<metapackage xmlns:os="http://opensuse.org/Standards/One_Click_Install" xmlns="http://opensuse.org/Standards/One_Click_Install">
  <group distversion="openSUSE Tumbleweed">
    <repositories>
      <repository recommended="true">
        <name>science:machinelearning</name>
        <summary>Machine Learning Software Packages</summary>
        <description>This project provides packages related to Machine Learning.</description>
        <url>https://download.opensuse.org/repositories/science:/machinelearning/openSUSE_Factory/</url>
      </repository>
      <repository recommended="false">
        <name>openSUSE:Factory</name>
        <summary>The next openSUSE distribution</summary>
        <description>openSUSE Tumbleweed: The Bleeding Edge, Perfected.
Tumbleweed is the ultimate rolling release distribution, providing the latest software as it’s released, built upon a foundation of world-class stability and testing.

* Always Current: Get the newest kernel, IDEs, desktops, and applications automatically.

* Powerfully Stable: Experience the velocity of a rolling release without sacrificing the reliability you depend on.

* Engineered for Professionals: The top choice for Developers, Power Users, and openSUSE Contributors who need the best tools for the job.

If you demand the latest stable software, your choice is Tumbleweed.

Staging dashboard is located at: https://build.opensuse.org/staging_workflows/openSUSE:Factory 

List of known devel projects: https://build.opensuse.org/package/view_file/openSUSE:Factory:Staging/dashboard/devel_projects

Have a look at http://en.opensuse.org/Portal:Factory for more details.</description>
        <url>https://download.opensuse.org/tumbleweed/repo/oss/</url>
      </repository>
    </repositories>
    <software>
      <item>
        <name>armnn</name>
        <summary>Arm NN SDK enables machine learning workloads on power-efficient devices</summary>
        <description>Arm NN is an inference engine for CPUs, GPUs and NPUs.
It bridges the gap between existing NN frameworks and the underlying IP.
It enables efficient translation of existing neural network frameworks,
such as TensorFlow Lite, allowing them to run efficiently – without
modification – across Arm Cortex CPUs and Arm Mali GPUs.</description>
      </item>
    </software>
  </group>
</metapackage>
