Connecting LAVA Lab to the pipeline instance

Connecting a LAVA lab to the KernelCI pipeline

As we are moving towards the new KernelCI API and pipeline, we need to make sure all the existing LAVA labs are connected to the new pipeline instance. This document explains how to do this.

Token setup

The first step is to generate a token for the lab. This is done by the lab admin, and the token is used to submit jobs from pipeline to the lab and to authenticate LAVA lab callbacks to the pipeline.

Requirements for the token:

  • Description: a string matching the regular expression [a-zA-Z0-9\-]+, for example “kernelci-new-api-callback”
  • Value: arbitrary, kept secret

IMPORTANT! You need to have both fields, as that’s how LAVA works:

  • You submit the job with the token description in job definition
  • LAVA lab sends the result back to the pipeline with the token value (retrieved by that token-description) in the header

More details in LAVA documentation.

Pipeline configuration

Update pipeline configuration

The first step is to add the lab configuration to pipeline configuration file.

Please add a new entry to the runtimes section of the configuration file as follows:

    lab_type: lava
    url: ''
    priority_min: 10
    priority_max: 40
        token: kernelci-new-api-callback

Where lava-broonie is the name of the lab, lab_type indicates the lab is of a lava type, url is the URL of the lab, priority_min and priority_max are the priority range allowed to jobs, assigned by lab owner, and notify is the notification configuration for the lab. The callback section contains the token description that you received from the above step and the URL of the pipeline instance LAVA callback endpoint. More details on how LAVA callback and token works can be found in the LAVA documentation.

Please submit a pull request to kernelci-pipeline repository to add the lab configurations. See the pull request for reference.

KernelCI configuration (TOML) file

The next step is to add the token to the pipeline services configuration file i.e. config/kernelci.toml file. Every lab/runtime should have a section runtime.<lab-name> in the TOML file. The lab token should be stored in a key named runtime_token inside the section. For example,


Section name lab-name should be replaced with the actual lab name, matching the name of the lab in the pipeline configuration i.e. config/pipeline.yaml. lab-token-value should be replaced with the actual token value that you received in the Token setup step. Usually, it is a long string of random characters. For example, in our documentation we used lava-broonie as the lab name, so the section will look like this:


docker-compose file

We are running all the pipeline services as docker containers. You need to provide lab name to --runtimes argument to the scheduler-lava service in the docker-compose.yml file to enable the lab. For example, the following configuration adds the lava-broonie lab along with other labs:

    <<: *scheduler
    container_name: 'kernelci-pipeline-scheduler-lava'
      - './pipeline/'
      - '--settings=${KCI_SETTINGS:-/home/kernelci/config/kernelci.toml}'
      - 'loop'
      - '--runtimes'
      - 'lava-collabora'
      - 'lava-collabora-staging'
      - 'lava-broonie'

Jobs and devices specific to the lab

The last step is to add some jobs that you want KernelCI to submit to the lab. You also need to add platforms that the job will run on in the lab. For example, the following adds a job and a device type for the lava-broonie lab:

    template: baseline.jinja2
    kind: test

    <<: *arm64-device
    mach: allwinner
    dtb: dtbs/allwinner/sun50i-h5-libretech-all-h3-cc.dtb

  - job: baseline-arm64-broonie
      channel: node
      name: kbuild-gcc-10-arm64
      result: pass
      type: lava
      name: lava-broonie
      - sun50i-h5-libretech-all-h3-cc

Jobs usually define tasks to be run such as kernel build or a test suite running on a particular device (platform). The device is defined in the platforms section, and the job is defined in the jobs section. Conditions for the job to be run are defined in the scheduler section. More details about pipeline configuration can be found in the pipeline configuration documentation (TBD).

Note We have lava-callback service that will receive job results from the lab and send them to the API.

And, here you go! You have successfully connected your lab with KernelCI.

Last modified May 29, 2024