grabowski
764764e07e
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).
18 lines
408 B
Python
18 lines
408 B
Python
#!/usr/bin/env python3
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"""CLI entry point for training the Ping River flood forecast models.
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Usage:
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python scripts/train_flood_model.py --stations all
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python scripts/train_flood_model.py --stations P.1,P.103 --skip-eval
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"""
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import os
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import sys
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sys.path.insert(0, os.path.join(os.path.dirname(__file__), ".."))
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from src.ml.train import cli
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if __name__ == "__main__":
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raise SystemExit(cli())
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