Customizing only the detector model path results in the warning
"Customizing more than a detector model path is unsupported." because
`{} is not {}` evaluates to True
* Revert numpy upgrade
* Upgrade arm64 onnx version to match amd64
* Increase CODEOWNERS granularity
Not sure if it has an effect since I don't have repository write access
Setting cache-to=compression=zstd causes the resulting user-pulled image
to have zstd-compressed layers, which are not compatible with docker
prior to 23.0. Ubuntu 20.04 still ships with docker 20.10, which yields
`Error processing tar file` when pulling these images.
Renaming the jetpack cache images is my way of clearing the cache of the
prior zstd layers, and it clarifies the convention I used for the other
cache images in which there is one cache per base image/job, not per
target/step. We don't need to delete the non-jetson cache images because
they haven't been rebuilt since zstd was enabled.
* fixup! Split independent builds into parallel jobs
* Combine caches within steps of same job
* Remove Maintain Cache workflow
Now that we're caching to ghcr instead of gha, we don't have to worry
about gha's cache eviction after 7 days/10 GB.
* Factor out common setup steps
* Re-order
* Split independent builds into parallel jobs
* Cache jetson builds
* Use zstd compression
* Switch from gha cache to registry cache
A CI run (four images cached with mode-max) populates the cache with 295
cache entries totalling 23.44 GB. This exceeds gha's 10GB limit, causing
trashing. Try with a registry instead.
* Enable manual CI runs
* Non-Jetson changes
Required for later commits:
- Allow base image to be overridden (and don't assume its WORKDIR)
- Ensure python3.9
- Map hwaccel decode presets as strings instead of lists
Not required:
- Fix existing documentation
- Simplify hwaccel scale logic
* Prepare for multi-arch tensorrt build
* Add tensorrt images for Jetson boards
* Add Jetson ffmpeg hwaccel
* Update docs
* Add CODEOWNERS
* CI
* Change default model from yolov7-tiny-416 to yolov7-320
In my experience the tiny models perform markedly worse without being
much faster
* fixup! Update docs
