.. meta:: :description: How to install deep learning frameworks for ROCm :keywords: deep learning, frameworks, ROCm, install, PyTorch, TensorFlow, JAX, MAGMA, DeepSpeed, ML, AI ******************************************** Installing deep learning frameworks for ROCm ******************************************** ROCm provides a comprehensive ecosystem for deep learning development, including :ref:`libraries ` for optimized deep learning operations and ROCm-aware versions of popular deep learning frameworks and libraries such as PyTorch, TensorFlow, and JAX. ROCm works closely with these frameworks to ensure that framework-specific optimizations take advantage of AMD accelerator and GPU architectures. The following guides provide information on compatibility and supported features for these ROCm-enabled deep learning frameworks. * :doc:`PyTorch compatibility <../compatibility/ml-compatibility/pytorch-compatibility>` * :doc:`TensorFlow compatibility <../compatibility/ml-compatibility/tensorflow-compatibility>` * :doc:`JAX compatibility <../compatibility/ml-compatibility/jax-compatibility>` This chart steps through typical installation workflows for installing deep learning frameworks for ROCm. .. image:: ../data/how-to/framework_install_2024_07_04.png :alt: Flowchart for installing ROCm-aware machine learning frameworks :align: center See the installation instructions to get started. * :doc:`PyTorch for ROCm ` * :doc:`TensorFlow for ROCm ` * :doc:`JAX for ROCm ` .. note:: For guidance on installing ROCm itself, refer to :doc:`ROCm installation for Linux `. Learn how to use your ROCm deep learning environment for training, fine-tuning, inference, and performance optimization through the following guides. * :doc:`rocm-for-ai/index` * :doc:`Training ` * :doc:`Fine-tuning LLMs ` * :doc:`Inference ` * :doc:`Inference optimization `