Instructions for DEVELOPERs of ODK

This is intended for developers only, see README for the main docs.

Installation

For running locally without Docker you will need

• robot
• owltools
• python3.6 or higher

See Dockerfile for details on how to obtain these

How it works

Previously ODK used a perl script to create a new repo. This iterated the template/ directory and used special magic for expanding into a target folder. This has been replaced by python code odk/odk.py with makes used of Jinja2 templates.

For example, the file template/src/ontology/Makefile.jinja2 will compile to a file src/ontology/Makefile in the target/output directory.

Jinja2 templates should be fairly easy to grok for anyone familiar with templating systems. The syntax is very similar to Liquid templates, which are used extensively on the OBO site. We feed the template engine with a project object that is passed in by the user (more on that later).

Logic in the templates should be non-existent.

Dynamic File Names

Sometimes the odk needs to create a file whose name is based on an input setting or configuration; sometimes lists of such files need to be created.

For example, if the user specifies 3 external ontology dependencies, then we want to see the repo with 3 files imports/{{ont.id}}_import.owl

Rather than embed this logic in code, we include all dynamic files in a single "tar-esque" formated file: template/dynamicfiles.jinja2

This file is actually a specification for multiple files, each target file specified with ^^^. Because the parent file is interpreted using templates, we can have dynamic file names, and entire files created via looping constructs.

The Project object

Currently the datamodel is specified as python dataclasses, for now the best way to see the complete spec is to look at the classes annotated with @dataclass in the code.

There is a schema folder but this is incomplete as the dataclasses-scheme module doesn't appear to work (TODO)...

There are also example project.yaml files in the examples folder, and these also serve as rudimentary unit tests.

See for example examples/go-mini/project.yaml

The basic data model is:

• An OntologyProject consists of various configuration settings, plus ProductGroups
• These are:
  • An ImportProduct group which specifies how import files are generated
  • A SubsetProduct group which specifies how subset/slim files are generated
  • Other product groups for reports and templates

Many ontology projects need only specify a very minimal configuration: id of ontology, github/gitlab location, and list of ontology ids for imports. However, for projects that need to customize there are multiple options. E.g. for an import product you can optionally specific a particular URL that overrides the default PURL.

Note that for backwards compatibility, a project.yaml file is not required. A user can specify an entire repo by running seed with options such as -d for dependencies.

Note that in all cases a project.yaml file is generated.

ODK commands

``` $ ./odk/odk.py --help Usage: odk.py [OPTIONS] COMMAND [ARGS]...

Options: --help Show this message and exit.

Commands: create-dynfile For testing purposes create-makefile For testing purposes dump-schema Dumps the python schema as json schema. export-project For testing purposes seed Seeds an ontology project ```

The most common command is seed.

Updating a Makefile and/or repo

Previously with odk there was no path to either upgrading an existing project with new settings (i.e. adding an import) OR to take advantage of changes to the odk (e.g changes in the core Makefile).

This should now be easier with the new odk, although the implementation emphasis has been on the seed command. Some things that will make this easier:

• Convention of using a second loaded Makefile for custom changes
• Maintaining a project.yaml in root folder will allow easy regeneration

TODO: add a refresh command. This could run odk in place, but preserving protected files. TBD how to determine protected files. Obviously the edit file should not be touched. Could use git log to determine if any modifications have been made?

General SOP for ODK release and publication

• Put the master branch in the state we want for release (i.e. merge any approved PR that we want included in that release, etc.).
• Update the constraits.txt file
• Do any amount of testing as needed to be confident we are ready for release (at the very least, do a local build with make build and run the test suite with make tests; possibly run some mock releases on known ontologies such as FBbt, etc.).
• Tag the release and push the tag to GitHub and create a formal release from the newly pushed tag.
• Run docker login to ensure you are logged in. You must have access rights to obolibrary organisation to run the following.
• Run docker buildx create --name multiarch --driver docker-container --use if you have not done so in the past. This command needs to be run only once, see below.
• Run make publish-multiarch to publish the ODK in the obolibrary dockerhub organisation.

If you want publish the multi-arch images under the obotools/ organisation, you need to run locally:

sh $ docker buildx create --name multiarch --driver docker-container --use $ make publish-multiarch IM=obotools/odkfull IMLITE=obotools/odklite DEV=obotools/odkdev

Same as before, the first command (docker buildx create..) only being needed when you attempt a multi-arch build for the first time. Its effects are persistent, so it will never be needed again for any subsequent release — unless you completely reset your Docker installation in the meantime.

More details below.

Docker

Note that with v1.2 the main odkfull Dockerfile is at the root level. We now use a base alpine image for compactness, and selectively add in unix tools like make and rsync.

Note also that we include odk.py and the template folders in the image. This means that odk seed can now be run from anywhere!

To build the Docker image from the top level:

make build

Note that this means local invocations to use obolibrary/odkfull will use the version you built.

To test:

make tests

To publish on Dockerhub:

make publish

Multi-arch images

To build multi-arch images that will work seemleassly on several platforms, you need to have buildx enabled on your Docker installation. On MacOS with Docker Desktop, buildx should already be enabled. For other systems, refer to Docker's documentation.

Create a builder instance for multi-arch builds (this only needs to be done once):

docker buildx create --name multiarch --driver docker-container --use

You can then build and push multi-arch images by running:

make publish-multiarch

Use the variable PLATFORMS to specify the architectures for which an image should be built. The default is linux/amd64,linux/arm64, for images that work on both x86_64 and arm64 machines.

Some notes on templating and logic

There is a potential for some confusion as to responsibility for logic. On the one hand we have dependency logic in the Makefile. But we also have minimal logic in deciding what to put in the Makefile.

For example, we could move some logic from the Makefile by using for/endfor Jinja constructs and unfolding every product in a group and have an explicit non-pattern target in the Makefile. Or we can continue to write targets with patterns. Or we can do a mixture of both.

Additionally there is some minimal logic in the python odk code, but this is kept to an absolute minimum; the role of the python code is to run template expansions.

In general the decision is to keep the templating as simple as possible, which leads to a slight mixed two level system.

One gotcha is the two levels of comments. The {# .. #} comments are template comments for the eyes of developers only. These are ignored when compiling down to the target file. Then we also have Makefile comments # which remain in the target file, and are intended for advanced ontology maintainers who need to debug their workflows. These are intermingled in Makefile.jinja2

Unit Tests

To run:

make test

These will seed a few example repos in the target/ folder, some from command line opts, others from a project.yaml

These are pseudo-tests as the output is not examined, however they do serve to guard against multiple kinds of errors as the seed script will often fail if things are not set up correctly.

The examples folder serves for both unit test and documentation purposes.

Migration System

TODO

Adding new programs or Python modules to the ODK

How and where to add a component to the ODK depends on the nature of the component and whether it is to be added to odkfull or odklite.

As a general rule, new components should probably be added to odkfull, as odklite is intended to be kept small. Components should only be added to odklite if they are required in rules from the ODK-generated standard Makefile. Note that any component added to odklite will automatically be part of odkfull.

Is the component available as a standard Ubuntu package? Then add it to the list of packages in the apt-get install invocation in the main Dockerfile (for inclusion into odkfull) or in the Dockerfile for odklite.

Is the component available as a pre-built binary? Be careful that many projets only provide pre-built binaries for the x86 architecture. Using such a binary would result in the component being unusable in the arm64 version of the ODK (notably used on Apple computers equipped with M1 CPUs, aka "Apple Silicon").

Java programs available as pre-built jars can be installed by adding new RUN commands at the end of either the main Dockerfile (for odkfull) or the Dockerfile for odklite.

If the component needs to be built from source, do so in the Dockerfile for odkbuild, and install the compiled file(s) in either the /staging/full tree or the /staging/lite tree, for inclusion in odkfull or odklite respectively.

If the component is a Python package, adds it to the requirements.txt file, and also in the requirements.txt.lite file if it is to be part of odklite. Please try to avoid version constraints unless you can explain why you need one.

Python packages are "frozen" before a release by installing all the packages listed in requirements.txt into a virtual environment and running python -m pip freeze > constraints.txt from within that environment.