fix(worker): set app_up only after the runtime version guard (SIENTIAPDE-2214)
The probes only check app_up, which was set to 1 when the metrics server started, before install_runtime. A pod that would fail the guard could turn Ready first, so a rollout would remove the old pod. app_up now goes to 1 right after the guard. Also: copy sys.modules keys before iterating, raise the sientia_model floor to the runtime's >=0.14.0, pin the same runtime versions in requirements-local.txt, test that the snapshot is taken before the install and the real importlib.metadata path, and document the boot failure in README and CLAUDE.md.
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@@ -45,7 +45,7 @@ Coverage gate in CI is 80% minimum; this repo targets 100% in practice — don't
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## Architecture
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### Temporal layering
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- **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.
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- **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` (boot fails if that changes the version of an already-imported package, so the `requirements.txt` pins must match the factory runtimes; `app_up` goes to 1 only after this check), 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.
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- **Workflows** (`model_manager/workflows/`): pure orchestration, no I/O beyond `execute_activity_method` calls.
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- `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.
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- `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`.
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@@ -149,6 +149,7 @@ The Model Manager system uses a Temporal-based workflow architecture with clear
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- Prometheus metrics server initialization
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- Notification handler setup and configuration
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- **Runtime Installation**: Automatically installs the required model runtime from the Plugin Store
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- **Runtime version guard**: boot fails if the install changes the version of a package the worker already imported, so the `requirements.txt` pins must match `model-plugin-factory` `runtime/basic.yaml` and `runtime/xgboost.yaml`; `app_up` goes to 1 only after this check
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- **Key Features**:
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- Automatic scaling with `PollerBehaviorAutoscaling`
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- Health check endpoints for Kubernetes liveness/readiness probes
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@@ -1249,6 +1250,10 @@ sientia-dataops-model-manager/
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- Check training parameter validation errors
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- Verify input data format and required fields
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5. **Worker exits at boot with `Runtime ... changed packages already imported by the worker`**
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- A runtime pin in `model-plugin-factory` `runtime/*.yaml` no longer matches `requirements.txt`; the message lists each package as `old -> new`
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- Set the runtime version in `requirements.txt` and `requirements-local.txt` here and in `sientia-dataops-laborious`, then redeploy
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### Debug Mode
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Enable debug logging by setting the log level:
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@@ -79,7 +79,8 @@ def _get_runtime(runtime: str | None) -> str:
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def _loaded_distribution_versions() -> dict[str, str]:
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"""Version on disk of every installed distribution whose top-level module is imported."""
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loaded = {name.partition('.')[0] for name in sys.modules}
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# list() copies the keys: another thread importing while we iterate would raise RuntimeError.
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loaded = {name.partition('.')[0] for name in list(sys.modules)}
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return {
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dist: importlib.metadata.version(dist)
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for module, dists in importlib.metadata.packages_distributions().items()
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@@ -114,7 +115,8 @@ async def main():
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1. Sets up logging and metadata
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2. Starts Prometheus metrics server
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3. Initializes notification handler, then installs the runtime; exits if the install
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changed the version of a package the worker already imported
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changed the version of a package the worker already imported, and only then sets
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``app_up`` (what the probes check) to 1
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4. Creates and configures activities
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5. Starts Temporal client and workers
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6. Manages worker lifecycle and graceful shutdown
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@@ -188,6 +190,10 @@ async def main():
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metrics.APP_UP.labels(pod_id=POD_ID).set(0)
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sys.exit(1)
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# The probes only check app_up, so it goes to 1 after the guard: a pod that would fail it
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# never turns Ready, and a rollout keeps the old pod.
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metrics.APP_UP.labels(pod_id=POD_ID).set(1)
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activities = Activities(
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postgres_config=build_postgres_config(),
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mlflow_config=build_mlflow_config(),
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@@ -274,8 +280,8 @@ def start_prometheus_server(logger: SientiaLogger, metadata: dict[str, str | Non
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"""
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Starts the Prometheus metrics server for monitoring and observability.
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This function initializes the Prometheus HTTP server on the configured port
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and sets the application health metric to indicate the service is running.
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This function initializes the Prometheus HTTP server on the configured port.
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``main`` sets ``app_up`` to 1 later, once the runtime install passed the version guard.
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The server exposes metrics that can be scraped by Prometheus for monitoring
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the health and performance of the Model Manager worker.
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@@ -291,7 +297,6 @@ def start_prometheus_server(logger: SientiaLogger, metadata: dict[str, str | Non
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port = int(os.getenv('HTTP_METRICS_PORT', 9090))
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start_http_server(port)
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logger.custom_info(f'Prometheus server initialized on port {port}.', metadata)
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metrics.APP_UP.labels(pod_id=POD_ID).set(1) # Mark app as UP
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except Exception as e: # noqa: BLE001
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logger.custom_critical(f'Failed to start Prometheus server: {e}', metadata)
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os._exit(1)
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@@ -9,3 +9,14 @@ prometheus-client==0.23.1
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beautifulsoup4==4.12.3
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evidently==0.6.7
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jsonschema==4.26.0
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# Same versions as the plugin runtimes (model-plugin-factory runtime/basic.yaml and xgboost.yaml).
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# The worker imports these before install_runtime; if the runtime installed a different version,
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# the old code would stay in memory, so the worker refuses to boot (SIENTIAPDE-2214).
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# Changing a runtime pin means changing it here and in requirements.txt too.
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mlflow==3.8.1
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pathspec==1.0.4
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pandas==2.3.3
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cloudpickle==3.1.2
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scikit-learn==1.8.0
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joblib==1.5.3
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rich==14.3.2
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@@ -4,7 +4,7 @@ sqlalchemy==2.0.50
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boto3==1.42.70
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botocore==1.42.70
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sientia_do>=1.12.2
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sientia_model>=0.8.1
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sientia_model>=0.14.0
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prometheus-client==0.23.1
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beautifulsoup4==4.12.3
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evidently==0.6.7
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@@ -12,7 +12,7 @@ jsonschema==4.26.0
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# Same versions as the plugin runtimes (model-plugin-factory runtime/basic.yaml and xgboost.yaml).
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# The worker imports these before install_runtime; if the runtime installed a different version,
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# the old code would stay in memory, so the worker refuses to boot (SIENTIAPDE-2214).
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# Changing a runtime pin means changing it here too.
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# Changing a runtime pin means changing it here and in requirements-local.txt too.
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mlflow==3.8.1
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pathspec==1.0.4
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pandas==2.3.3
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@@ -132,9 +132,8 @@ def test_start_prometheus_server_success(
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# Verify HTTP server started
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mock_start_http_server.assert_called_once_with(9090)
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# Verify APP_UP metric was set to 1
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mock_metrics.APP_UP.labels.assert_called_once_with(pod_id='test-pod-123')
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mock_app_up.set.assert_called_once_with(1)
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# app_up goes to 1 in main(), after the runtime version guard.
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mock_app_up.set.assert_not_called()
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mock_logger.custom_info.assert_called_once()
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@@ -284,7 +283,8 @@ async def test_main_successful_startup(
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# Verify cleanup was performed
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mock_notification_handler.shutdown.assert_called_once()
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mock_activities.shutdown.assert_called_once()
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mock_app_up.set.assert_called_with(0)
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# Ready after the guard, down again on shutdown.
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assert [c.args[0] for c in mock_app_up.set.call_args_list] == [1, 0]
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@pytest.mark.asyncio
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@@ -941,22 +941,30 @@ async def test_main_runtime_changed_loaded_package_exits(mock_env_vars, mock_log
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plugin_store = Mock()
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plugin_store.install_runtime = AsyncMock(return_value=None)
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before = {'scikit-learn': '1.9.1'}
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def snapshot():
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# Taken after install_runtime, it would already see 1.8.0 and the guard would pass.
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assert plugin_store.install_runtime.await_count == 0
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return before
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mongo = {'connection_string': 'mongodb://test', 'database_name': 'test_db', 'uri': 'x'}
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with (
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patch.object(worker_module, 'RUNTIME', 'basic'),
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patch.object(worker_module, 'ensure_runtime_directories'),
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patch.object(worker_module, 'metrics'),
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patch.object(worker_module, 'metrics') as mock_metrics,
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patch.object(worker_module, 'start_prometheus_server'),
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patch.object(worker_module, 'get_logger', return_value=mock_logger),
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patch.object(worker_module, 'build_mongodb_config', return_value=mongo),
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patch.object(worker_module, 'NotificationHandler'),
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patch.object(worker_module, 'build_plugin_store_config', return_value=MagicMock()),
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patch.object(worker_module, 'PluginStore', return_value=plugin_store),
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patch.object(worker_module, '_loaded_distribution_versions', side_effect=snapshot),
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patch.object(
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worker_module,
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'_runtime_changed_loaded_packages',
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return_value={'scikit-learn': '1.9.1 -> 1.8.0'},
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),
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) as mock_changed,
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patch.object(worker_module, 'Activities') as mock_activities_class,
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patch.object(worker_module.sys, 'exit', side_effect=SystemExit(1)) as mock_exit,
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):
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@@ -965,5 +973,35 @@ async def test_main_runtime_changed_loaded_package_exits(mock_env_vars, mock_log
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mock_exit.assert_called_once_with(1)
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mock_activities_class.assert_not_called()
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plugin_store.install_runtime.assert_awaited_once()
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mock_changed.assert_called_once_with(before)
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mock_metrics.APP_UP.labels.return_value.set.assert_called_once_with(0)
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message = mock_logger.custom_critical.call_args.args[0]
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assert "'scikit-learn': '1.9.1 -> 1.8.0'" in message
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def test_guard_detects_version_change_on_disk(tmp_path, monkeypatch):
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"""The real importlib.metadata path: a package swapped on disk after import is reported."""
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import types
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from model_manager.worker.worker import (
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_loaded_distribution_versions,
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_runtime_changed_loaded_packages,
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)
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info = tmp_path / 'fakepkg_2214-1.0.dist-info'
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info.mkdir()
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(info / 'METADATA').write_text('Metadata-Version: 2.1\nName: fakepkg-2214\nVersion: 1.0\n')
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(info / 'top_level.txt').write_text('fakepkg_2214\n')
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monkeypatch.syspath_prepend(str(tmp_path))
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# The guard only reads sys.modules keys; setitem removes the entry again on teardown.
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monkeypatch.setitem(sys.modules, 'fakepkg_2214', types.ModuleType('fakepkg_2214'))
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before = _loaded_distribution_versions()
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assert before['fakepkg-2214'] == '1.0'
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# What pip does on an upgrade: the old dist-info goes away and a new one takes its place.
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new_info = info.rename(tmp_path / 'fakepkg_2214-2.0.dist-info')
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(new_info / 'METADATA').write_text('Metadata-Version: 2.1\nName: fakepkg-2214\nVersion: 2.0\n')
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assert _runtime_changed_loaded_packages(before) == {'fakepkg-2214': '1.0 -> 2.0'}
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