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feat(marketplace): GPU (CUDA) plugin bundle variants + bundle-based demo image #638
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f6984f8
feat(marketplace): GPU (CUDA) plugin bundle variants + bundle-based d…
streamkit-devin a4a9f73
ci(marketplace): don't block registry publish on skipped CUDA build
streamkit-devin 97963e6
ci(marketplace): pass signing key to CUDA registry build
streamkit-devin ac6f9de
fix(marketplace): harden GPU variant pipeline and detect accelerator …
streamkit-devin 6b4b1e8
fix(marketplace): carry plugin asset types into bundles + harden CUDA…
streamkit-devin 63f95f5
fix(marketplace): re-key bundle installs by accelerator and address r…
streamkit-devin 3e712cb
chore(ci): retrigger checks after apt mirror flake in servo lint
streamkit-devin 026a083
fix(marketplace): apply whisper portability flags to CUDA build and g…
streamkit-devin 9684702
test(server): exercise accelerator-keyed bundle removal on uninstall
streamkit-devin e0bba4f
feat(ui): surface accelerator variants in marketplace and installed p…
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🚩 Release workflow always invokes the CUDA job, which requires the self-hosted GPU runner
build_cudadefaults totrue(.github/workflows/marketplace-build.yml:14-18) andmarketplace-release.yml:18calls the reusable workflow without passingbuild_cuda, sobuild-marketplace-cuda(which targetsruns-on: [self-hosted, linux, x64, gpu]) always runs on release.continue-on-error: truemakes the job's conclusion success even if its steps fail, andpublish-registry/create-releasesusealways()/job-success semantics, so a failing GPU build degrades gracefully. However, if no runner with those labels is registered, the job stays queued indefinitely rather than failing — and downstream jobs (needs: [..., build-marketplace-cuda]) wait for it to complete, stalling the entire release. This is acceptable given the workflow is manual (workflow_dispatch) and the only caller, but it does hard-couple releases to GPU runner availability.Was this helpful? React with 👍 or 👎 to provide feedback.
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Agreed, and by design for now: the workflow is
workflow_dispatch-only with a single caller, so coupling releases to GPU-runner availability is acceptable. If no runner with the[self-hosted, linux, x64, gpu]labels is registered the job stays queued rather than failing, which would stall downstream jobs — the mitigation is operational (ensure the runner is registered, or passbuild_cuda=falseto skip it). If we later want releases to be fully decoupled from GPU availability we can add a timeout or split the CUDA build into its own non-blocking workflow; tracking that as a follow-up rather than expanding this PR.