ROCm is an open-source stack for GPU computation. ROCm is primarily Open-Source Software (OSS) that allows developers the freedom to customize and tailor their GPU software for their own needs while collaborating with a community of other developers, and helping each other find solutions in an agile, flexible, rapid and secure manner. Read More..

In this guide, I will install AMD ROCm + TensorFlow and PyTorch on Ubuntu 22.04 LTS

1. Run the following commands:

sudo apt update && sudo apt upgrade

2. Reboot if necessary or continue to the next step.

This section provides steps to add any current user to a video group to access GPU resources.

1. To check the groups in your system, issue the following command:
2. Add yourself to the render or video group using the following instruction:

sudo usermod -a -G render $LOGNAME
sudo usermod -a -G video $LOGNAME

3. Use of the video group is recommended for all ROCm-supported operating systems.

To add all future users to the video and render groups by default, run the following commands:

echo 'ADD_EXTRA_GROUPS=1' | sudo tee -a /etc/adduser.conf
echo 'EXTRA_GROUPS=video' | sudo tee -a /etc/adduser.conf
echo 'EXTRA_GROUPS=render' | sudo tee -a /etc/adduser.conf

To download and install the amdgpu-install script on the system, run the following commands:

sudo apt update
wget https://repo.radeon.com/amdgpu-install/5.5.1/ubuntu/jammy/amdgpu-install_5.5.50501-1_all.deb
sudo apt install ./amdgpu-install_5.5.50501-1_all.deb

We will install ROCm with Single-version Installation mode, run the following commands:

sudo amdgpu-install --usecase=hiplibsdk,rocm

After everything has been done, reboot your system.

1. Install Docker Engine using bash script, run the following commands:

curl -sSL https://get.docker.com/ | sudo sh

2. The Docker daemon binds to a Unix socket, not a TCP port. By default it’s the root user that owns the Unix socket, and other users can only access it using sudo. The Docker daemon always runs as the root user. Run the following commands:

sudo groupadd docker

sudo usermod -aG docker $USER

newgrp docker

1. Pull the latest public TensorFlow Docker image.

docker pull rocm/tensorflow:latest

3. Once you have pulled the image, run it by using the command below:

docker run -it --network=host --device=/dev/kfd --device=/dev/dri --ipc=host --shm-size 16G --group-add video --cap-add=SYS_PTRACE --security-opt seccomp=unconfined rocm/tensorflow:latest

1. Pull the latest public PyTorch Docker image.

docker pull rocm/pytorch:latest

3. Once you have pulled the image, run it by using the command below:

docker run -it --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --device=/dev/kfd --device=/dev/dri --group-add video --ipc=host --shm-size 8G rocm/pytorch:latest

Make sure before running any program in either TensorFlow or PyTorch, run the following command:

HSA_OVERRIDE_GFX_VERSION=10.3.0