According to this Vulkan tutorial, I can use vkEnumeratePhysicalDevices to get a list of available GPUs. However, I don't see my external NVIDIA GPU in there, only my Intel iGPU.
This eGPU is connected via Thunderbolt and is running CUDA code just fine. Is there anything I might have missed? Is it supposed to work out of the box?
My machine is running Arch Linux with up-to-date proprietary NVIDIA drivers.
The eGPU is a NVIDIA GTX 1050 (Lenovo Graphics Dock). Is it possible that it just does not support Vulkan somehow?
Vulkan support should work just as well with external GPUs (eGPUs). Seeing the eGPU enumerated as a Vulkan device may require the eGPU to be recoznized by Xorg (or Wayland in the future).
See recently created https://wiki.archlinux.org/title/External_GPU#Xorg for changes probably required in Xorg config.
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My pc has two cards: one Intel and one NVIDIA. Although I select the NVIDIA card in the Cytoscape Desktop OpenCl preferences everything runs in the Intel one.
How can I force it to use the NVIDIA one?
Many thanks!
I already made sure I have installed all the appropriate drivers.
At first, let me explain what i have to do.
My develop enviroment is Tizen OS. may be you are unfamilier that, anyway this os is using linux kernel based redhat and targeting on mobile, tv, etc.. And my target device is consists of exynos 5422 and arm mali-t628.
My main work is implement some gpu library to let tensorflow lite's operation can use the library.
I proceeded to build and install tensorflow lite as a rpm package file.
I am googling many times about the tensorflow and gpu. and get some useless information about cuda. i didnt see any info for my case(tizen and mali gpu).
i think linux have gpu instruction like the cpu or library.. but i cant find them.
can you suggest search keyword or document?
You can go to nvidia’s cuda toolkit page, where you can find the documentation and
Training buttons / options.
Also there’s the CUDA programming guide wich i myself find very usefull and helpull for CUDA.
I believe that one or two of those may help you.
CUDA is for NVidia GPU. Mali is not NVidia's, but ARM's. So you CANNOT use CUDA in your given hardware. Besides, if you want CUDA, you'd better drop Tensorflow-lite and use Tensorflow.
If you want to use CUDA, get a hardware with supported NVidia GPU (e.g., x64 machine with NVidia GPU). Note that you can use Tensorflow-GPU & CUDA/CUDNN in Tizen with x64+NVidia GPU. You just need to be careful on nvidia GPU kernel driver version and userspace driver version. Because NVidia's GPU userspace driver and CUDA/CUDNN are statically built, its Linux drivers are compatible with Tizen. (I've tested tensorflow-gpu, CUDA/CUDNN in Tizen with NVidia driver version 111... probably in winter, 2017)
If you want to use Tizen/Tensorflow-lite in the given hardware, forget CUDA.
My MacBook Pro doesn't have a NVIDIA gpu. So it's not possible to run CUDA. I'm wondering which of the earlier versions of TensorFlow have gpu support for Mac OS? And how can I install on Anaconda?
As stated on the official site:
Note: As of version 1.2, TensorFlow no longer provides GPU support on
Mac OS X.
..so installing any earlier version should be fine. But since your hardware does not have NVIDIA graphics card with CUDA support, it doesn't matter anyway.
In terms of installing TensorFlow on Mac OSX using Anaconda, you can just follow steps nicely described in the official docs
TensorFlow relies on CUDA for GPU use so you need Nvidia GPU. There's experimental work on adding OpenCL support to TensorFlow, but it's not supported on MacOS.
On anecdotal note, I've heard bad things from people trying to use AMD cards for deep learning. Basically AMD doesn't care about deep learning, they change their interfaces without notice so things break or run slower than CPU.
I bought a dell 7559 laptop for deep learning. I got ubuntu 16.04 installed on it but I am having trouble getting caffe and tensorflow on it. The laptop used Nvidia Optimus technology to switch between gpu and cpu to save battery usage. I checked the bios to see if I can set it to use only gpu but there is no option for it. Using bumblebee or nvidia-prime didnt work either. I now have ubuntu 16 with mate desktop environment it is preventing from getting the black screen but didnt help with the cuda issue. I was able to install the drivers and cuda but when I build caffe and tensorflow they fail saying that it didnt detect a gpu. And I wasnt able to install opengl. I tried using several versions of nvidia drivers but it didnt help. Any help would be great. thanks.
I think Bumblebee can enable you to run Caffe/Tensorflow in GPU mode. More generally, it also allows you to run other CUDA programs on a laptop with Optimus technology .
When you have installed Bumblebee correctly (tutorial: Bumblebee Wiki for Ubuntu ), you can invoke the Caffe binary by pepending optirun before the caffe binary. So it goes like the following:
optirun ../../caffe-master/build/tools/caffe train --solver=solver.prototxt
This works for the NVidia DIGITS server as well:
optirun ./digits-devserver
In addition, Bumblebee also works on my dual-graphics desktop PC (Intel HD 4600 + GTX 750 Ti) as well. The display on my PC is driven by the Intel HD 4600 through the HDMI port on the motherboard. The NVidia GTX 750 Ti is only used for CUDA programs.
In fact, for my desktop PC, the "nvidia-prime" (it's actually invoked through the command line program prime-select) is used to choose the GPU that drives the desktop. I have the integrated GPU connect to the display with the HDMI port and the NVidia GPU through a DisplayPort. Currently, the DisplayPort is inactive. The display signal comes from the HDMI port.
As far as I understand, PRIME does so by modifying /etc/X11/Xorg.conf to make either the Intel integrated GPU or the NVidia GPU the current display adapter available to X. I think the PRIME settings only makes sense when both GPUs are connected to some display, which means there need not be an Optimus link between the two GPUs like in a laptop (or, for a laptop with a Mux such as Dell Precision M4600, the Optimus is disabled in BIOS).
More information about the Display Mux and Optimus may be found here: Using the NVIDIA Driver with Optimus Laptops
Hope this helps!
TensorFlow fails to use nvidia card though nvidia driver, cuda toolkit, cudnn installed and configured.
One thing that I suspect is the reason is the nvidia card on my laptop is connected to pci as 3d controller instead of VGA:
00:02.0 VGA compatible controller: Intel Corporation Sky Lake Integrated Graphics (rev 07)
Subsystem: ASUSTeK Computer Inc. Skylake Integrated Graphics
Kernel driver in use: i915_bpo
01:00.0 3D controller: NVIDIA Corporation GK208M [GeForce 920M] (rev a1)
Subsystem: ASUSTeK Computer Inc. GK208M [GeForce 920M]
Kernel modules: nvidiafb, nouveau, nvidia_304
Even the Nvidia xserver settings don't see the GPU:
Is this true that tensorflow can only use the graphic card as VGA?
After three month, I finally figured out even first what the issue is and resolved it. It turned out to be a nvidia issue with Secure Boot.
Feel obliged to thank jorgemf and Yao Zhang for your help at a time I couldn't even good articulate the problem.
Meanwhile I hope my case can help other people having a same problem.
All started with my attempt to install nvidia driver again today. The installation seemed successful but in the end, it says,
Unable to load the “nvidia-drm” kernel module.
So I thought maybe I could manually load the kernel with
modprobe mvidia-drm
but got an error says something like "required key not applicable". Wonder what that meant so googled a bit. It turned out to be application not registered! So that module has been stopped by Secure Boot!
Went back to boot settings and disabled secure boot. Installed nvidia driver again, successful! Now in Nvidia settings it looks like this:
See now the gpu device shows there.
Head further to install cuda and cudnn. Found this github gist super useful: https://gist.github.com/wangruohui/df039f0dc434d6486f5d4d098aa52d07
Last step, just followed the installation on Tensorflow home page. Tested it did run on GPU!
The take-home message is if you fail to install Nvidia driver on linux system, you probably need to disable Secure Boot. Personal opinion, Windows turned this good idea into a nightmare for linux users!