Prerequisites to use ROCm on Radeon desktop GPUs for machine learning development

Prerequisites to use ROCm on Radeon desktop GPUs for machine learning development#

Before starting with the installation, ensure that your system meets the necessary requirements such as supported hardware, a compatible operating system, and the recommended system configuration to ensure optimal performance and functionality.

See Compatibility matrices for more information.

Supported hardware#

Supported graphics processing units#

To successfully install ROCm™ for machine learning development, ensure that your system is operating on a Radeon™ Desktop GPU listed in the Compatibility matrices section.

Recommended memory#

The recommended memory to use ROCm on Radeon. These specifications are required for complex AI/ML workloads (for example, large language models):

64GB Main Memory
24GB GPU Video Memory

Note
AMD recommends having the same amount of system memory as video memory, as a minimum.

Minimum recommendations#

Minimum memory requirements to use ROCm on Radeon. Note that low system memory may cause issues running inference models.

16GB Main Memory
8GB GPU Video Memory

Important!
These are guidelines only. Note that minimum memory required will vary depending on workload.

Supported operating systems#

Ensure that your operating system is up-to-date to successfully install ROCm for machine learning development.

Refer to Compatibility matrices for up-to-date operating system compatibility.

Update Ubuntu® operating system#

Use the following commands to bring your OS up-to-date:

sudo apt-get update
sudo apt-get dist-upgrade

Recommended system configuration#

This section guides users on how to optimize system configuration for ROCm™ usage, ensuring smooth and performant ROCm operation.

PCIe atomics for PyTorch#

ROCm is an extension of HSA platform architecture, and shares queuing model, memory model, signaling and synchronization protocols.

Platform atomics are integral to perform queuing and signaling memory operations, where there may be multiple-writers across CPU and GPU agents.

For more details, see How ROCm uses PCIe atomics.

Disable iGPU#

The iGPU is non-essential for AI and ML workloads and not officially supported. Disable iGPU in SBIOS before proceeding to avoid unknown issues.

Alternatively, use environment variables to select the target GPU.

Here are examples to disable iGPU on some AMD motherboards:

Gigabyte™ X670 AORUS ELITE AX#

Enter BIOS

Path: Advanced → AMD CBS → NBIO Common Options → GFX Configuration → iGPU Configuration
Set iGPU to Disabled

ASUS Prime X670-P WIFI#

Enter BIOS

Path: Advanced → NB Configuration → Integrated Graphics
Set to Disabled

NOTE: This step only applies to AMD motherboards, no action is required for non-AMD motherboards.

There are no minimum motherboard hardware requirements.

Alternative option: Use environment variables to select target GPU#

An alternative option to disabling the iGPU is to use environment variable to select the GPU.

See GPU Isolation Techniques to specify the device indices you would like to expose to your application.

Additional package repositories#

For some distributions, the ROCm installation packages depend on packages that aren’t included in the default package repositories. These external repositories need to be sourced before installation. Use the following instructions specific to your distribution to add the necessary repositories.

Red Hat Enterprise Linux#

Add the EPEL repository for RHEL 9.

wget https://dl.fedoraproject.org/pub/epel/epel-release-latest-9.noarch.rpm
sudo rpm -ivh epel-release-latest-9.noarch.rpm

Enable the CodeReady Linux Builder (CRB) repository.

In order to enable CRB, you may need to install dnf-plugin-config-manager first.
```
sudo dnf install dnf-plugin-config-manager
sudo crb enable
```
Note

For more information, see Additional package repositories for Instinct GPUs.