Deep learning frameworks for ROCm#
2025-08-22
3 min read time
Deep learning frameworks provide environments for machine learning, training, fine-tuning, inference, and performance optimization.
ROCm offers a complete ecosystem for developing and running deep learning applications efficiently. It also provides ROCm-compatible versions of popular frameworks and libraries, such as PyTorch, TensorFlow, JAX, and others.
The AMD ROCm organization actively contributes to open-source development and collaborates closely with framework organizations. This collaboration ensures that framework-specific optimizations effectively leverage AMD GPUs and accelerators.
The table below summarizes information about ROCm-enabled deep learning frameworks. It includes details on ROCm compatibility and third-party tool support, installation steps and options, and links to GitHub resources. For a complete list of supported framework versions on ROCm, see the Compatibility matrix topic.
Learn how to use your ROCm deep learning environment for training, fine-tuning, inference, and performance optimization through the following guides.