedlenet = NetTrain[lenet, trainingData, ValidationSet -> testData, 
  MaxTrainingRounds -> 3, TargetDevice -> "GPU"]

This way you can be sure to not get them mixed up.

In your picture, some of the code is black and some of the code is blue.

The syntax highlighter highlights things in blue if they are undefined. This means that it thinks "lenet", "trainingdata" and "testData" are all undefined for some reason.

I've attached my notebook. It shows a simple evaluation which leads to a failure (because I don't have a compatible graphics card). But you can see it runs.

Attachments:

example.nb

There does seem to be problem. It probably only happens with the your graphics card or driver. We haven't been able yet to reproduce it.

What Operating System are you using? Additionally, do you know what your graphics card is?

We would like to reproduce the problem as closely as possible. If you can, please send an email to support@wolfram.com about this issue. If you can, include a notebook where you have evaluated the command SystemInformation[]. This will tell us more about your computer and its graphics card, helping us to fix this issue.

I am running Mathematica on Ubuntu 16.04. Here is the same info for my system:

Perhaps it is related to the CUDA Compute Capabilities of my Geforce GTX 460.

Gijsbert

Trying this on my notebook. Mathematica 11.1 on Win7 64-bit, GPU is Quadro K2100M with compute capabilities 3.0, latest drivers. There's also an Intel integrated graphics card, but Mathematica is set to us NVIDIA:

CUDAInformation[]
{1->{Name->Quadro K2100M,Clock Rate->666500,Compute Capabilities->3.,GPU Overlap->1,Maximum Block Dimensions->{1024,1024,64},Maximum Grid Dimensions->{2147483647,65535,65535},Maximum Threads Per Block->1024,Maximum Shared Memory Per Block->49152,Total Constant Memory->65536,Warp Size->32,Maximum Pitch->2147483647,Maximum Registers Per Block->65536,Texture Alignment->512,Multiprocessor Count->3,Core Count->96,Execution Timeout->1,Integrated->False,Can Map Host Memory->True,Compute Mode->Default,Texture1D Width->65536,Texture2D Width->65536,Texture2D Height->65536,Texture3D Width->4096,Texture3D Height->4096,Texture3D Depth->4096,Texture2D Array Width->16384,Texture2D Array Height->16384,Texture2D Array Slices->2048,Surface Alignment->512,Concurrent Kernels->True,ECC Enabled->False,TCC Enabled->False,Total Memory->2147483648}}

CUDADriverVersion[]
368.39

And I get this message:

NetTrain::badtrgdev: TargetDevice -> GPU could not be used, please ensure that you have a compatible NVIDIA graphics card and have installed the latest drivers.

Any ideas why this fails? Thank you in advance.

Here is my info:

In[9]:= CUDAInformation[]

Out[9]= {1 -> {"Name" -> "GeForce GTX 660", "Clock Rate" -> 888500, 
   "Compute Capabilities" -> 3., "GPU Overlap" -> 1, 
   "Maximum Block Dimensions" -> {1024, 1024, 64}, 
   "Maximum Grid Dimensions" -> {2147483647, 65535, 65535}, 
   "Maximum Threads Per Block" -> 1024, 
   "Maximum Shared Memory Per Block" -> 49152, 
   "Total Constant Memory" -> 65536, "Warp Size" -> 32, 
   "Maximum Pitch" -> 2147483647, 
   "Maximum Registers Per Block" -> 65536, "Texture Alignment" -> 512,
    "Multiprocessor Count" -> 6, "Core Count" -> 192, 
   "Execution Timeout" -> 1, "Integrated" -> False, 
   "Can Map Host Memory" -> True, "Compute Mode" -> "Default", 
   "Texture1D Width" -> 65536, "Texture2D Width" -> 65536, 
   "Texture2D Height" -> 65536, "Texture3D Width" -> 4096, 
   "Texture3D Height" -> 4096, "Texture3D Depth" -> 4096, 
   "Texture2D Array Width" -> 16384, 
   "Texture2D Array Height" -> 16384, 
   "Texture2D Array Slices" -> 2048, "Surface Alignment" -> 512, 
   "Concurrent Kernels" -> True, "ECC Enabled" -> False, 
   "TCC Enabled" -> False, "Total Memory" -> 1610612736}}

In[10]:= CUDAResourcesInformation[]

Out[10]= {{"Name" -> "CUDAResources", "Version" -> "10.5.0", 
  "BuildNumber" -> "", "Qualifier" -> "Win64", 
  "WolframVersion" -> "10.5+", "SystemID" -> {"Windows-x86-64"}, 
  "Description" -> "{ToolkitVersion -> 7.0, MinimumDriver -> 300.0}", 
  "Category" -> "", "Creator" -> "", "Publisher" -> "", 
  "Support" -> "", "Internal" -> False, 
  "Location" -> 
   "C:\\Users\\David\\AppData\\Roaming\\Mathematica\\Paclets\\\
Repository\\CUDAResources-Win64-10.5.0", "Context" -> {}, 
  "Enabled" -> True, "Loading" -> Manual, 
  "Hash" -> "79fa747a52a45bf2d78e2c3516c80061"}}

No, Classify has no support for GPU (as you can see from docs).

I have upgraded to a GTX 1050 with compute capability 6.1 and my error is gone. The training results are wrong however. Without TargetDevice->"GPU" the results are OK, but with TargetDevice->"GPU" I am seeing this while training:

and the resulting recognition is also very poor. I am running Mathematica 11.0.1.0 on 64-bit Ubuntu 4.4.0-51-generic, the Nvidia driver version is 875.20 and the CUDA version is 8.0.

How can I troubleshoot this?

GW

FYI, downgrading to CUDA 7.5 does not solve the issue.

GW

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.

This issue seems to be resolved in version 11.1 (at least for my GTX 1050)!

Gijsbert

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.

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.

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.

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

@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!

Dear Sebastian,

thank you very much for that helpful reply. I will try to find some time to post some easy instructions of how to make the BizonBox work with Mathematica and show some benchmarks.

Thanks a lot,

Marco

EDIT: Sorry for that. I have fixed the thing. Actually, it works better now than in 10.4. I did not need to perform one step and that caused trouble now. The thing works out of the box.

FYI: Nvidia announces new Titan Xp GPU along with upcoming beta Pascal drivers for the Mac https://9to5mac.com/2017/04/06/nvidia-titan-xp-beta-pascal-drivers-mac/ Update: We have reached out to Nvidia for a statement about compatibility down the line with lesser 10-series cards, and I’m happy to report that Nvidia states that all Pascal-based GPUs will be Mac-enabled via upcoming drivers. This means that you will be able to use a GTX 1080, for instance, on a Mac system via an eGPU setup, or with a Hackintosh build. Exciting times, indeed.

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.

Another cloud option everyone can use is this.

Running Mathematica on the Cloud with Rescale.

Video: https://support.rescale.com/customer/en/portal/articles/2651932-running-mathematica-on-the-cloud-with-rescale

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.