Anyone know how to offline compile OpenGL ES 2.0 shaders for Nvidia Tegra chip? What tool should I use? Thanks.
not supported (anymore)...
http://developer.nvidia.com/beta-forum#/discussion/436/androidtegra-binary-shaders-example-shader-compiler
Have you seen? http://developer.nvidia.com/tegra-android-development-pack
Related
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.
I am a student. My question may be very silly but I want to clear it. I have a device with Vivante GPU with openCL 1.1 version. I want to run tensorflow sample code with SYCL support on GPU. But before trying Tensorflow sample code, I want to try SYCL sample code with openCL 1.1 on GPU.
I have seen several SYCL implementations, like computeCPP, triSYCL, sycl-gtx. All the implementations support openCL 1.2 or above.
Does anyone know the reason why SYCL doesn't support openCL 1.1?
And how feasible will it be the attempt to modify the SYCL open-source code to support openCL 1.1?
The main reason for SYCL 1.2 to require OpenCL 1.2 is because the Khronos intermediate representation SPIR 1.2 requires it. Without SPIR, or any other intermediate representation, a SYCL implementation cannot compile C++ code into device binaries, and would need to convert C++ to OpenCL C, which is quite problematic.
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.
Does Google Tensorflow support OpenCL... or is it still only Cuda?
OpenCL does not appear to be supported yet (April 2017) per this open issue - https://github.com/tensorflow/tensorflow/issues/22 but I keep reading that support exists -- I might be missing something.
tf-coriander is an implementation of Tensorflow for OpenCL 1.2 GPUs. It's based on coriander, which is a general compiler to run NVIDIA® CUDA™ code on OpenCL 1.2 devices. Disclosure: I'm the author, of both projects.
There is OpenCL support via SYCL on Tensorflow, some features are in, others are in progress: https://github.com/tensorflow/tensorflow/issues/22#issuecomment-266050835