feat: refuse silent v3->v2 downgrade; monthly retrain timer with staged promote

train_all() now raises RainUnavailableError when use_rain=True and the
Open-Meteo history cannot be loaded, instead of logging a warning and
writing gauge-only (v2) bundles over the deployed v3 set -- which is what
the 2026-09-01 server retrain did unnoticed. --no-rain remains the explicit
way to get v2. CLI exits 2 with a one-line error. Three tests cover the
guard, the opt-out, and the v3 happy path.

scripts/retrain.sh trains into models/.staging, refuses to promote unless
metrics.json shows hgb-v3+ and >=14 trained stations, then renames bundles
into place (previous generation kept in models/.previous). No API restart:
predict.py reloads by mtime on the hourly precompute.

water-monitor-retrain.{service,timer}: 1st of each month 03:30, Persistent,
OMP_NUM_THREADS=4, Nice=15, same sandbox as the API unit. install.sh now
does `uv sync` into .venv (one env rule; removes a stale venv/) and enables
the timer. water-monitor.service in the repo matched neither the deployed
unit nor the uv env; it now does (run.py --web-api, .venv, EnvironmentFile).
This commit is contained in:
2026-09-11 21:37:11 +02:00
parent 0a4bf843ff
commit 764764e07e
11 changed files with 326 additions and 26 deletions
+7 -5
View File
@@ -9,17 +9,19 @@ Type=simple
User=water-monitor
Group=water-monitor
WorkingDirectory=/opt/thailand-water-monitor
ExecStart=/opt/thailand-water-monitor/venv/bin/python src/water_scraper_v3.py
# The uv-managed env (uv sync -> .venv). Same interpreter for water-monitor-retrain.service.
ExecStart=/opt/thailand-water-monitor/.venv/bin/python run.py --web-api
ExecReload=/bin/kill -HUP $MAINPID
Restart=always
RestartSec=60
TimeoutStopSec=30
# Environment variables
Environment=DB_TYPE=victoriametrics
Environment=VM_HOST=localhost
Environment=VM_PORT=8428
# DB_TYPE / POSTGRES_CONNECTION_STRING / MATRIX_* come from the .env file.
EnvironmentFile=/opt/thailand-water-monitor/.env
Environment=PYTHONPATH=/opt/thailand-water-monitor
# Serving path is latency-bound; single-threaded BLAS is 2.6x faster per call
# (docs/FLOOD_FORECASTING.md section 6). Training sets its own value.
Environment=OMP_NUM_THREADS=1
Environment=PYTHONUNBUFFERED=1
# Security settings