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I'm working on a computer on which Nvidia drivers and Cuda were installed by someone else so I don't know the method they used to install them.
In the /usr/local/ there were two directories cuda and cuda.10.0. Running nvidia-smi would output:
CUDA Version: 11.0
which made me believe two cuda versions were installed on the system which were causing some errors.
following this question I removed cuda by first doing:
sudo apt-get --purge remove "*cublas*" "cuda*" "nsight*"
and then doing
sudo rm -rf /usr/local/cuda*
(I did not uninstsall nvidia-drivers and Driver Version: 450.80.02 is installed).
Running nvidia-smi still outputs:
CUDA Version: 11.0
How do I uninstall cuda 11? I prefer to have cuda 10 and I can't find where cuda 11 is installed.
Do I need to uninstall nvidia-drivers as well?
The nvidia-smi command does not show which version of CUDA is installed, it shows which CUDA version the installed nVidia driver supports, so there is no problem here, just the incorrect interpretation of the output of this command.
Even if you remove all CUDA installations, nvidia-smi would still show the maximum CUDA version that you can use with this driver.
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Tensorflow official recommendation
So, I'm using Ubuntu 20.4 and I want to use Tensorflow with version 2.3. The offcial Tf sources say that 10.1 is supported, but I couldn't find the installation of CUDA 10.1 for Ubuntu 20.4.
Is it possible to use CUDA 10.1 on Ubuntu and if not, how can I install CUDA 11, so I can make TF 2.3 work?
Yes you can! The usual way would be to build TF from source, which can take many hours (thats atleast what I read). This is required, as tensorflow is compiled with a specific cuda version, thats why they have to match.
After some research I found out, that davidenunes compiled different TF version with different cuda version, so you dont need to do that!
Have a look at his github and pull the version you need. With this I got my 2.3 tf working on Ubuntu 20.4 with cuda 11.
If you have a working cuda 11 installation ready, or you need cuda 11, you can do it this way.
However, you should too be able to install cuda 10.1 on Ubuntu 20.04, but I recommend you to use cuda 11, as with it you can use cudnn 8 which speeds learning up alot.
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I am trying to set up my system for gpu computing for training deep learning models. The tensorflow version required is 1.15 gpu. I would like to know which version of CUDA and CUDnn i have to install in my system?
From the official TF documentation.
For TF >=1.13, CUDA 10. Here
For TF>= 2.1, CUDA 10.1. Here
And, CuDNN will be same for both, CuDNN >= 7.6. Here
I found "TF 1.15 was compiled against CUDA 10.0" here. cuDNN 7.6.4 seems to fit this following this
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Closed last year.
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Is there any way to install hardware monitoring tool like lm-sensors on proxmox VE 4.2 (was installed from ISO: https://www.proxmox.com/en/downloads)? Or maybe there is another way to monitor host's hardware (cpu/mb/hdd temp, fan speed, etc)?
apt-get result:
root#pve:~# apt-get install lm-sensors
Reading package lists... Done
Building dependency tree
Reading state information... Done
E: Unable to locate package lm-sensors
Look's like proxmox use it's own repo, if so how additional repos can be added?
Thanks!
One more question: What monitoring system (with web-interface) can be used on proxmox? or some web front-end for lm-sensors
no need to add repository if the Debian main one is present in /etc/apt/source.list.
Here is mine :
deb http://ftp.fr.debian.org/debian jessie main contrib
# security updates
deb http://security.debian.org jessie/updates main contrib
# PVE pve-no-subscription repository provided by proxmox.com, NOT recommended for production use
deb http://download.proxmox.com/debian jessie pve-no-subscription
Just do apt-get update && apt-get install lm-sensors
Try to install Netdata (https://www.netdata.cloud),
a free monitoring tool, easy to install.
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# apt-get install kvm kvm-source
Reading package lists... Done
Building dependency tree
Reading state information... Done
E: Unable to locate package kvm-source
While installing kvm in my ubuntu12.04 lts machine. I got this error.What i want to do.
It seems this package just doesn't exist:
apt-cache search kvm-source
#if it returns nothing it means the package doesn't exit
As long as we want to search only Ubuntu official repositories, we can also check on http://packages.ubuntu.com/. Here we see that the package kvm exists, but that kvm-source doesn't.
If you want to retrieve the source for this package, you should try
apt-get source kvm
(see man apt-get for more info on the source command)
Edit to answer comment:
You can check if a package is installed with e.g.:
dpkg --status kvm
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I would be interested to try a GPU emulator, but I have tried to use Multi2Sim, GPGPU-sim, and Ocelot, and for each of these three emulators I get a problem for which it seems hard to find a solution on the internet. I will describe the problem I have with each emulator and maybe you can help. First of all, to give you some detailed context, I am using Ubuntu 12.04 LTS.
Multi2Sim says that it is not compatible with 64-bit and so you should compile for 32-bit. If I compile CUDA code for 32-bit, then when I run the compiled executable, I get the error message "CUDA driver version is insufficient for CUDA runtime version." If I compile OpenCL code for 32-bit, then when I run the compiled executable, I find that the function clGetPlatformIDs does not give me the Nvidia OpenCL platform that I get when I compile for 64-bit.
The documentation for GPGPU-sim says:
We have tested OpenCL on GPGPU-Sim using NVIDIA driver version 256.40
http://developer.download.nvidia.com/compute/cuda/3_1/drivers/devdriver_3.1_linux_64_256.40.run
Note the most recent version of the NVIDIA driver produces PTX that is incompatible with this version of GPGPU-Sim.
I have NVIDIA Driver Version 295.49. When I look in "Additional Drivers" from "System Settings" I see two things listed: "NVIDIA accelerated graphics driver (version current) [Recommended]" and "NVIDIA accelerated graphics driver (post-release updates) (version current-updates)". The first one was activated, so I clicked Remove and then the second one automatically became activated. So I decided to just try installing version 256.40 and I got this error message which simply intimidates me:
ERROR: If you are using a Linux 2.4 kernel, please make sure
you either have configured kernel sources matching your
kernel or the correct set of kernel headers installed
on your system.
If you are using a Linux 2.6 kernel, please make sure
you have configured kernel sources matching your kernel
installed on your system. If you specified a separate
output directory using either the "KBUILD_OUTPUT" or
the "O" KBUILD parameter, make sure to specify this
directory with the SYSOUT environment variable or with
the equivalent nvidia-installer command line option.
Depending on where and how the kernel sources (or the
kernel headers) were installed, you may need to specify
their location with the SYSSRC environment variable or
the equivalent nvidia-installer command line option.
When I try to build Ocelot, I get the following, even though I followed the instructions "To pull from the LLVM SVN and build":
ocelot/ocelot/ir/implementation/ExternalFunctionSet.cpp:27:36: fatal error: llvm/Target/TargetData.h: No such file or directory