#. **Additional reading:** The blog post `AMD Composable Kernel library: efficient fused kernels for AI apps with just a few lines of code <https://community.amd.com/t5/instinct-accelerators/amd-composable-kernel-library-efficient-fused-kernels-for-ai/ba-p/553224>`_ provides a deeper understanding of the CK library and showcases its performance capabilities.
@@ -32,7 +32,7 @@ CK library acceleration features are based on:
If you need more technical details and benchmarking results read the following
`blog post <https://community.amd.com/t5/instinct-accelerators/amd-composable-kernel-library-efficient-fused-kernels-for-ai/ba-p/553224>`_.
To download the library visit the `composable_kernel repository <https://github.com/ROCmSoftwarePlatform/composable_kernel>`_.
To download the library visit the `composable_kernel repository <https://github.com/ROCm/composable_kernel>`_.
Hardware targets
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@@ -58,7 +58,7 @@ This tutorial is based on the use of docker images as explained in :ref:`docker-
.. note::
You can also `install ROCm <https://rocm.docs.amd.com/projects/install-on-linux/en/latest/>`_ on your system, clone the `Composable Kernel repository <https://github.com/ROCmSoftwarePlatform/composable_kernel.git>`_ on GitHub, and use that to build and run the examples using the commands described below.
You can also `install ROCm <https://rocm.docs.amd.com/projects/install-on-linux/en/latest/>`_ on your system, clone the `Composable Kernel repository <https://github.com/ROCm/composable_kernel.git>`_ on GitHub, and use that to build and run the examples using the commands described below.
Both the docker container and GitHub repository include the Composable Kernel library. Navigate to the library::