It seems as though GPU drivers do not seem to work on the Deep Learning AMI (Ubuntu 180.04) g4dn.xlarge instances I have set up for the past few hours. nvidia-smi gives output:
NVIDIA-SMI has failed because it couldn't communicate with the NVIDIA driver. Make sure that the latest NVIDIA driver is installed and running.
and torch.cuda.is_available() returns false.
Does anyone know why this is happening?
Related
I'm currently trying to run ARM assembly on my DE series board. However when I try to open my project I get the following error on the Intel FPGA Monitor Program:
Error running 'make DETECT_DEVICES'. (java.io.IOException: The pipe is
being closed)
How can I solve that?
Depends on the OS you are running. If you are running on Windows 11, it's not going to work because there is no USB Blaster II driver support for it unfortunately.
(see: https://community.intel.com/t5/Programmable-Devices/USB-Blaster-for-Windows-11/m-p/1422212#M87272)
NazrulNaim_Intel Employee
10-16-2022 11:57 PM
Hi Fari,
Regarding the issue with the USB blaster, as mention by ak6dn there will be issues regarding installing the USB blaster in Windows 11 because It is not officially supported yet by Intel. We cannot sure that it will 100% works in windows 11. As for work around to troubleshoot the issue, you can follow the instruction from the link that I have attached below.
https://www.terasic.com.tw/wiki/Altera_USB_Blaster_Driver_Installation_Instructions
Regards,
Nazrul Naim
I suggest you use a VM with Windows 10 if that's the case.
The FPGA monitor program requires WSL1 with a Linux distro installed on your PC. Make sure WSL1 is set to default, WSL2 is not supported and will result in crashes while trying to compile your code.
To install WSL1 and set it to default, follow this link:
https://learn.microsoft.com/en-us/windows/wsl/install
After installation, launch the installed distro and follow this link step by step:
https://www.intel.com/content/www/us/en/docs/programmable/683525/21-3/installing-windows-subsystem-for-linux.html
Although the document refers to the NIOS II EDS it is also applicable for the FPGA monitor.
Also make sure that the version of Quartus corresponds to the version of the FPGA monitor and keep the Linux distro running in the background while compiling.
On starting any Genymotion device, I get the error:
Unable to start the virtual device. VirtualBox cannot start the virtual device. Your CPU is incompatible with virtualization technologies. For more information, check the log files. Please refer to: www.genymotion.com/faq/logs
My CPU is the AMD Ryzen 3 2200G 3.5 GHz Quad-Core Processor, which includes a built in graphics chip (I think that's what it's called). I think my question is, is my CPU really incompatible with Genymotion? I have used VirtualBox before, so I know I can run a virtual machine. Is there any way to get around this error, or is my CPU really incompatible with this? If there is any questions on anything that would help, please ask, because I would really like to get Genymotion working.
Are you trying to run Genymotion from a VM? If so, it will not work. Genymotion needs to be run from a real OS, not a VM.
Otherwise, see Genymotion FAQ
I'm wondering if it'd be possible to use TensorFlow GPU running on an Ubuntu VM that's running on a Windows machine.
A search shows that this was not possible around 1.5-2 years ago as tf on ubuntu was unable to access the gpu through the VM (1, 2). According to what it says here, it seems like it could technically be a remote possibility. I was wondering if anyone has a more up-to-date experience.
Anyone know if it's possible nowadays?
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 have used ATI Stream SDK on windows XP SP3 and implemented one algorithm on GPU. But Now I am interested in scaling this algorithm on multiple GPUs on mutiple machines I switched to UBUNTU to use MPI ( To send messages ).
I googled this but I got references for installation on SLES and RHEL but I am looking for UBUNTU 9.04.
Thanks
GG
AMD is switching to OpenCL based API soon. May be it will be worthwhile holding your horses till the OpenCL API stabilizes. Cuda is far ahead of the curve in terms of GPU usability, there is a nice project called MAGMA which is bringing together the LAPACK library for joint CPU-GPU usage.
I know of people who are using the ATI Stream SDK and ACML-GPU on Ubuntu without any special problems -- that is, no problems that they wouldn't have on any other Linux distro.
If you can get the Catalyst drivers installed correctly (which in this case will probably mean compiling your kernel modules) and your X windows configured correctly (especially DRI module, and there are security issues if you want Stream to work with remote access) it should work.
I'm tempted to ask/comment how you plan to share GPUs between multiple MPI processes, but that's probably wandering off-topic.