# MAGATAMA Lane-Specific RunPod Adoption + Versioning Date: 2026-05-07 ## Scope Harden MAGATAMA training automation for: - `magatamallm` - `fo_blogllm` - `tip_llm` Goal: - lane-specific training pools remain isolated - RunPod `COMPLETED` counts only when model return/adoption is real - active lane model gets a new release/version marker after successful adoption - dashboard status and errors remain truthful ## Problem The data/build side of training already worked: - lane-specific RunPod datasets were built - RunPod jobs were submitted - registry often showed `IN_PROGRESS` / `COMPLETED` But the end of the chain remained weak: 1. adoption/version truth still depended on one shared: - `~/magatama-llm/fine-tuning/last_run.json` 2. multiple lanes could therefore overwrite the same success marker 3. the modal could degrade late-stream adoption failures into a generic `network error` 4. the user requirement was stricter: - training pool -> RunPod -> artifact -> local import -> version bump -> active alias switch - all fully automatic ## Code changes made locally ### 1. Lane-specific last-run metadata File: - `magatama/packages/fine-tuner/training_api.py` Added: - `lane_last_run_file(lane)` Resulting files: - `~/magatama-llm/fine-tuning/magatamallm-last_run.json` - `~/magatama-llm/fine-tuning/fo_blogllm-last_run.json` - `~/magatama-llm/fine-tuning/tip_llm-last_run.json` Compatibility: - `magatamallm` still mirrors to legacy: - `~/magatama-llm/fine-tuning/last_run.json` ### 2. Automatic release alias / version step File: - `magatama/packages/fine-tuner/training_api.py` Added: - `next_release_metadata(lane, active_model)` - release alias creation New adoption sequence: 1. RunPod artifact imported to candidate model 2. candidate smoke tests pass 3. release alias is created: - example shape: `-rN` 4. stable active alias is repointed to that release alias This means the lane now receives a concrete new release/version marker after successful adoption. ### 3. Dashboard lane status truth File: - `magatama/packages/dashboard/src/server.ts` Changed: - `/api/llm/status` now reads lane-specific last-run metadata first - `release_alias` is preferred as visible model version - this prevents one lane from falsely inheriting another lane's last successful run marker ### 4. Truthful RunPod terminal failure messaging Files: - `magatama/packages/dashboard/src/server.ts` - `magatama/packages/dashboard/public/index-v2.html` Changed: - if RunPod says `COMPLETED` but: - no model artifact exists - no HF repo appears - adoption fails the UI now reports that exact reason instead of collapsing into a vague generic failure Frontend hardening: - avoid showing a misleading late `network error` after the server already emitted a terminal training event - if the stream dies without a terminal event, the modal says so explicitly ### 5. Local training metrics future-proofed File: - `magatama/packages/fine-tuner/train.py` Changed: - metrics now also respect lane-specific last-run files via `TRAINING_LANE` ## Local verification Passed: - `python3 -m py_compile .../training_api.py .../train.py` - `pnpm -C .../packages/dashboard build` ## Live deployment state Not yet completed in this step. Reason: - direct Erik access failed during this block: - `ssh: connect to host 82.165.222.127 port 22: Connection refused` - later also `Operation timed out` Therefore: - the automation fix is locally ready - but not yet verified live against the currently running: - `tip_llm` - `fo_blogllm` ## Operational next step Once Erik SSH is reachable again: 1. deploy updated: - `training_api.py` - `train.py` - dashboard build / server bundle 2. restart: - `magatama-dashboard` - Mac-side training API if used 3. verify lane-specific status: - `tip_llm` - `fo_blogllm` - `magatamallm` 4. verify that a successful RunPod training now results in: - artifact found - adoption report present - lane-specific `*-last_run.json` - release alias incremented - stable alias repointed