Applies to: Ubuntu 24.04 LTS
Includes: The PWstudio GPU version is like any other mainstream container-based GPU application — at runtime it depends on the NVIDIA GPU driver, Docker, and nvidia-container-toolkit. Before installing the PWstudio GPU version, please make sure these dependencies are already installed on your machine. This guide includes installation steps for each of them for reference.

Before you start, we strongly recommend reading Read this before installing PWstudio first, especially the notes about “copy-paste” and “the command prompt is not part of the command.” If you don't yet know how to use a tool to log in to a server or upload/download files, read the MobaXterm beginner tutorial first.

If your machine already had the driver installed, do the check in 1.2 first — if there's output, you can skip this entire chapter 1 and go straight to “2. Install Docker”.

lspci | grep -i nvidia

• There's output (a line containing “NVIDIA”): the GPU was detected — continue on.
• No output: the GPU wasn't detected. Contact your administrator to confirm the hardware before continuing with the driver install.

nvidia-smi

If you see the GPU model, driver version, CUDA version, etc., the driver is already installed, skip this step. That means you can skip 1.3~1.7 entirely and go straight to chapter 2, “Install Docker”. If it says “command not found,” it isn't installed yet — continue with the steps below.

# 1. Disable nouveau first
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
 
# 2. Reboot the system (so disabling nouveau takes effect)
sudo reboot

After running sudo reboot, the terminal connection will drop — this is expected and necessary, don't worry. Wait 1-2 minutes for the server to finish rebooting, then log back in to the server (e.g. reconnect with MobaXterm), and continue with step 1.4 below.

sudo apt update

This refreshes the package source list. You'll see the check status for each source scroll by — that's normal, just let it finish; no further action needed.

sudo apt install -y ubuntu-drivers-common

# Install driver version 535 (supports CUDA 12.1 and above)
sudo ubuntu-drivers install nvidia:535

To see what driver version the system recommends, you can first run sudo ubuntu-drivers list for reference.

This step downloads and installs the driver, which may take a few minutes depending on your network speed — please be patient and don't force-close the terminal partway through.

sudo reboot

Again, the terminal will disconnect after this — that's normal. Wait 1-2 minutes, then log back in to the server.

nvidia-smi

Note that nvidia-smi has a hyphen in the middle and no space — we recommend copying and pasting it directly rather than typing it manually.

If you see the GPU model, driver version, CUDA version, etc., the driver was installed successfully (as shown in the example below — a table like this means it worked):

NVIDIA-SMI 535.309.01 is the version number of the command-line tool itself — the executable used to query the GPU's status;

Driver Version: 535.309.01 is the version of the NVIDIA GPU kernel driver — the core low-level program that actually controls the GPU hardware and talks to the OS kernel;

CUDA Version: 12.2 is the highest CUDA runtime version this driver can support. As long as the CUDA version required by a CUDA application (such as PyTorch or TensorFlow) is 12.2 or lower, it can use the GPU normally.

Before installing, please check whether Docker is already installed on your machine:

docker --version
# If it prints something like "Docker version 29.6.1, build 8900f1d", Docker is already installed — you can skip this chapter and go straight to "3. Install nvidia-container-toolkit"
 
systemctl status docker
# Check whether the Docker service is running — if it shows "active (running)", it's not only installed but also running

sudo apt update
sudo apt install -y ca-certificates curl

# 1. Create the directory to hold the key
sudo install -m 0755 -d /etc/apt/keyrings
 
# 2. Download and save Docker's GPG key from the Alibaba Cloud mirror
sudo curl -fsSL http://mirrors.aliyun.com/docker-ce/linux/ubuntu/gpg -o /etc/apt/keyrings/docker.asc
sudo chmod a+r /etc/apt/keyrings/docker.asc
 
# 3. Add the Docker package source (also via the Alibaba Cloud mirror)
echo \
  "deb [arch=$(dpkg --print-architecture) signed-by=/etc/apt/keyrings/docker.asc] http://mirrors.aliyun.com/docker-ce/linux/ubuntu \
  $(. /etc/os-release && echo "${UBUNTU_CODENAME:-$VERSION_CODENAME}") stable" | \
  sudo tee /etc/apt/sources.list.d/docker.list > /dev/null

We recommend copying and pasting all of step 3 (from echo to the final > /dev/null) as one whole block rather than running it line by line, since it's really a single command that spans multiple lines. Here, $(dpkg –print-architecture) and $(. /etc/os-release && echo …) are both system variables that get automatically substituted with your system's architecture and version codename — no manual editing needed.

# Refresh the package index again
sudo apt update
 
# Install Docker and its components
sudo apt install -y docker-ce docker-ce-cli containerd.io docker-buildx-plugin docker-compose-plugin

This downloads and installs Docker and its related components, which may take a minute or two depending on your network speed.

sudo systemctl start docker
sudo systemctl enable docker

sudo docker run --rm swr.cn-north-4.myhuaweicloud.com/ddn-k8s/docker.io/hello-world:latest
 
# Seeing output like the following means the install succeeded
# Hello from Docker!
# This message shows that your installation appears to be working correctly.
 
# To generate this message, Docker took the following steps:
#  1. The Docker client contacted the Docker daemon.
#  2. The Docker daemon pulled the "hello-world" image from the Docker Hub. (amd64)
#  3. The Docker daemon created a new container from that image which runs the executable that produces the output you are currently reading.
#  4. The Docker daemon streamed that output to the Docker client, which sent it to your terminal.
 
#  To try something more ambitious, you can run an Ubuntu container with:
#   $ docker run -it ubuntu bash

As long as you see the words “Hello from Docker!” in the terminal, Docker was installed successfully and is working correctly — you can move on to the next step with confidence.

sudo usermod -aG docker $USER

$USER here is a system variable that gets automatically substituted with your currently logged-in username — you don't need to manually change it to your own username, just copy and paste it as-is.

Note: you need to log out and back in (or restart your session) for this to take effect. That is, after running this command, exit the terminal (type exit) and log back in to the server for the setting to actually apply.

For Ubuntu 24.04, we recommend installing via NVIDIA's official apt repository, rather than the now-deprecated apt-key add method.

Link: http://223.70.160.60:9999/qflow/nvidia-container-toolkit_1.19.1_deb_amd64.tgz
Username: pwmat
Password: SimplyTheBest

You can also download it directly on the server (if your server has direct access to the outside internet, you can skip the “download in browser, then upload” step and run the command below directly in the server's terminal):

wget --user=pwmat --password=SimplyTheBest "http://223.70.160.60:9999/qflow/nvidia-container-toolkit_1.19.1_amd64.deb.tgz"

If your server doesn't have direct outside internet access, do it the same way as the driver and Docker earlier: download it in a browser first, then upload it to the server with MobaXterm.

tar -zxvf nvidia-container-toolkit_1.19.1_deb_amd64.tgz
cd nvidia-container-toolkit_1.19.1_deb_amd64
sudo dpkg -i *.deb

The first command extracts the archive, the second enters the extracted folder, and the third installs every .deb package inside it.

sudo nvidia-ctk runtime configure --runtime=docker

sudo systemctl restart docker

Steps 3.3 and 3.4 only need to be run once — once configured, they stay in effect, so you don't need to redo them every time before using Docker.

sudo docker run --rm --gpus all swr.cn-north-4.myhuaweicloud.com/ddn-k8s/docker.io/nvidia/cuda:12.4.0-base-ubuntu22.04 nvidia-smi

If nvidia-smi inside the container produces normal output (as shown below — the GPU model, driver version, etc.), nvidia-container-toolkit was installed successfully and the container can now use the GPU.

Click the link below, starting from “Install the server.py service on the host machine”: Go to "Install the server.py service on the host machine", and follow the guide through to the end.