LAVA developer guide

Pre-requisites to start with development

LAVA is written in Python, so you will need to know (or be willing to learn) the language. Likewise, the web interface is a Django application so you will need to use and debug Django if you need to modify the web interface. The pipeline model uses YAML (so you’ll need the YAML Parser) and Jinja2. All LAVA software is maintained in git, as are many of the support scripts, test definitions and job submissions. Some familiarity with Debian is going to be useful, helper scripts are available when preparing packages based on your modifications.

LAVA is complex and works to solve complex problems. This has implications for how LAVA is developed, tested, deployed and used.

Other elements involved in LAVA development

The Django backend used with LAVA is PostgreSQL and some postgres-specific support is used. The LAVA UI has some use of Javascript and CSS. LAVA also uses ZMQ and XML-RPC and the LAVA documentation is written with RST.

In addition, test jobs and device support can involve use of U-Boot, GuestFS, ADB, QEMU, Grub, SSH and a variety of other systems and tools to access devices and debug test jobs.

Developing using device-type templates

If you are an administrator, you may think the previous link sent you to the wrong section. However, administrators need to understand how device-type templates operate and how the template engine will use the template to be able to make changes.


Device type templates are more than configuration files - the templates are processed as source code at runtime. Anyone making changes to a device-type .jinja2 template file must understand the basics of how to test templates using the same tools as developers.

Device type templates as code

Jinja2 provides a powerful templating engine. Templates in LAVA use several standard programming concepts:

  • Conditional Logic
  • Inheritance
  • Default values

In addition, LAVA templates need to always render to valid YAML. It is this YAML which is sent to the worker as device.yaml. The worker does not handle the templates. All operations are done on the master.

Testing new device-type templates

The simplest check is to render the new template to YAML and check that it contains the expected commands. As with test job files, there are common YAML errors which can block the use of new templates.

lava-server manage device-dictionary --hostname <HOSTNAME> --review

A more rigorous test is to use the dedicated unit test which does not require lava-server to be installed, i.e. it does not require a database to be configured. This test can be run directly from a git checkout of lava-server with a few basic python packages installed (including python-jinja2).

$ python -m unittest -vcf lava_scheduler_app.tests.test_templates.TestTemplates.test_all_templates

Individual templates have their own unit tests to test for specific elements of the rendered device configuration.

Most changes to device-type templates take effect immediately - as soon as the file is changed in /etc/lava-server/dispatcher-config/device-types/ the next testjob for that device-type will use the output of that template. Always test your templates locally before deploying the template to the master. (Test jobs which have already started are not affected by template changes.)

Use version-control for device-type templates

This cannot be stressed enough. ALL admins need to keep device-type templates in some form of version control. The template files are code and admins will need to be able to upgrade templates when packages are upgraded and when devices need to implement new support.

Contribute device-type templates back upstream

As code, device-type templates need to develop alongside the rest of the codebase. The best way to maintain support is to Contributing Upstream so that new features can be tested against your templates and new releases can automatically include updates to your templates.

Developer workflows


LAVA is developed using Debian packaging to ensure that daemons and system-wide configuration is correctly updated with changes in the codebase. There is no support for pypi or python virtual environments or installing directly from a git directory. python-setuptools is used but only with sdist to create the tarballs to be used for the Debian packaging, not for install. Some dependencies of LAVA are not available with pypi, for example python-guestfs.

Developers can update the installed code on their own systems manually (by copying files into the system paths) and/or use symlinks where appropriate but changes need to be tested in a system which is deployed using the Developer package build before being proposed for review. All changes must also pass all the unit tests, unless those tests are already allowed to be skipped using unittest decorators.

Mixing the use of python code in /usr/local/lib and /usr/lib on a single system is known to cause spurious errors and will only waste your development time. Be very careful when copying files and when using symlinks. If in doubt, remove /usr/local/lib/python* and ~/.local/lib/python* then build a local developer package and install it.

If your change introduces a dependency on a new python module, always ensure that this module is available in Debian by searching the Debian package lists. If the module exists but is not in the current stable release of Debian, it can be backported but be aware that this will delay testing and acceptance of your change. It is expressly not acceptable to add a dependency on a python module which is only available using pypi or pip install. Introducing such a module to Debian can involve a large amount of work - talk to us before spending time on code which relies on such modules or which relies on newer versions of the modules than are currently available in Debian testing.

Naming conventions and LAVA V2 architecture

Certain terms used in LAVA V2 have specific meanings, please be consistent in the use of the following terms:

The physical hardware sitting in a rack or on a desk.
A means of communicating with a device, often using a serial port but can also be SSH or another way of obtaining a shell-type interactive interface. Connections will typically require a POSIX type shell.
An integer calculated by the master and separately by the worker to determine whether the worker is running older code than the master.

In lava-server, a device is a database object in LAVA which stores configuration, information and status relating to a single board. The device information can be represented in export formats like YAML for use when the database is not accessible.

In lava-dispatcher, the database is not accessible so the scheduler prepares a simple dictionary of values derived from the database and the template to provide the information about the device.


A database object which collates similar devices into a group for purposes of scheduling. Devices of a single type are often the same vendor model but not all boards of the same model will necessarily be of the same device-type.

See also

Device types

The dispatcher software relates to the lava-dispatcher source package in git and in Debian. The dispatcher software for LAVA V2 can be installed without the server or the scheduler and a machine configured in this way is also called a dispatcher.
dispatcher-master or simply master
A singleton process which starts and monitors test jobs running on one or more dispatchers by communicating with the slave using ZMQ.
dynamic data - the Action base class provides access to dynamic data stores
which other actions can access. This provides the way for action classes to share information like temporary paths of downloaded and / or modified files and other data which is generated or calculated during the operation of the pipeline. Use self.set_common_data to set the namespace, key and value and self.get_common_data to retrieve the value using the namespace and the key.
A static, read-only, dictionary of values and available for the job and the device. Parameters must not be modified by the codebase - use the common_data primitives of the Action base class to copy parameters and store the modified values as dynamic data.

The name for the design of LAVA V2, based on how the actions to be executed by the dispatcher are arranged in a unidirectional pipe. The contents of the pipe are validated before the job starts and the description of all elements in the pipe is retained for later reference.

An API used by the python code inside lava-dispatcher to interact with external systems and daemons when a shell like environment is not supported. Protocols need to be supported within the python codebase and currently include multinode, LXC and vland.

A singleton process which is solely responsible for assigning a device to a test job. The scheduler is common to LAVA V1 and LAVA V2 and performs checks on submission restrictions, device availability, device tags and schema compliance.

See also

device tag

The server software relates to the lava-server source package in git and in Debian. It includes components from LAVA V1 and LAVA V2 covering the UI and the scheduler daemon.
A daemon running on each dispatcher machine which communicates with the dispatcher-master using ZMQ. The slave in LAVA V2 uses whatever device configuration the dispatcher-master provides.
test job
A database object which is created for each submission and retains the logs and pipeline information generated when the test job executed on the device.

Updating online documentation

LAVA online documentation is written with RST format. You can use the command below to generate html format files for LAVA V2:

$ cd lava-server/
$ make -C doc/v2 clean
$ make -C doc/v2 html
$ firefox doc/v2/_build/html/index.html
(or whatever browser you prefer)

We welcome contributions to improve the documentation. If you are considering adding new features to LAVA or changing current behaviour, ensure that the changes include updates for the documentation.

Wherever possible, all new sections of documentation should come with worked examples.

  • Add a testjob submission YAML file to doc/v2/examples/test-jobs
  • If the change relates to or includes particular test definitions to demonstrate the new support, add a test definition YAML file to doc/v2/examples/test-definitions
  • Use the include options supported in RST to quote snippets of the test job or test definition YAML, following the examples of the existing examples.
  • Use comments liberally in the examples and link to existing terms and sections.
  • Read the comments in the doc/v2/index.rst file if you are adding new pages or altering section headings.

Code locations

The ongoing migration complicates some of the workflow when it comes to finding all of the V2 code. When the V1 code is removed, the organisation of the code will be tidied up.

  • lava-server includes the lava_scheduler_app, lava_results_app, lava_server, lava and linaro_django_xmlrpc components of LAVA V2.
  • lava-dispatcher includes the lava_dispatcher and lava_test_shell components. All LAVA V2 dispatcher code lives in lava_dispatcher/pipeline. Some lava_test_shell scripts remain in the top level lava_test_shell directory with overrides in pipeline/lava_test_shell.

There are also locations which provide device configurations to support the unit tests. Only the Jinja2 support is used by the installed packages,


The compatibility mechanism allows the dispatcher-master daemon to prevent issues that would arise if the worker is running older software. A job with a lower compatibility may fail much, much later but this allows the job to fail early. In future, support is to be added for re-queuing such jobs.

Developers need to take note that in the code, compatibility should reflect the removal of support for particular elements, similar to handling a SONAME when developing in C. When parts of the submission YAML are changed to no longer support fields previously used, then the compatibility of the associated strategy class must be raised to one more than the current highest compatibility in the lava-dispatcher codebase. Compatibility does not need to be changed when adding new classes or functionality. It remains a task for the admins to ensure that the code is updated when new functionality is to be used on a worker as this typically involves adding devices and other hardware.

Compatibility is calculated for each pipeline during parsing. Only if the pipeline uses classes with the higher compatibility will the master prevent the test job from executing. Therefore, test jobs using code which has not had a compatibility change will continue to execute even if the worker is running older software. Compatibility is not a guarantee that all workers are running latest code, it exists to let jobs fail early when those specific jobs would attempt to execute a code path which has been removed in the updated code.

Jinja2 support

The Jinja2 templates can be found in lava_scheduler_app/tests/device-types in the lava-server codebase. The reason for this is that all template changes are checked in the unit-tests. When the package is installed, the device-types directory is installed into /etc/lava-server/dispatcher-config/device-types/. The contents of lava_scheduler_app/tests/devices is ignored by the packaging, these files exist solely to support the unit tests.

See also

Unit-tests and Testing the new design for examples of how to run individual unit tests or all unit tests within a class or module.

Device dictionaries

Individual instances will each have their own locations for the device dictionaries of real devices. To allow the unit tests to run, some device dictionaries are exported into lava_scheduler_app/tests/devices but there is no guarantee that any of these would work with any real devices, even of the declared device-type.

For example, the Cambridge lab stores each device dictionary in git at and you can look at the configuration of staging as a reference:

Dispatcher device configurations

The lava-dispatcher codebase also has local device configuration files in order to support the dispatcher unit tests. These are not Jinja2 format, these are YAML - the same YAML as would be sent to the dispatcher by the relevant master after rendering the Jinja2 templates on that master. There is no guarantee that any of the device-type or device configurations in the lava-dispatcher codebase would work with any real devices, even of the declared device-type.

Contributing Upstream

The best way to protect your investment on LAVA is to contribute your changes back. This way you don’t have to maintain the changes you need by yourself, and you don’t run the risk of LAVA changed in a way that is incompatible with your changes.

Upstream uses Debian, see Developing LAVA on Debian for more information.


The LAVA software team use Jira for long term planning for new features and concepts. The JIRA instance used by LAVA is and anonymous access is available for anyone interested in LAVA to find out more about the future direction of LAVA. Not all features are available at this stage but all LAVA issues are visible individually. Not all issues will necesarily be delivered exactly as described, many descriptions are written well in advance of delivery of the feature.

Many git commit messages within the LAVA codebase contain references to JIRA issues as LAVA-123 etc. All references like this can be appended to a basic URL to find the details of that issue: e.g. the addition of this section on JIRA relates to LAVA-735 which can be viewed as

Within JIRA, there is a hierarchy of issues. EPIC is the highest level to group similar issues. Stories are each within a single EPIC and sub-tasks can exist within a single Story.

This information is made available for interest and to make our development process open to the community. If you have comments or questions about anything visible within the LAVA project, please subscribe to one of the mailing lists and ask your questions there. For bugs in the current release, please continue to file bug reports using Bugzilla.

Many stories contain comments linking directly to one or more gerrit reviews related to that story. When the review is merged, the story will be marked as resolved with a Fix Version matching the git tag of the release containing the fix from the review.

Community contributions

The LAVA software team use git review to manage contributions. Each review is automatically tested against all the unit tests. All reviews must pass all unit tests before being considered for merging into the master branch. The contributor is responsible for making the changes necessary to allow the unit tests to pass and to keep the review up to date with other changes in the master branch.

To setup git review for the first time, install the package and setup the local git configuration. (This can take a little time.):

$ apt -y install git-review
$ cd lava-server/
$ git review -s


All changes need to support both Debian unstable and Debian stable - currently Jessie. This often includes multiple versions of django and other supporting packages. Automated unit tests are run on stable (with backports).

The master branch may be significantly ahead of the latest packages available from Debian (unstable or stable backports) which are based on the release branch. Use the LAVA repositories and/or Developer build versions to ensure that your instance is up to date with master.

See also

Release process.

Patches, fixes and code

If you’d like to offer a patch (whether it is a bug fix, documentation update, new feature or even a simple typo fix) it is best to follow this simple check-list:

  1. Clone the master branch of the correct project.

  2. Create a new, clean, local branch based on master:

    $ git checkout -b fixupbranch
  3. Add your code, change any existing files as needed.

  4. Commit your changes on the local branch.

  5. Checkout the master branch and git pull

  6. Checkout your existing local branch:

    $ git checkout fixupbranch
  7. rebase your local branch against updated master:

    $ git rebase master
  8. Fix any merge conficts. #. Send the patch to the Linaro Code Review system (gerrit):

    $ git review
  9. If successful, you will get a link to a review.

  10. Login to gerrit and add the lava-team as reviewers.

  11. The unit tests will automatically start and you will be notified by email of the results and a link to the output which is useful if the tests fail.

See also

Patch Submissions and workflow for detailed information.

Contributing via your distribution

You are welcome to use the bug tracker of your chosen distribution. The maintainer for the packages in that distribution should Register with Linaro as a Community contributor with Linaro (or already be part of Linaro) to be able to forward bug reports and patches into the upstream LAVA systems.

Register with Linaro as a Community contributor

If you, or anyone on your team, would like to register with Linaro directly, this will allow you to file an upstream bug, submit code for review by the LAVA team, etc. Register at the following url:

If you are considering large changes, it is best to register and also to subscribe to the lava-devel mailing list and talk to us on IRC:

Contributing via GitHub

You can use GitHub to fork the LAVA packages and make pull requests

It is worth sending an email to the lava-devel mailing list, so that someone can migrate the pull request to a review.


You will need to respond to comments on the review and the process of updating the review is not linked to the github pull request process.