MONAI on ROCm documentation#
2026-09-28
1 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 1.6.0 on ROCm is a HIP port of MONAI upstream version 1.6.0. It is API-compatible with upstream MONAI without requiring any code changes.
MONAI on ROCm, a ROCm-enabled version of MONAI, 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 AMD-Ecosystem/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.