Installing ROCm and machine learning frameworks#
2025-09-11
3 min read time
Before getting started, install ROCm and supported machine learning frameworks.
Each release of ROCm supports specific hardware and software configurations. Before installing, consult the System requirements and Installation prerequisites guides.
If you’re new to ROCm, refer to the ROCm quick start install guide for Linux.
If you’re using a Radeon GPU for graphics-accelerated applications, refer to the Radeon installation instructions.
You can install ROCm on compatible systems via your Linux distribution’s package manager. See the following documentation resources to get started:
ROCm installation overview
Using your Linux distribution’s package manager
Multi-version installation
Follow the post-installation instructions to configure your system linker, PATH, and verify the installation.
If you encounter any issues during installation, refer to the Installation troubleshooting guide.
Machine learning frameworks#
ROCm supports popular machine learning frameworks and libraries including PyTorch, TensorFlow, JAX, and DeepSpeed.
Review the framework installation documentation. For ease-of-use, it’s recommended to use official ROCm prebuilt Docker images with the framework pre-installed.
PyTorch for ROCm
TensorFlow for ROCm
JAX for ROCm
verl for ROCm
Stanford Megatron-LM for ROCm
DGL for ROCm
Next steps#
After installing ROCm and your desired ML libraries – and before running AI workloads – conduct system health benchmarks to test the optimal performance of your AMD hardware. See System health benchmarks to get started.