Writing YAML job submission files

Online YAML Parser.

The submission format schema has not been created, so the detail may change and errors in the content might not be picked up by the code, so take care when preparing new files.

Basics of the YAML format

Basic structure for job submission

The basic structure of the submission YAML is:

  1. parameters for the job
  2. list of actions.

The parameters for a job must include:

  1. a device_type
  2. a job_name

Other parameters commonly supported include:

  1. job_timeout - the default for this is not currently decided, so always specify a timeout for the job as a whole.
  2. action_timeout - the default timeout for each individual action within the job, unless an explicit timeout is set later in the YAML.
  3. priority

See also

Timeouts

device_type: kvm
job_name: kvm-pipeline
job_timeout:
  minutes: 15            # timeout for the whole job (default: ??h)
action_timeout:
  minutes: 5         # default timeout applied for each action; can be overriden in the action itself (default: ?h)
priority: medium

In YAML, a list has a name, then a colon then an indented set of items, each of which is preceded by a hyphen:

actions:
   - deploy:

Within a single action, like deploy, the parameters for that action are expressed as a hash (or dict in python terminology). In YAML, this is presented as an indented block of lines without a preceding hyphen.

actions:
   - deploy:
       timeout:
         minutes: 20
       to: tmpfs
       image: https://images.validation.linaro.org/kvm/standard/stretch-2.img.gz
       os: debian

This stanza describes a deployment strategy where the timeout for the entire deployment action is 20 minutes, the deployment happens to tmpfs (it is up to the python code for the strategy to work out what this means or fail the validation of the pipeline). The deployment uses an image and the deployment data to be used is that for a Debian system.

As the refactoring proceeds, other media can be supported in the to instruction and other deployment types can be supported apart from image. The final schema will need to express the available values for deployment strategies, boot strategies and test strategies. A new strategy will need support in the Basic structure for device_type configuration for each type which supports that strategy and in the python code to implement a pipeline for that strategy.

The rest of the actions are listed in the same way - the name of the top level Strategy Action class as a list item, the parameters for that action class as a dictionary.

Individual actions and parameters are described under Dispatcher Action Reference.

Sample JOB definition for a KVM

device_type: kvm

job_name: kvm-pipeline
job_timeout:
  minutes: 15            # timeout for the whole job (default: ??h)
action_timeout:
  minutes: 5         # default timeout applied for each action; can be overriden in the action itself (default: ?h)
priority: medium

actions:

   - deploy:
       timeout:
         minutes: 20
       to: tmpfs
       image: https://images.validation.linaro.org/kvm/standard/stretch-2.img.gz
       os: debian
       # if root_partition partition is not present:
       # - look for a partitions labelled "root" or "ROOT" or "Root" (i.e. case insensitive)
       # - look into device configuration
       root_partition: 1

   - boot:
       method: kvm
       media: tmpfs
       failure_retry: 2
       prompts:
         - 'linaro-test'
         - 'root@debian:~#'

   - test:
       failure_retry: 3
       name: kvm-basic-singlenode  # is not present, use "test $N"
       timeout:
         minutes: 5 # uses install:deps, so takes longer than singlenode01
       definitions:
           - repository: git://git.linaro.org/lava-team/lava-functional-tests.git
             from: git
             path: lava-test-shell/smoke-tests-basic.yaml
             name: smoke-tests
           - repository: https://git.linaro.org/lava-team/lava-functional-tests.git
             from: git
             path: lava-test-shell/single-node/singlenode03.yaml
             name: singlenode-advanced

To see an example of how the sample YAML would look as a python snippet, use the Online YAML Parser.

Basic structure for device_type configuration

To take advantage of the new dispatcher design and to make the LAVA device configuration more consistent, a new format is being created for the device_type and device configuration files, again using YAML.

The device type outlines which strategies devices of this type are able to support. The parameters and commands contained in the device_type configuration will apply to all devices of this type.

The main block is a dictionary of actions. Each item is the name of the strategy containing a list of arguments. All strategies require a method of how that strategy can be implemented. The methods supported by this device type appear as a list.

actions:
 deploy:
   # list of deployment methods which this device supports
   methods:
     - image
   # no need for root-part, the MountAction will need to sort that out.

 boot:
   prompts:
     - 'linaro-test'
     - 'root@debian:~#'
   # list of boot methods which this device supports.
   methods:
     - qemu
   # Action specific stanza
   command:
     # allows for the one type to support different binaries
     amd64:
       qemu_binary: qemu-system-x86_64
   # only overrides can be overridden in the Job
   overrides:
     - boot_cmds
     - qemu_options
   parameters:
     boot_cmds:
       - root: /dev/sda1
       - console: ttyS0,115200
     qemu_options:
       - -nographic
     machine:
        accel=kvm:tcg
     net:
       - nic,model=virtio
       - user

Basic structure for device configuration

Individual devices then populate parameters for a specified device_type. A device can only have one device_type.

device_type: kvm
root_part: 1
architecture: amd64
memory: 512

Overriding values in device type, device dictionary and the job context

Administrators have full control over which values allow overrides, in the following sequence:

  1. the device dictionary can always override variables in the device-type template by setting the variable name to a new value.
  2. the job definition can override the device dictionary if the device dictionary has no value set for that variable.
  3. job definition can be allowed to override a variable from the device dictionary only if the device type template specifically allows this by allowing a variable from the job context to override a variable from the device dictionary and only if the variable name in the job context differs from the name used in the device dictionary.
  4. Variables which should never be overridden can be included as simple text in the device type template or always defined in the device dictionary for all devices of that type. Remember to Only protect the essential components.

Where there is no sane default available for a device type template, the validation of the pipeline must invalidate a job submission which results in a missing value.

Currently, these override rules are not clearly visible from the UI, this will change as development continues.

Device type templates exist as files in /etc/lava-server/dispatcher-config/device-types and can be modified by the local administrators without losing changes when the packages are updated.

Device dictionaries exist in the database of the instance and can be modified from the command line on the server - typically this will require sudo. See Developer access to django shell.

Example One

For a device dictionary containing:

{% set console_device: '/dev/ttyO0' %}

The job is unable to set an override using the same variable name, so this will fail to set /dev/ttyAMX0:

context:
  console_device: /dev/ttyAMX0

The final device configuration for that job will use /dev/ttyO0.

Example Two

If the device dictionary contains no setting for console_device, then the job context value can override the device type template default:

context:
  console_device: /dev/ttyAMX0

The final device configuration for that job will use /dev/ttyAMX0.

Example Three

If the device type template supports a specific job context variable, the job can override the device dictionary. If the device type template contains:

{% set mac_address = tftp_mac_address | default(mac_address) %}

The device dictionary can set:

{% set mac_address: '00:01:73:69:5A:EF' %}

If the job context sets:

context:
  tftp_mac_address: 'FF:01:00:69:AA:CC'

Then the final device configuration for that job will use:

'TFTP on MAC Address: FF:01:00:69:AA:CC'

If the job context does not define tftp_mac_address, the final device configuration for that job will use:

'TFTP on MAC Address: 00:01:73:69:5A:EF'

This mechanism holds for variables set by the base template as well:

{% set base_kernel_args = extra_kernel_args | default(base_kernel_args) %}

Pipeline Device Configuration

Device configuration is a combination of the device dictionary and the device type template. A sample device dictionary (jinja2 child template syntax) for nexus 10 will look like the following:

{% extends 'nexus10.jinja2' %}
{% set adb_serial_number = 'R32D300FRYP' %}
{% set fastboot_serial_number = 'R32D300FRYP' %}
{% set fastboot_options = ['-u'] %}
{% set device_info = [{'board_id': 'R32D300FRYP'}] %}
{% set connection_command = 'adb -s R32D300FRYP shell' %}
{% set soft_reboot_command = 'adb -s R32D300FRYP reboot bootloader' %}

The corresponding device type template for nexus 10 is as follows:

{% extends 'base.jinja2' %}
{% block body %}
device_type: nexus10
adb_serial_number: {{ adb_serial_number|default('0000000000') }}
fastboot_serial_number: {{ fastboot_serial_number|default('0000000000') }}
fastboot_options: {{ fastboot_options|default([]) }}
device_info: [{'board_id': 'R32D300FRYP'}]

{% block vland %}
{# skip the parameters dict at top level #}
{% endblock %}

actions:
  deploy:
    methods:
      fastboot:
    connections:
      serial:
      adb:
  boot:
    connections:
      adb:
    methods:
      fastboot:

{% endblock %}

The device type template extends base.jinja2 which is the base template used by all devices and has logic to replace some of the values provided in the device dictionary. For example, the following lines within base.yaml will add connection command to the device:

{% if connection_command %}
commands:
    connect: {{ connection_command }}
{% endif %}

See /etc/lava-server/dispatcher-config/device-types/base.yaml for the complete content of `base.yaml

The above device dictionary and the device type template are combined together in order to form the device configuration which will look like the following for a nexus 10 device:

commands:
    connect: adb -s R32D300FRYP shell
    soft_reboot: adb -s R32D300FRYP reboot bootloader
device_type: nexus10
adb_serial_number: R32D300FRYP
fastboot_serial_number: R32D300FRYP
fastboot_options: ['-u']
device_info: [{'board_id': 'R32D300FRYP'}]

actions:
  deploy:
    methods:
      fastboot:
    connections:
      serial:
      adb:
  boot:
    connections:
      adb:
    methods:
      fastboot:

timeouts:
  actions:
    apply-overlay-image:
      seconds: 120
    umount-retry:
      seconds: 45
    lava-test-shell:
      seconds: 30
    power_off:
      seconds: 5
  connections:
    uboot-retry:
      seconds: 60

Use the following lava_tool command to get the device configuration in the command line:

lava-tool get-pipeline-device-config http://localhost/RPC2 qemu01

which will download the device configuration to a file called qemu01_config.yaml, alternatively the following command can be used in order to print the device configuration to stdout:

lava-tool get-pipeline-device-config http://localhost/RPC2 qemu01 --stdout

Viewing the Device Dictionary

On scheduler device detail page

The current device dictionary content is available on the scheduler device detail page, under the Configuration property as a link called Device Dictionary, e.g. for a device called qemu01, the URL to view this page would be /scheduler/device/qemu01/.

On Job Description Tab

The information from device dictionary is also available from the Job Description tab of a pipeline device. On the job details page e.g. https://staging.validation.linaro.org/scheduler/job/136847 click on Job Description tab, in which the first section gives information about the device.

Dispatcher actions

Mapping deployment actions to the python code

  1. See also Following the code flow
  2. Start at the parser. Ensure that the parser can find the top level Strategy (the name in action_data).
  3. If a specific strategy class exists and is included in the parser, the Strategy class will be initialised with the current pipeline using the select classmethod of the strategy. Only subclasses of the Strategy class will be considered in the selection. The subclasses exist in the actions/ directory in a sub-directory named after the strategy and a python file named after the particular method.
  4. The accepts classmethod of the Strategy subclass determines whether this subclass will be used for this job. Subclasses need to be imported into the parser to be considered. (pylint will complain, so mark these import lines to disable unused-import.)
  5. The initialisation of the Strategy subclass instantiates the top-level Action for this Strategy.
  6. The named Action then populates an internal pipeline when the Strategy subclass adds the top-level Action to the job pipeline.
  7. Actions cascade, adding more internal pipelines and more Actions until the Strategy is complete. The Action instantiating the internal pipeline should generally be constrained to just that task as this makes it easier to implement RetryActions and other logical classes.
  8. The parser moves on to the next Strategy.
  9. If the parser has no explicit Strategy support, it will attempt to find an Action subclass which matches the requested strategy. This support may be removed once more strategies and Action sub-classes are defined.

Deployment actions

Supported methods

  1. image

    An image deployment involves downloading the image and applying a LAVA overlay to the image using loopback mounts. The LAVA overlay includes scripts to automate the tests and the test definitions supplied to the test strategy.

    Example code block:

    - deploy:
        timeout:
          minutes: 20
        to: tmpfs
        image: https://images.validation.linaro.org/kvm/standard/stretch-2.img.gz
        os: debian
        # if root_partition partition is not present:
        # - look for a partitions labelled "root" or "ROOT" or "Root" (i.e. case insensitive)
        # - look into device configuration
        root_partition: 1
    

Boot actions

Supported methods

  1. kvm

    The KVM method uses QEMU to boot an image which has been downloaded and had a LAVA overlay applied using an Image deployment.

    Example code block:

    - boot:
     method: kvm
     media: tmpfs
     failure_retry: 2
     prompts:
       - 'linaro-test'
       - 'root@debian:~#'
    

Test actions

Example code block:

- test:
    failure_retry: 3
    name: kvm-basic-singlenode  # is not present, use "test $N"
    # only s, m & h are supported.
    timeout:
      minutes: 5 # uses install:deps, so takes longer than singlenode01
    definitions:
        - repository: git://git.linaro.org/lava-team/lava-functional-tests.git
          from: git
          path: lava-test-shell/smoke-tests-basic.yaml
          name: smoke-tests
        - repository: https://git.linaro.org/lava-team/lava-functional-tests.git
          from: git
          path: lava-test-shell/single-node/singlenode03.yaml
          name: singlenode-advanced

Metadata

This is an optional parameter that can be added to any YAML job definition. It takes a list of key: value arguments which can be used later to query the test results and find similar jobs (incoming features).

Example:

metadata:
    foo: bar
    bar: foo

Submit actions

There is no submit action in the pipeline. Results are transmitted live from any class in the pipeline with support for declaring a result.

There is no meta-format for the results, results are based on the test job and do not exist without reference to the test job.