fix(worker): pin the runtime versions and refuse to boot when install_runtime swaps an imported package (SIENTIAPDE-2214)

Replaces the re-exec. requirements.txt now pins the union of the basic and xgboost
runtime libraries, so install_runtime has nothing to change in the packages the worker
already imported. If a runtime pin changes and requirements.txt is not updated, the
worker logs which packages changed and exits before starting the workers.

test_main_entrypoint assigned worker.main = AsyncMock() without restoring it, so every
later test of main() ran the mock; it now uses patch.object.

CLAUDE.md

@@ -45,7 +45,7 @@ Coverage gate in CI is 80% minimum; this repo targets 100% in practice — don't
 ## Architecture
 
 ### Temporal layering
-- **Worker** (`model_manager/worker/worker.py`): entrypoint. Builds config from env, connects Postgres/MLFlow/MinIO/Plugin Store, installs the `RUNTIME` model runtime via `PluginStore.install_runtime` and then re-executes itself once (`os.execv`, flagged by the internal `SIENTIA_RUNTIME_READY=1`) so every import loads the runtime's package versions (SIENTIAPDE-2214), starts two Temporal workers (one per task queue) plus Prometheus metrics server and the cleanup schedule. All heavy imports are wrapped in `workflow.unsafe.imports_passed_through()` — required because Temporal workflow sandboxing forbids non-deterministic imports at module scope outside that block.
+- **Worker** (`model_manager/worker/worker.py`): entrypoint. Builds config from env, connects Postgres/MLFlow/MinIO/Plugin Store, installs the `RUNTIME` model runtime via `PluginStore.install_runtime`, starts two Temporal workers (one per task queue) plus Prometheus metrics server and the cleanup schedule. All heavy imports are wrapped in `workflow.unsafe.imports_passed_through()` — required because Temporal workflow sandboxing forbids non-deterministic imports at module scope outside that block.
 - **Workflows** (`model_manager/workflows/`): pure orchestration, no I/O beyond `execute_activity_method` calls.
   - `train_model.py` (`TrainModel`, workflow name `train_model`): validate `experiment_run_id` → `load_model_metadata` → `validate_train_params` → update DB status `ORCHESTRATOR_WAITING_PROC` → `train_model` activity → update DB status `TRAINING_SUCCESS`/`TRAINING_ERROR` → `cleanup_resources` (always runs in `finally` if a `run_dir` exists, even on failure). Uses distinct `RetryPolicy`s per operation class: `network_retry_policy` (transient I/O), `no_retry_policy` (training/validation — data errors are permanent, and retrying would duplicate MLflow writes), `database_retry_policy` (Postgres). Timeouts are env-driven (`TIMEOUT_*`), extend `TIMEOUT_TRAIN_MODEL` instead of adding retries for slow training.
   - `cleanup_files.py` (`CleanupFiles`): scheduled (`cleanup_schedule.py`, default daily at midnight UTC via `CLEANUP_CRON`) deletion of stale local temp dirs matching `name_YYYYMMDD_HHMMSS_microseconds`, older than `CLEANUP_RETENTION_HOURS`.