dde-cargo

Stimela cabs for DDFacet/Killms on both arm64 and x86-64 systems.

The cab YAMLs in dde_cargo/ are verbatim copies of meerkat_imaging/cabs/ (ddfacet.yml, its two _included parameter schemas, and the two memory-plan cabs). A change to a cab starts in that repository; copy it here, bump the version, and tag.

Cabs and images

All images are published for amd64 and arm64.

Install

pip install dde-cargo

Requires Python >= 3.10,< 3.14 (the same window as skarabina-cargo) and installs stimela>=2.1.4 — every cab runs in its container, so the host needs nothing else.

Usage

In a stimela 2 recipe, load the cabs from the installed package with configuratt's package-include syntax. The cultcargo include must come first: ddfacet.yml carries an image-only override for cult-cargo's bdsf.catalog cab (the upstream image is amd64-only; these cabs redirect it to the multi-arch ddfacet image), and stimela builds every cab at load time, so bdsf.yml must already be loaded when ddfacet.yml merges in:

_include:
  - (cultcargo):
      - bdsf.yml
  - (dde_cargo):
      - ddfacet.yml
      - ddfacet-memory-plan.yml
      - killms-memory-plan.yml

then reference them from steps:

steps:
  ddfacet-target:
      cab: ddfacet
      params:
          Data.MS: mydata.ms
          Output.Name: target0
  killms-target:
      cab: killms
      params:
          MSName: mydata.ms
          InCol: CORRECTED_DATA

Only ddfacet.yml is essential; the memory-plan files are optional extras (wire them ahead of a step as shown in their header comments — e.g. ddfacet-memory-plan suggests Parallel.NCPU, Data.ChunkHours and Selection.ChanStep for the imaging step that follows).

Notes carried over from the upstream cab comments:

• The autofacet cab imports auto_facet_tool/auto_facet.py from the working directory, so it needs a checkout of meerkat_imaging beside the recipe (stimela binds the working directory into the container).
• The memory-plan cabs pin their image "to whatever the recipe uses": a recipe with its own image var should override image: on the cab instead of editing the package.
• The DDFacet cab wraps every parset namespace (Data, Image, Facets, Deconv, ...) as dashed CLI options (--Data-MS, --Image-NPix, ...); the killMS cab passes its flat parameter set through kMS.py.

Publishing

Trusted publishing (OIDC) — there is no API token. The GitHub Actions workflow .github/workflows/publish_pypi.yaml fires on a vX.Y.Z tag, builds with uv, and publishes from the pypi environment.

1. Register the pending publisher on PyPI (once): pypi.org/manage/account/publishing with owner tmolteno, repository dde-cargo, workflow publish_pypi.yaml, environment pypi. The workflow filename must match exactly.
2. Bump version in pyproject.toml.
3. git commit -m "..." && git tag v0.1.0 && git push origin main --follow-tags

The workflow run appears under the repo's Actions with the pypi environment; the publish is live at pypi.org/project/dde-cargo.

Development

uv sync --all-groups
uv run pytest          # schema contracts for the packaged cabs
uv build               # wheel + sdist into dist/

An end-to-end smoke check (loads the cabs through stimela's real spec loader, as a recipe would):

uv run stimela run /tmp/dde-cargo-smoke.yml dde-cargo-smoke -d

with /tmp/dde-cargo-smoke.yml any recipe that _includes the package as shown above and runs one or two of the cabs.