garg-aayush/Steps_multiple_cuda_environments.md

## Steps_multiple_cuda_environments.md

      
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              Steps_multiple_cuda_environments.md
            
          
    Steps to manage multiple CUDA environments


Latest Update: May 19th, 2024

This gist contains all the steps required to:

Install multiple CUDA versions (e.g., CUDA 11.8 and CUDA 12.1
Manage multiple CUDA environments on Ubuntu using the utility called environment modules.
Use this approach to avoid CUDA environment conflicts.


Environment Modules is a package that provides for the dynamic modification of a user's environment via modulefiles. You can find more on it at https://modules.readthedocs.io/en/latest/

1. Install the Compatible NVIDIA Drivers (if required)


Add PPA GPU Drivers Repository to the System
sudo add-apt-repository ppa:graphics-drivers/ppa


Check GPU and available drives
ubuntu-drivers devices
# install it using: sudo ubuntu-drivers


Install the compatible driver
# best to allow Ubuntu to autodetect and install the compatible nvidia-driver
sudo ubuntu-drivers install

For example, I tried to install nvidia-driver-545 using sudo ubuntu-drivers install nvidia:545 command. However, I was unable to install it. There was always some or the other issue.


Note:
Please restart your system after installing the nvidia driver. Ideally, you should be able to get GPU state and stats using nvidia-smi


Check the installed NVIDIA driver
nvidia-detector 


Additionally, you can also install NVIDIA drivers using the Software & Updates Ubuntu app. Just go to the Additional Drivers tab, choose a driver, and click Apply Changes.


2. Install CUDA 11.8 and CUDA 12.1


Go to the https://developer.nvidia.com/cuda-toolkit-archive and select CUDA Toolkit 11.8 from the available options.


Choose your OS, architecture, distribution, version, and installer type. For example, in my case:


Option
value


OS
Linux


Architecture
x86_64


Distribution
Linux


Version
22.04


Installer type
deb(local)


Follow the provided installation instructions by copying and pasting the commands into your terminal. This will install CUDA 11.8. Use the following commands:
wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/cuda-ubuntu2204.pin
sudo mv cuda-ubuntu2204.pin /etc/apt/preferences.d/cuda-repository-pin-600  
wget https://developer.download.nvidia.com/compute/cuda/11.8.0/local_installers/cuda-repo-ubuntu2204-11-8-local_11.8.0-520.61.05-1_amd64.deb
sudo dpkg -i cuda-repo-ubuntu2204-11-8-local_11.8.0-520.61.05-1_amd64.deb
sudo cp /var/cuda-repo-ubuntu2204-11-8-local/cuda-*-keyring.gpg /usr/share/keyrings/
sudo apt-get update
sudo apt-get -y install cuda


Similarly, install CUDA 12.1 using the following commands:
wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/cuda-ubuntu2204.pin
sudo mv cuda-ubuntu2204.pin /etc/apt/preferences.d/cuda-repository-pin-600
wget https://developer.download.nvidia.com/compute/cuda/12.1.0/local_installers/cuda-repo-ubuntu2204-12-1-local_12.1.0-530.30.02-1_amd64.deb
sudo dpkg -i cuda-repo-ubuntu2204-12-1-local_12.1.0-530.30.02-1_amd64.deb
sudo cp /var/cuda-repo-ubuntu2204-12-1-local/cuda-*-keyring.gpg /usr/share/keyrings/
sudo apt-get update
sudo apt-get -y install cuda


Make sure to copy and execute the commands above in your terminal to install CUDA 11.8 and CUDA 12.1 on your system.


3. Install cuDNN library


Go to https://developer.download.nvidia.com/compute/cudnn/redist/cudnn/linux-x86_64/ and download the cuDNN tar for CUDA 11.x. Note that you might need to create a developer's account first.


Untar the downloaded file using the following command:
tar -xvf cudnn-linux-x86_64-9.1.0.70_cuda11-archive.tar.xz # CUDA 11.x
tar -xvf cudnn-linux-x86_64-9.1.0.70_cuda12-archive.tar.xz # CUDA 12.x


Copy the cuDNN files to the CUDA toolkit files:
# for CUDA 11.8
cd cudnn-linux-x86_64-9.1.0.70_cuda11-archive/
sudo cp include/cudnn*.h /usr/local/cuda-11.8/include
sudo cp lib64/libcudnn* /usr/local/cuda-11.8/lib64

# for CUDA 12.1
cd cudnn-linux-x86_64-9.1.0.70_cuda12-archive/
sudo cp include/cudnn*.h /usr/local/cuda-12.1/include
sudo cp lib64/libcudnn* /usr/local/cuda-12.1/lib64


Make the files executable:
sudo chmod a+r /usr/local/cuda-11.8/include/cudnn*.h /usr/local/cuda-11.8/lib64/libcudnn*
sudo chmod a+r /usr/local/cuda-12.1/include/cudnn*.h /usr/local/cuda-12.1/lib64/libcudnn*


Note:
Strictly speaking, you are done with the CUDA setup. You can use it by adding the CUDA bin and library path to the PATH and LD_LIBRARY_PATH environment variables. For example, you can set up CUDA 11.8 by adding the following lines in the ~/.bashrc:
PATH=/usr/local/cuda-11.8/bin:$PATH
LD_LIBRARY_PATH=/usr/local/cuda-11.8/extras/CUPTI/lib64:$LD_LIBRARY_PATH
LD_LIBRARY_PATH=/usr/local/cuda-11.8/lib64:$LD_LIBRARY_PATH
Similarly, you can set up CUDA 12.1. However, manually changing the paths every time can be cumbersome!


Note: In case, you only want to install either of the one, CUDNN 11.x or CUDNN 12.x. The simpler way is to go to https://developer.nvidia.com/cudnn-downloads and install the CUDNN 11.x or CUDNN 12.x similar to CUDA installation.

4. Manage multiple CUDA versions using environment modules

a) Install the environment modules utility:


Run the following commands:
    sudo apt-get update
    sudo apt-get install environment-modules

Check the installation:
# Check the installation by running
module list


You should see a list of default installed modules like git and maybe their versions displayed when you run the command module list. This confirms that the environment modules utility has been successfully installed on your system.

b) Create modulefiles for CUDA distributions


Note: You might need root permissions to create directories and files. Use sudo in that case.


Create a directory /usr/share/modules/modulefiles/cuda to hold modulefiles for cuda distributions
sudo mkdir -p /usr/share/modules/modulefiles/cuda


Create a modulefile /usr/share/modules/modulefiles/cuda/11.8 for CUDA 11.8 and add the following lines:
#%Module1.0
##
## cuda 11.8 modulefile
##

proc ModulesHelp { } {
    global version
    
    puts stderr "\tSets up environment for CUDA $version\n"
}

module-whatis "sets up environment for CUDA 11.8"

if { [ is-loaded cuda/12.1 ] } {
module unload cuda/12.1
}

set version 11.8
set root /usr/local/cuda-11.8
setenv CUDA_HOME	$root

prepend-path PATH $root/bin
prepend-path LD_LIBRARY_PATH $root/extras/CUPTI/lib64
prepend-path LD_LIBRARY_PATH $root/lib64
conflict cuda


Similarly, create a modulefile /usr/share/modules/modulefiles/cuda/12.1 for CUDA 12.1 and add the following lines:
#%Module1.0
##
## cuda 12.1 modulefile
##

proc ModulesHelp { } {
    global version
    
    puts stderr "\tSets up environment for CUDA $version\n"
}

module-whatis "sets up environment for CUDA 12.1"

if { [ is-loaded cuda/11.8 ] } {
module unload cuda/11.8
}

set version 12.1
set root /usr/local/cuda-12.1
setenv CUDA_HOME	$root

prepend-path PATH $root/bin
prepend-path LD_LIBRARY_PATH $root/extras/CUPTI/lib64
prepend-path LD_LIBRARY_PATH $root/lib64
conflict cuda


c) Make CUDA 11.8 the default cuda version


Create a file /usr/share/modules/modulefiles/cuda.version to make CUDA 11.8 the default cuda module:
#%Module
set ModulesVersion 11.8


Note: make sure to reload your terminal.

5. Changing and Viewing the CUDA Module


To change and view the loaded CUDA module, you can use the following commands:
# Check the currently loaded module
module list
# Check the available modules
module avail

# Load a specific cuda version
module load cuda/12.1
# Unload the currently loaded CUDA module
module unload cuda
# Load CUDA 11.8
module load cuda/11.8

# verify the paths of the loaded CUDA
nvcc --version # should give the loaded CUDA version
echo $CUDA_HOME
echo $PATH
echo $LD_LIBRARY_PATH


Note: You can add additional CUDA versions or other packages by creating corresponding modulefiles and following the steps outlined in this gist.

6. Some Useful Tips

a) nvidia-smi does not works


Sometime, after Ubuntu update or some other weird issue. The system might not be able to detect drivers. For example, you get erros such as nvidia-smi has failed because it couldn't communicate with the NVIDIA driver. Make sure that the latest NVIDIA driver is installed and running. The best solution is to remove the current drivers and reinstall the compatible nvidia-driver.
# removes all the nvidia drivers
sudo apt-get --purge remove "*nvidia*" "libxnvctrl*"
# reinstall the compatible driver and restart
sudo ubuntu-drivers install


b) Purge CUDA from your computer


DO IT AT YOUR OWN RISK

```bash
# removes all the nvidia drivers
sudo apt-get --purge remove "*nvidia*" "libxnvctrl*"
# remove all cuda versions
sudo apt-get --purge remove "*cuda*" "*cublas*" "*cufft*" "*cufile*" "*curand*"  "*cusolver*" "*cusparse*" "*gds-tools*" "*npp*" "*nvjpeg*" "nsight*" "*nvvm*"
# remove all cuda folders
sudo rm -rf /usr/loca/cuda*
```

Resources and helpful links


https://ubuntu.com/server/docs/nvidia-drivers-installation
https://developer.nvidia.com/cuda-toolkit-archive
https://developer.nvidia.com/cudnn-downloads
Option	value
OS	Linux
Architecture	x86_64
Distribution	Linux
Version	22.04
Installer type	deb(local)