MONAI on ROCm documentation#
2026-04-01
2 min read time
The Medical Open Network for AI (MONAI) is a domain-optimized, open-source framework based on PyTorch, explicitly designed for deep learning in healthcare imaging. MONAI 1.5.2 on ROCm is a HIP port of MONAI upstream version 1.5.2. It is API-compatible with upstream MONAI without requiring any code changes.
MONAI on ROCm is built on top of PyTorch for AMD ROCm, helping healthcare and life science innovators to leverage GPU acceleration with AMD Instinct™ GPUs for high-performance inference and training of medical AI applications.
MONAI on ROCm offers open, scalable, and high-performance solutions for life science and healthcare workloads.
The MONAI on ROCm key features include:
Flexible preprocessing for multidimensional medical imaging data.
Compositional and portable APIs for smooth integration into existing workflows.
Domain-specific implementations for networks, losses, evaluation metrics, and more.
Customizable design according to user expertise.
Multi-GPU multinode data parallelism support.
The code is open and hosted at ROCm-LS/monai.
The documentation is structured as follows:
To contribute to MONAI on ROCm, refer to Contributing to MONAI on ROCm.
You can find licensing information on the Licensing page.