I want to install GPU on LX2160A to train AI data,but I don’t know how to install a NVIDIA GPU on LX2160A.
Pls someone could tell me how to operate with it?
Currently we do not support Nvidia cards on the LX2160a, at least with Nvidia’s proprietary binary driver. I have made some slight success with the new open kernel driver and Nvidia’s latest binary driver release, but it is still very unstable and requires a Turing generation card or newer. We are hoping that soon Nvidia will be able to provide better support for their hardware on Aarch64 systems.
OK，thx！
What about AMD GPU card to install on LX2160A ? Could some operation advice give to me?
Honestly I haven’t done much regarding training on the AMD GPUs. In general you are going to need a Navi based card or newer, so you can use ROCm on the LX2160a. The amdgpu kernel module currently does not support Aarch64 for Navi and newer cars, as they require some floating point instructions in the kernel and the code needs to be re-organized so these functions are compiled with different flags and can run safely in the kernel without corrupting the userspace stack.
Most likely I will publish my patches for the nvidia binary driver first just because I already have a card that is working. The amdgpu kernel work is on my radar but GPU availability and pricing hasn’t made it a priority over the past year or so.
Have you already purchased a card, or are you looking to use hardware you already have?
Yes, I have some Nvidia GPU,such as GTX3080 Ti 、P400、A100. But no AMD GPU.
Most likely I will publish my patches for the nvidia binary driver first just because I already have a card that is working.
When will you push your patch?
Not really sure. Most likely I will get back around to testing nvidia’s drivers more this weekend.
Nvidia released a driver update. I will test it and then push a howto guide which is “use at your own risk”
Hi Jon, any update on this?
I will try a RTX A2000 on a HoneyComb LX2 running Ubuntu Server 22.04.3.
Thanks.
Hi, I’ve recently replaced my main PC GPU and now have a spare NVidia GTX 1660Ti which I just installed into my Honeycomb LX2K.
Here are the steps I used on Ubuntu 22.04 LTS:
Install Ubuntu open NVidia drivers
# Install server driver since this is a headless workstation,
# if using as workstation, install nvidia-driver-535-open (haven't tested this yet)
sudo apt install nvidia-driver-535-server-open
# Add configs
cat <<'EOF' | sudo tee /etc/modprobe.d/nvreg_fix.conf
options nvidia NVreg_OpenRmEnableUnsupportedGpus=1
EOF
cat <<'EOF' | sudo tee /etc/modprobe.d/nvidia.conf
options nvidia-drm modeset=1
EOF
# Blacklist noveau driver
cat <<'EOF' | sudo tee /etc/modprobe.d/blacklist-nouveau.conf
blacklist nouveau
options nouveau modeset=0
EOF
# Update initramfs
sudo update-initramfs -u
Reboot so new modules are correctly loaded
After reboot, run nvidia-smi to confirm your system detected the GPU.
Install Docker (if needed/used)
for pkg in docker.io docker-doc docker-compose docker-compose-v2 podman-docker containerd runc; do sudo apt-get remove $pkg; done
# Add Docker's official GPG key:
sudo apt-get update
sudo apt-get install ca-certificates curl gnupg
sudo install -m 0755 -d /etc/apt/keyrings
curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo gpg --dearmor -o /etc/apt/keyrings/docker.gpg
sudo chmod a+r /etc/apt/keyrings/docker.gpg
# Add the repository to Apt sources:
echo "deb [arch="$(dpkg --print-architecture)" signed-by=/etc/apt/keyrings/docker.gpg] https://download.docker.com/linux/ubuntu \
"$(. /etc/os-release && echo "$VERSION_CODENAME")" stable" | \
sudo tee /etc/apt/sources.list.d/docker.list > /dev/null
sudo apt-get update
sudo apt-get install docker-ce docker-ce-cli containerd.io docker-buildx-plugin docker-compose-plugin
Install NVidia container runtime and configure Docker to use it
curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg \\n && curl -s -L https://nvidia.github.io/libnvidia-container/stable/deb/nvidia-container-toolkit.list | \\n sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' | \\n sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list
sudo apt update
sudo apt-get install -y nvidia-container-toolkit
sudo nvidia-ctk runtime configure --runtime=docker
sudo systemctl restart docker
Check GPU in Docker
sudo docker run --rm --runtime=nvidia --gpus all ubuntu nvidia-smi
# Running a MNIST trainning in Docker using GPU
docker run --runtime=nvidia --gpus all -it --rm -v $(pwd):/work -w /work --ipc=host --ulimit memlock=-1 --ulimit stack=67108864 nvcr.io/nvidia/pytorch:23.10-py3
wget https://github.com/pytorch/examples/raw/main/mnist/main.py -o mnist_pytorch.py
time python3 mnist_pytorch.py --epochs=1
2 Likes
Apologies if there is a newer thread on this topic, I didn’t come across it. I’m wondering if there has been any progress with having NVIDIA GPUs play nice with this board. For my own usecase I’m not concerned with pixels, I’d just like to be able to dev and debug CUDA/C++ code on device..
If you are using the EFI firmware then there should be no issues using an Nvidia card with the newer open source kernel module and driver. This should also work with u-boot and device-tree but I have not specifically tested this use case.