I need openGL 3.3 or higher to use GLSL 3.3. My problem is that I have Mac Os X which doesn't allow to use a version higher than 2.1 of OpenGL. I've installed a virtual machine of Ubuntu inside my system but also has the version 2.1 of OpenGL. I don't understand what's going on because I have an AMD Radeon HD 6490M 256 MB which is compatible with version 4.1 of OpenGL. Is there any way that I can use openGL 3.3 or higher without doing a disk partition?
I don't understand what's going on because I have an AMD Radeon HD 6490M 256 MB which is compatible with version 4.1 of OpenGL
The GPU serves the host machine. The virtual machine sees only some dumb framebuffer device, or the OpenGL API available to the VM running on the host passed through the guest.
If you want to leverage the OpenGL-4 capabilities you must install an OS that can access the GPU natively. Also, if you want to run Linux you'll have to install the proprietary fglrx drivers (also called Catalyst for Linux), as the open source drivers that ship as distribution default haven't caught up yet.
Is there any way that I can use openGL 3.3 or higher without doing a disk partition?
Upgrade to OS X 10.9 when it comes out or grab the beta.
Or find some VM software for OS X that supports VGA passthrough.
If you're willing to repartition you can install Windows or Linux natively and use the drivers from AMD.
Related
I am using MacBook Pro (16-inch, 2019, macOS 10.15.5 (19F96))
GPU
AMD Radeon Pro 5300M
Intel UHD Graphics 630
I am trying to use Pytorch with Cuda on my mac.
All of the guides I saw assume that i have Nvidia graphic card.
I found this: https://github.com/pytorch/pytorch/issues/10657 issue, but it looks like I need to install ROCm, and according to their Supported Operating Systems, it only supports Linux.
Is it possible to run Pytorch on GPU using mac and AMD Graphic card?
No.
CUDA works only with supported NVidia GPUs, not with AMD GPUs.
There is an ongoing effort to support acceleration for AMD GPUs with PyTorch (via ROCm, which does not work on MacOS).
CUDA is a framework for GPU computing, that is developed by nVidia, for the nVidia GPUs. Also, the same goes for the CuDNN framework.
At the moment, you cannot use GPU acceleration with PyTorch with AMD GPU, i.e. without an nVidia GPU. The O.S. is not the problem, i.e. it doesn't matter that you have macOS. It is a matter of what GPU you have.
What you can do though, is that you can either purchase an external nVidia GPU or use some cluster. For example, Google Colab offers PyTorch compatibility.
PyTorch now supports training using Metal.
Announcement: https://pytorch.org/blog/introducing-accelerated-pytorch-training-on-mac/
To get started, install the latest nightly build of PyTorch: https://pytorch.org/get-started/locally/
Answer pre May 2022
Unfortunately, no GPU acceleration is available when using Pytorch on macOS. CUDA has not available on macOS for a while and it only runs on NVIDIA GPUs. AMDs equivalent library ROCm requires Linux.
If you are working with macOS 12.0 or later and would be willing to use TensorFlow instead, you can use the Mac optimized build of TensorFlow, which supports GPU training using Apple's own GPU acceleration library Metal.
Currently, you need Python 3.8 (<=3.7 and >=3.9 don't work) to run it. To install, run:
pip3 install tensorflow-macos
pip3 install tensorflow-metal
You may need to uninstall existing tensorflow distributions first or work in a virtual environment.
Then you can just
import tensorflow as tf
tf.test.is_gpu_available() # should return True
It will be possible in 4 months, around march 2022. See Soumith reply to this question on GitHub. https://github.com/pytorch/pytorch/issues/47702
I'm in the process of setting up Torch on OSX on a 2015 macbook pro with a Radeon GPU using this library (cltorch) for OpenCL support
I can successfully run Torch scripts now, but running this test script which outputs the device and platform being used I get:
Using Apple , OpenCL platform: Apple
Using OpenCL device: Iris Pro
Obviously, I want torch to run on Radeon instead of the integrated Iris, but I have no idea how to do that.
You can use cltorch.setDevice to choose the device, like:
cltorch.setDevice(2)
From TensorFlow's "Getting Started" page:
# Only CPU-version is available at the moment.
$ pip install https://storage.googleapis.com/tensorflow/mac/tensorflow-0.5.0-py2-none-any.whl
I'm not super familiar with using GPU or CUDA libraries, but if I installed TensorFlow inside a Linux VM (say the precise32 available through Vagrant), then would TensorFlow utilize the GPU when running inside that VM?
Probably not. VirtualBox, for example, does not support PCI Passthrough on a MacOS host, only a Linux host (and even then, I'd... uh, not get my hopes up). MacOS ends up so tightly integrated with its GPU(s) that I'd be very dubious that any VM can do it at this point.
As an update: Tensorflow can now use GPUs on Mac OS X. The relevant PR is https://github.com/tensorflow/tensorflow/pull/664 and after a brew install coreutils the Linux installation 'build from source' instructions should work. I see a 10x speedup compared to the CPU version with an NVIDIA gforce 960 and Intel i7-6700K.
Edit/(downdate?): Starting with MacOS Mojave, due some API changes and what appears to be some long-standing beef between Apple and NVidia, drivers for NVidia graphics cards are no longer available. No NVidia means no Cuda means no Tensorflow, nor really any other respectable machine learning. It appears something like Google Collaboratory is the way to go for now.
I used to think that Tesla will not support OpenGL API, but recently leanred that Tesla products also can be used on visualization via OpenGL.
I have a workstation, in which there are 2 Intel E5 CPUs, and 1 Tesla C2050. According to https://developer.nvidia.com/opengl-driver, Tesla C2050 should support at least OpenGL version 3.
Now, I'd like to run a render service program using OpenGL 3.3 on that workstation, but without success.
The following is what I tried.
If I login through RDP remote desktop, the supported OpenGL version is 1.1 due to the virtual graphics adapter. Here, I used tscon commond to reconnect to the pysical console. As a result, the RDP connection lost. When I reconnected, I saw all the windows resized to 800*600 and the detected OpenGL support was still 1.1.
If I login with a monitor pluged to some kind of "integrated graphics adapter", the supported OpenGL version is still 1.1, maybe because the program was started within the screen pluged to the basic adapter. BUt the Tesla GPU does not have a grpahics output port.
I wonder how should I config the host to enable the use of Tesla GPU for OpenGL based rendering.
I have solved this problem.
First, in fact the Tesla C2050 is dedicated video card and has one DVI display port.
What is more important is that, the BIOS on motherboard was set to start up on integrated GPU. Changing this config to PCI-E card solves the problem that unable to access Tesla card.
Next, about graphics API support.
The official driver on NVIDIA site is offering support for OpenGL 4.4.
And, the Tesla card can be used to render via OpenGL just as the same as Quadro or Geforce card. There's no notable difference and no special configuration is necessary.
I have seen this question many times but never found an answer for Windows.
I recently ported my CUDA code to OpenCL.
When testing with an ATI card, the Catalyst drivers contain a CPU OpenCL driver, hence I can run the OpenCL code on the CPU.
When testing with an NVIDIA card, there is no driver for the CPU.
Question is: how can I install (and deploy) a CPU driver when running with an Nvidia card?
Thanks a lot
To use OpenCL on CPU you don't need any driver, you only need OpenCL runtime that supports CPU, which (in case of AMD/ATI) is part of APP SDK. It could be installed no matter what GPU you have. Your end-users would also have to install the APP SDK: currently, there is no way to install OpenCL runtime only.
If you have Intel CPU, you better try Intel OpenCL SDK, which has separate installer. However, AMD APP SDK works on Intel CPUs quite well, but note vice versa.