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AMD ROCm™ installation

AMD ROCm™ is the first open-source software development platform for HPC/Hyperscale-class GPU computing. AMD ROCm™ brings the UNIX philosophy of choice, minimalism and modular software development to GPU computing. Please see the AMD Open Software Platform for GPU Compute and ROCm Informational Portal pages for more information.

More detailed information on ROCm installation is available on the Deploy ROCm on Linux page.

Installation of the AMD ROCm™ software package can be challenging without a clear understanding of the pieces involved and the flow of the installation process. This introductory material shows how to install ROCm on a workstation with an AMD GPU card that supports the AMD GFX9 architecture. A follow on blog will discuss installing ROCm in other environments, such as a Docker Container, Linux LXC or a full HPC installation.

The website https://rocm.docs.amd.com contains links to the Release, Support and API documentation for ROCm. Please refer to the Installation Guide and Hardware / Software Support Guide for the software and hardware supported by the V 5.7 release of ROCm. This post will be based on an Ubuntu 22.04 operating system and the AMD MI (MI210, MI250, etc.) series GPU card. The full installation process is documentated in the Installation Guide.

AMD ROCm™ is a brand name for the ROCm open software platform supporting GPUs using AMD’s CDNA, and RDNA GPU architectures. The platform includes drivers and runtimes for libraries and developer tools.

Three installation options will be described in this blog post:

  1. Installation of ROCm using an AMD provided script.

  2. Support for multiple ROCm versions on one system.

  3. Installation of ROCm using Ubuntu’s apt-get.

Option 1

AMD provides an installation script for specific operating system and ROCm versions. The script name and download location can be different for each combination of O/S and ROCm so check the How to Install page for your specific combination. We are using Ubuntu 22.04 and installing ROCm 5.7.1 and find that the script is named amdgpu-install_5.7.50701-1_all.

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sudo apt update
wget https://repo.radeon.com/amdgpu-install/5.7.1/ubuntu/jammy/amdgpu-install_5.7.50701-1_all.deb
sudo apt install ./amdgpu-install_5.7.50701-1_all.deb

Once the amdgpu-install script has been extracted, it can be used to install the kernel code, libraries and developer code. For a typical HPC environment HIP, ROCm and the kernel drivers should be sufficient:

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sudo amdgpu-install --usecase=hiplibsdk,rocm,dkms

Additional libraries can be installed and the list of available use cases can be found using:

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sudo amdgpu-install --list-usecase

The ROCm code installs to /opt/rocm by default. You can verify that ROCm is installed by running

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/opt/rocm/bin/rocminfo

and checking that the card was detected by the software. The supported GPU card will start with “GFX9”.

Option 2

If ROCm is already installed, an old version can be removed and a new version may be installed. Alternatively, an additional version may be installed while keeping the old version. Installing an additional version creates a multi-version system and care must be used to ensure the proper paths are in the environment variables. For example, the current /opt/rocm may now be /opt/rocm-5.4.0 or /opt/rocm-5.3.2 depending on the installed ROCm versions.

To remove all old versions use:

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sudo amdgpu-uninstall --rocmrelease=all

AMD provides an installation script for specific operating system and ROCm versions. The script name and download location can be different for each combination of O/S and ROCm so check the How to Install page for your specific combination. We are using Ubuntu 22.04 and installing ROCm 5.7.1 and find that the script is named amdgpu-install_5.7.50701-1_all.

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sudo apt update
wget https://repo.radeon.com/amdgpu-install/5.7.1/ubuntu/jammy/amdgpu-install_5.7.50701-1_all.deb
sudo apt install ./amdgpu-install_5.7.50701-1_all.deb

Once the amdgpu-install script has been extracted, it can be used to install the kernel code, libraries and developer code. The steps below will install the kernel driver code at level 5.7.1 and the libraries at level 5.7.0 and 5.7.1. For a typical HPC environment, HIP and the ROCm libraries should be sufficient:

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sudo amdgpu-install --usecase=hiplibsdk,rocm,dkms --rocmrelease=5.7.1
sudo amdgpu-install --usecase=hiplibsdk,rocm --rocmrelease=5.7.0 --no-dkms
sudo amdgpu-install --usecase=hiplibsdk,rocm --rocmrelease=5.7.1 --no-dkms

Additional libraries can be installed and the list of available use cases can be found using:

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sudo amdgpu-install --list-usecase

The ROCm code installs to /opt/rocm by default. You can verify that ROCm is installed by running

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/opt/rocm-5.7.1/bin/rocminfo

and checking that the card was detected by the software. The supported GPU card will start with “GFX9”.

Option 3

While AMD provides an installation script for specific operating system and ROCm versions, the script will ultimately install the software using the O/S standard installation software. In the case of Ubuntu, the script will use apt-get to install ROCm. The full apt-get process is shown on the How to Install page. The steps below will install ROCm 5.4 with a custom version of the apt-get commands.

Determine the location of the ROCm software to install and HIP and the related ROCm software:

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export ROCM_REPO_BASEURL="https://repo.radeon.com/rocm/apt/5.7.1/"
export ROCM_REPO_COMP="ubuntu"
export ROCM_REPO_BUILD="main"
echo "deb [arch=amd64 trusted=yes] ${ROCM_REPO_BASEURL} ${ROCM_REPO_COMP} ${ROCM_REPO_BUILD}" > /etc/apt/sources.list.d/rocm.list
sudo apt-get update
DEBIAN_FRONTEND=noninteractive apt-get install -y 
 libdrm-amdgpu* 
 initramfs-tools 
 libtinfo* 
 initramfs-tools 
 rocm-llvm 
 rocm-hip-runtime 
 rocm-hip-sdk 
 roctracer-dev

To find a list of other parts of ROCm that can also be installed using apt-get try using apt-cache:

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apt-cache search rocm

The ROCm code installs to /opt/rocm by default. You can verify that ROCm is installed by running

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/opt/rocm/bin/rocminfo

and checking that the card was detected by the software. The supported GPU card will start with “GFX9”.

Note: It is not recommended to mix the apt-get and amdgpu-install methods. Pick one or the other approach for installing ROCm.

If you have any questions or comments, please reach out to us on GitHub Discussions

David Doscher
David Doscher

David Doscher is a Senior Member of Technical Staff (SMTS) in the Software System Design Engineering group. David leads the quality assurance program for AMD’s High Performance Computing applications. David’s background includes development and quality assurance work that spans low level circuit design to IBM mainframe applications and operating systems. He has a Masters in Computer Science from Rensselaer Polytechnic Institute with a focus on parallel processing.

Rajat Arora
Rajat Arora

Rajat Arora is a Senior Member of Technical Staff (SMTS) Software System Design Engineer in the Data Center GPU Software Solutions group at AMD, where he works on porting and optimizing high-performance computing applications for AMD GPUs. He obtained his PhD in Computational Mechanics from Carnegie Mellon University. His PhD research focused at the intersection of high performance scientific computing, numerical analysis, and material science. Recently, his research interests have expanded to include development of physics-informed machine learning models and tools to accelerate scientific discovery and engineering design.

Asitav Mishra
Asitav Mishra

Asitav Mishra is a Senior Member of Technical Staff at AMD with a focus on porting and optimizing scientific applications on modern AMD GPUs that drives world's largest and exascale supercomputers. He received his Ph.D. in Aerospace Engineering from the University of Maryland. Prior to joining AMD he was a Senior Research Engineer at the National Institute of Aerospace, VA supporting NASA's projects on computational aerosciences. In addition, he has held Research Scientist and post-doctoral positions at several Universities solving complex multi-disciplinary aerospace problems. His research interests include unsteady adjoint-based methods, computational fluid dynamics (CFD), and HPC leveraging on multiple parallel models including HIP/MPI/OpenMP/OpenACC.

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