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Installing CUDA on your DataCrunch.io server

Last Updated: 2022-06-14

Option 0: Using a CUDA image

It's not up to you to install CUDA! Use one of our images that have CUDA pre-installed to avoid the hassle.

If you want to use a specific version, you can do it the manual way as listed below.

Option 1: The easy way

If your goal is to get up and running as fast as you can, this installer script does everything you will need.

It works for Ubuntu 18.04 and 20.04 and installs either CUDA 10.2 or CUDA 11.0. Pick CUDA 10.2 if you are not sure what to take. The script does not yet work for CUDA 10.2 on Ubuntu 20.04.

sudo git clone https://github.com/DataCrunchIO/Install-CUDA.git ~/Install-CUDA

sudo chmod +x ~/Install-CUDA/installer.sh

sudo ~/Install-CUDA/installer.sh

Follow the instructions of the installer and your server will be running CUDA in mere minutes!

Option 2: The slightly-more-work way

If you would like to learn how to install the NVidia driver and CUDA manually; these are the steps the installer takes.

First we will need the CUDA installer which we can find on NVidia’s website. The installer includes an appropriate driver as well.

Note that we do not actually need to install CUDA, the NVidia driver is actually enough since we will be using conda environments which include CUDA. However, if you want to run CUDA accelerated programs outside of conda, it is convenient to have it installed.

The installer fails if the nouveau driver is active. We need to disable it with these initial steps;

sudo bash -c "echo blacklist nouveau > /etc/modprobe.d/blacklist-nvidia-nouveau.conf"


sudo bash -c "echo options nouveau modeset=0 >> /etc/modprobe.d/blacklist-nvidia-nouveau.conf"

sudo update-initramfs -u

sudo reboot -h now

Next we download the wanted CUDA installation file. You can find the most recent one here; NVidia CUDA Following command fetches CUDA 10.2 for Ubuntu 18.04 in the current folder;

wget http://developer.download.nvidia.com/compute/cuda/10.2/Prod/local_installers/cuda_10.2.89_440.33.01_linux.run

Before installing, we will need to install some dependencies:

sudo apt update

sudo apt install build-essential gcc-multilib dkms

The installer will ask what to install, you should select the driver and CUDA toolkit.

Next, we make the file executable and run it:

sudo chmod +x cuda_10.2.89_440.33.01_linux.run

sudo ./cuda_10.2.89_440.33.01_linux.run

Follow the instructions given by the installer. Choose to install the driver and CUDA toolkit. The samples are optional.

Next we will want to configure the runtime library:

bash -c "echo /usr/local/cuda/lib64/ > /etc/ld.so.conf.d/cuda.conf"

sudo ldconfig

We add our path variable by adding “/usr/local/cuda/bin” to our PATH variable:

sudo nano /etc/environment

We want our file to look like this:

After adding, press ctrl+x to exit, save the file when prompted.

At this point, you can check the output of “nvidia-smi”, you should see your GPU’s, driver version and CUDA version. If all is looking good, we will modify our startup script;

sudo nano /etc/rc.local

paste:

#!/bin/bash
nvidia-smi -pm 1
nvidia-smi -e 0
exit 0

/etc/rc.local should look like this:

If you are wondering what the script does;

“#!/bin/bash”: required to let the shell know to use bash. (this is not a normal comment, not a optional line)

“nvidia-smi -pm 1”: This will enable persistence mode to keep the driver loaded (which will increase the speed of some actions).

“nvidia-smi -e 0”: This will disable error correcting on the memory of the GPU. This is safe to do for most applications and will allow using more GPU memory.

“exit 0”: Save and close the script.

Let’s make the file executable and reboot:

sudo chmod +x /etc/rc.local

sudo reboot -h now

And that’s it, you are ready to use your GPU’s! You can confirm the status of persistence mode and ecc by running 'nvidia-smi'