v0.2.2 - spark-vllm-docker update checks + Apply Update
Backend:
- updates.py: get_update_status() runs git fetch + git rev-list --left-right --count HEAD...origin/main to learn ahead/behind/dirty, plus git log for pending commits
- UpdateManager class with asyncio.Lock; one update at a time
- POST /api/updates/apply triggers "git pull --ff-only && ./build-and-copy.sh -c" over SSH with streamed log + phase detection (Pulling / Building the vLLM container / Copying to peer Sparks)
- GET /api/updates returns {ok, behind, ahead, dirty, current, log[], branch}
Frontend:
- Persistent banner near footer: hidden when up-to-date, blue when N commits behind, warn (orange) when local dirty changes block update
- 'Show details' expands a list of pending commits
- 'Apply update' triggers the long-running build with phase + elapsed timer + collapsible logs
- Confirmation dialog explains the 5–40 min duration
Package: bump 0.2.2:0
This commit is contained in:
@@ -1,10 +1,10 @@
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import { VersionInfo, IMPOSSIBLE } from '@start9labs/start-sdk'
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export const v0_1_0 = VersionInfo.of({
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version: '0.2.1:0',
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version: '0.2.2:0',
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releaseNotes: {
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en_US:
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'Model download from the dashboard. Click "+ Download a new model", paste a Hugging Face repo (e.g. RedHatAI/...), choose solo or cluster, and watch %% progress with bytes, rate, and ETA. Drives ./hf-download.sh on Spark 1 over SSH and parses the tqdm output.',
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'Update checking for spark-vllm-docker. Dashboard footer shows "N commits behind upstream" when applicable; click for the commit log, then "Apply update" runs git pull + ./build-and-copy.sh -c on Spark 1 with a streamed log and elapsed timer. No auto-apply — you confirm each update.',
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},
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migrations: {
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up: async ({ effects }) => {},
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