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Upgrade AVD version

PyAVD (Arista's Python engine) is what renders your device configurations. When a new PyAVD release is available, you move to it and validate the result on a branch before anything reaches production. Because both Infrahub and AVD evolve, you confirm compatibility on both sides rather than upgrading in place.

The branch-first upgrade flow is a maintainer/operator task — you rebuild the custom image, so it goes beyond the service portal.

Before you start​

  • A running stack (invoke start) with at least one fabric already generated, so you have a config baseline to diff against.
  • Know your current PyAVD version — it is pinned in pyproject.toml (pyavd>=...).
  • Check the AVD documentation for breaking changes in the target version.

Steps​

  1. Create a branch for the upgrade. Create a named Infrahub branch (for example upgrade-avd) so the change is isolated and reviewable.

  2. Bump the PyAVD pin. Update the pyavd version in pyproject.toml, then re-sync and rebuild the custom image:

    uv sync --all-packages
    uv run invoke build
    uv run invoke restart

  3. Regenerate on the branch. Re-run the AVD generators so host_vars and structured configuration are rebuilt with the new PyAVD version. Regeneration is idempotent (checksum-based), so it only reprocesses what the new version changes — see Regenerate a Fabric.

  4. Review the rendered-config diff. Open a proposed change from your branch and inspect the diff of the rendered EOS configurations and structured config. This is where a PyAVD version bump shows its effect — look for unexpected changes to interfaces, BGP, or EVPN stanzas.

  5. Validate. Confirm the rendered artifacts build cleanly and the diff matches the release notes' expected changes. If ANTA catalog generation is enabled, regenerate the catalogs and review them too.

  6. Merge through the proposed change. Once the diff is understood and approved, merge the proposed change. Only then does the new PyAVD version reach production.

If something looks wrong​

  • A large or surprising diff points to a PyAVD default or schema change between versions — cross-check the release notes.
  • Roll back by discarding the branch (nothing merged, nothing deployed) and pinning the previous PyAVD version.
  • For pipeline-level failures, see Debugging the Pipeline.