Software requirements¶

Operating System¶

IRIS+ Professional can be installed on any Linux distribution that supports docker, however the recommended distribution is Ubuntu 22.04. This guide assumes an Ubuntu 22.04 installation.

Docker¶

All IRIS+ Professional components run as docker containers. Follow the steps below to install docker on Ubuntu 22.04.

1. Install required dependencies¶

sudo apt update && \
sudo apt install -y apt-transport-https ca-certificates curl software-properties-common

2. Add the docker GPG key and repository¶

curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo gpg --dearmor -o /usr/share/keyrings/docker-archive-keyring.gpg && \
echo "deb [arch=amd64 signed-by=/usr/share/keyrings/docker-archive-keyring.gpg] https://download.docker.com/linux/ubuntu $(lsb_release -cs) stable" | sudo tee /etc/apt/sources.list.d/docker.list > /dev/null && \
sudo apt update

3. Install docker¶

sudo apt install -y docker-ce docker-ce-cli docker-compose-plugin containerd.io

4. Add your user to the docker group to run Docker commands without sudo¶

sudo usermod -aG docker $USER && \
newgrp docker

5. Test installation¶

1	`docker run hello-world`

Test Output

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

Share images, automate workflows, and more with a free Docker ID:
 https://hub.docker.com/

For more examples and ideas, visit:
 https://docs.docker.com/get-started/

NVIDIA¶

The steps below will install NVIDIA software components, specifically:

NVIDIA GPU driver
NVIDIA docker runtime

1. Install the NVIDIA driver¶

sudo apt update && \
sudo apt install -y nvidia-driver-550

2. Reboot the system to load the new driver¶

1	`sudo reboot`

If installation is done remotely, login via ssh again to continue.

3. Verify driver installation¶

1	`nvidia-smi`

Example Output

Sun May 26 11:11:33 2024       
+-----------------------------------------------------------------------------------------+
| NVIDIA-SMI 550.54.15              Driver Version: 550.54.15      CUDA Version: 12.4     |
|-----------------------------------------+------------------------+----------------------+
| GPU  Name                 Persistence-M | Bus-Id          Disp.A | Volatile Uncorr. ECC |
| Fan  Temp   Perf          Pwr:Usage/Cap |           Memory-Usage | GPU-Util  Compute M. |
|                                         |                        |               MIG M. |
|=========================================+========================+======================|
|   0  NVIDIA GeForce RTX 3070 ...    Off |   00000000:01:00.0 Off |                  N/A |
| N/A   49C    P0             N/A /   80W |       8MiB /   8192MiB |      0%      Default |
|                                         |                        |                  N/A |
+-----------------------------------------+------------------------+----------------------+

+-----------------------------------------------------------------------------------------+
| Processes:                                                                              |
|  GPU   GI   CI        PID   Type   Process name                              GPU Memory |
|        ID   ID                                                               Usage      |
|=========================================================================================|
|    0   N/A  N/A      1595      G   /usr/lib/xorg/Xorg                              4MiB |
+-----------------------------------------------------------------------------------------+

4. Download the NVIDIA Docker repository GPG key¶

curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg

5. Add the NVIDIA container toolkit repository and update the package cache¶

echo 'deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://nvidia.github.io/libnvidia-container/stable/deb/$(ARCH) /' | sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list && \
sudo apt update

6. Install the NVIDIA Docker runtime¶

sudo apt install -y nvidia-docker2 && \
sudo systemctl restart docker

7. Test the installation¶

Run the following command to test the NVIDIA installation. Note that it may take some time to download the docker image.

docker run --rm --gpus all ubuntu nvidia-smi

Example Output

Mon Jun 24 12:57:08 2024
+---------------------------------------------------------------------------------------+
| NVIDIA-SMI 535.86.10              Driver Version: 535.86.10    CUDA Version: 12.2     |
|-----------------------------------------+----------------------+----------------------+
| GPU  Name                 Persistence-M | Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp   Perf          Pwr:Usage/Cap |         Memory-Usage | GPU-Util  Compute M. |
|                                         |                      |               MIG M. |
|=========================================+======================+======================|
|   0  NVIDIA GeForce RTX 3070 ...    On  | 00000000:01:00.0 Off |                  N/A |
| N/A   50C    P0              24W /  80W |     10MiB /  8192MiB |      0%      Default |
|                                         |                      |                  N/A |
+-----------------------------------------+----------------------+----------------------+

+---------------------------------------------------------------------------------------+
| Processes:                                                                            |
|  GPU   GI   CI        PID   Type   Process name                            GPU Memory |
|        ID   ID                                                             Usage      |
|=======================================================================================|
+---------------------------------------------------------------------------------------+