QTZPOC-20

QTZPOC-20: Remove PyTorch from supported model flavors as it was never functional due to missing runtime dependencies.

README.md

@@ -1109,8 +1109,8 @@ Legacy MinIO object layout (relative key):
 
 ## Model Flavors
 
-`model_config.predict_flavor` / `model_config.transform_flavor` accept `sklearn`, `pyfunc`,
+`model_config.predict_flavor` / `model_config.transform_flavor` accept `sklearn`, `pyfunc` and
-`pytorch` and `joblib`. The flavor is configuration, never discovered from the artifact, and there is
+`joblib`. The flavor is configuration, never discovered from the artifact, and there is
 no fallback between flavors: a failing loader propagates.
 
 `joblib` reads the artifact directly — download, first top-level `.pkl`, `joblib.load` — with the

docs/model-flavors.md

@@ -24,11 +24,13 @@ reads the artifact's `MLmodel` manifest to guess it.
 |---|---|---|---|
 | `sklearn` | yes | yes | `mlflow.sklearn.load_model(<uri>)` |
 | `pyfunc` | yes | yes | `mlflow.pyfunc.load_model(<uri>)`; the wrapper path also unwraps `._model_impl.python_model` |
-| `pytorch` | yes | yes | `mlflow.pytorch.load_model(<uri>)` |
 | `joblib` | yes | yes | downloads the artifact and `joblib.load`s its pickle — **no MLflow flavor loader involved** |
 
 Anything else raises `ValueError` with `INVALID_FLAVOR_MESSAGE`, **before** any artifact is resolved
-or downloaded. The same message and the same four values govern reading and writing.
+or downloaded. The same message and the same three values govern reading and writing.
+
+`pytorch` was accepted until this change and is now rejected like any other unknown value: `torch` is
+not installed in the runtime, so `mlflow.pytorch.load_model` could never have loaded anything there.
 
 **There is no fallback between flavors.** A failing loader propagates: one attempt, one cause. A
 model configured with the wrong flavor fails visibly instead of being rescued silently.

e2e/conftest.py

@@ -546,10 +546,6 @@ def patch_mlflow(mock_mlflow_models, joblib_flavor_artifacts):
     mock_mlflow.pyfunc = MagicMock()
     mock_mlflow.pyfunc.load_model = MagicMock(side_effect=mock_pyfunc_load_model)
 
-    # Mock pytorch.load_model
-    mock_mlflow.pytorch = MagicMock()
-    mock_mlflow.pytorch.load_model = MagicMock(return_value=mock_mlflow_models['predict_model'])
-
     # Mock other mlflow methods that might be called
     mock_mlflow.set_tracking_uri = MagicMock()
     mock_mlflow.get_run = MagicMock(

e2e/scenarios.md

@@ -1124,7 +1124,7 @@ restores `sys.path` in teardown, because the flavor deliberately never removes w
 **Assertions**:
 - Exactly one prediction row, `prediction_status='Good'`, value matching the artifact's model
 - `download_artifacts` called once, with `'prediction_model'`
-- No `mlflow.{sklearn,pyfunc,pytorch}.load_model` call names this model
+- No `mlflow.{sklearn,pyfunc}.load_model` call names this model
 
 ---
 

e2e/test_predictions_batch_joblib_flavor.py

@@ -26,7 +26,7 @@ from laborious.workflows.predictions_batch import PredictionsBatch
 
 base_query = 'SELECT timestamp, variable, value, created_at FROM predictions_schema.laborious_data WHERE model_id = {model_id}'
 
-FLAVOR_LOADERS = ('sklearn', 'pyfunc', 'pytorch')
+FLAVOR_LOADERS = ('sklearn', 'pyfunc')
 
 
 def build_input_data(

laborious/utils/repository/model_repository.py

@@ -45,7 +45,7 @@ ARTIFACTS_PATH = './tmp/artifacts'
 TRANSFORMED_COMPRESSED_PATH = 'artifacts/training_transformer.pkl'
 PREDICTION_COMPRESSED_PATH = 'artifacts/stacking_model.pkl'
 
-INVALID_FLAVOR_MESSAGE = "Invalid flavor. Use 'sklearn' or 'pyfunc' or 'pytorch' or 'joblib'."
+INVALID_FLAVOR_MESSAGE = "Invalid flavor. Use 'sklearn' or 'pyfunc' or 'joblib'."
 
 
 def force_memory_release(logger: Logger):
@@ -550,7 +550,7 @@ class MLFlowRepository(SientiaMonitoring):
         Args:
             model_name (str): The name of the model to download from the registry.
             metadata (dict[str, Any]): Metadata used for structured logging.
-            flavor (str): Model flavor ('pyfunc', 'sklearn', 'pytorch', 'joblib')
+            flavor (str): Model flavor ('pyfunc', 'sklearn', 'joblib')
             artifact_path (str | None): Path to compressed artifacts if model is compressed
 
         Returns:
@@ -564,7 +564,7 @@ class MLFlowRepository(SientiaMonitoring):
               `joblib.load`, which needs no `MLmodel` manifest. The artifact's
               `code` directory, when present, is put on `sys.path` first.
         """
-        if flavor not in ('pyfunc', 'sklearn', 'pytorch', 'joblib'):
+        if flavor not in ('pyfunc', 'sklearn', 'joblib'):
             raise ValueError(INVALID_FLAVOR_MESSAGE)
 
         if flavor == 'joblib':
@@ -584,10 +584,8 @@ class MLFlowRepository(SientiaMonitoring):
         try:
             if flavor == 'pyfunc':
                 model = mlflow.pyfunc.load_model(model_uri)
-            elif flavor == 'sklearn':
-                model = mlflow.sklearn.load_model(model_uri)
             else:
-                model = mlflow.pytorch.load_model(model_uri)
+                model = mlflow.sklearn.load_model(model_uri)
         except Exception as e:
             await self.emit_metric(metric_object=metrics.MODEL_READ_ERROR_COUNT, tags=core_labels)
             raise e
@@ -608,7 +606,7 @@ class MLFlowRepository(SientiaMonitoring):
         Args:
             model_name (str): The name of the model to download.
             metadata (dict[str, Any]): Metadata used for structured logging.
-            flavor (str): Model flavor ('sklearn', 'pyfunc', 'pytorch', 'joblib')
+            flavor (str): Model flavor ('sklearn', 'pyfunc', 'joblib')
             artifact_path (str | None): Path to compressed artifacts if model is compressed
 
         Returns:
@@ -626,7 +624,7 @@ class MLFlowRepository(SientiaMonitoring):
               directory, when present, is put on `sys.path` first.
         """
 
-        if flavor not in ('sklearn', 'pyfunc', 'pytorch', 'joblib'):
+        if flavor not in ('sklearn', 'pyfunc', 'joblib'):
             raise ValueError(INVALID_FLAVOR_MESSAGE)
 
         latest_production_id = self.get_model_run_id(model_name=model_name, stage='Production')
@@ -650,10 +648,8 @@ class MLFlowRepository(SientiaMonitoring):
         try:
             if flavor == 'sklearn':
                 model = mlflow.sklearn.load_model(model_uri)
-            elif flavor == 'pyfunc':
-                model = mlflow.pyfunc.load_model(model_uri)
             else:
-                model = mlflow.pytorch.load_model(model_uri)
+                model = mlflow.pyfunc.load_model(model_uri)
         except Exception as e:
             await self.emit_metric(metric_object=metrics.MODEL_READ_ERROR_COUNT, tags=core_labels)
             raise e
@@ -677,7 +673,7 @@ class MLFlowRepository(SientiaMonitoring):
             model_name (str): Name of the model to download
             metadata (dict[str, Any]): Metadata used for structured logging.
             model_type (str): Type of model ('predict' or 'transform')
-            flavor (str): Model flavor ('sklearn', 'pyfunc', 'pytorch', 'joblib')
+            flavor (str): Model flavor ('sklearn', 'pyfunc', 'joblib')
             load_wrapper (bool): Whether to load wrapper
 
         Returns:
@@ -862,7 +858,7 @@ class MLFlowRepository(SientiaMonitoring):
             metadata (dict[str, Any]): Metadata used for structured logging.
             retention (int): Cache retention time in minutes (0 = no cache).
             model_type (str): Type of model ('predict' or 'transform')
-            flavor (str): Model flavor ('sklearn', 'pyfunc', 'pytorch')
+            flavor (str): Model flavor ('sklearn', 'pyfunc', 'joblib')
 
         Returns:
             Any: Model object.
@@ -950,7 +946,7 @@ class MLFlowRepository(SientiaMonitoring):
             model_name (str): Registered model name.
             data (pd.DataFrame): Input data.
             retention (int): Cache retention in minutes.
-            flavor (str): Model flavor ('sklearn', 'pyfunc', 'pytorch').
+            flavor (str): Model flavor ('sklearn', 'pyfunc', 'joblib').
             metadata (dict[str, Any]): Metadata used for structured logging.
         Returns:
             pd.DataFrame | ndarray: Operation result.
@@ -1228,7 +1224,7 @@ class MLFlowRepository(SientiaMonitoring):
 
         Args:
             model_data (dict): Model holder with keys 'model' and optional 'artifact_path'.
-            flavor (str): Model flavor ('sklearn', 'pyfunc', 'pytorch', 'joblib').
+            flavor (str): Model flavor ('sklearn', 'pyfunc', 'joblib').
             model_type (str): Artifact name, e.g., 'prediction_model' or 'data_model'.
             metadata (dict): Metadata for structured logging.
 
@@ -1257,8 +1253,6 @@ class MLFlowRepository(SientiaMonitoring):
                 model.store_model(artifact_path=model_type, code_path=code_path, to_disk=False)
 
                 self.debug('Model uploaded successfully', metadata)
-            elif flavor == 'pytorch':
-                mlflow.pytorch.log_model(model, model_type)
             elif flavor == 'joblib':
                 with tempfile.TemporaryDirectory() as tmp_dir:
                     pkl_path = path.join(tmp_dir, 'model.pkl')

tests/laborious/utils/repository/test_model_repository.py

@@ -455,17 +455,6 @@ async def test_load_predict_model_pyfunc(mlflow, mlflow_repository):
     )
 
 
-@pytest.mark.asyncio
-async def test_load_predict_model_pytorch(mlflow, mlflow_repository):
-    result = await mlflow_repository.load_predict_model('test_model', {}, 'pytorch')
-    assert result == mlflow.pytorch.load_model.return_value
-    mlflow.pytorch.load_model.assert_called_once_with('models:/test_model/production')
-    mlflow_repository.observe_lag.assert_called_once_with(ANY, metrics.MODEL_READ_LAG, ANY)
-    mlflow_repository.emit_metric.assert_called_once_with(
-        metric_object=metrics.MODEL_READ_COUNT, tags=ANY
-    )
-
-
 @pytest.mark.asyncio
 async def test_load_predict_model_error(mlflow_repository):
     with pytest.raises(ValueError) as e:
@@ -523,23 +512,6 @@ async def test_load_transform_model_pyfunc(mlflow, mlflow_repository):
     )
 
 
-@pytest.mark.asyncio
-async def test_load_transform_model_pytorch(mlflow, mlflow_repository):
-    mlflow_repository.get_model_run_id = MagicMock()
-    mlflow_repository.get_model_uri = MagicMock()
-
-    result = await mlflow_repository.load_transform_model('test_model', {}, 'pytorch')
-    validate_common_load_transform_model_mocks(mlflow_repository, 'test_model')
-
-    assert result == mlflow.pytorch.load_model.return_value
-    mlflow.pytorch.load_model.assert_called_once_with(mlflow_repository.get_model_uri.return_value)
-
-    mlflow_repository.observe_lag.assert_called_once_with(ANY, metrics.MODEL_READ_LAG, ANY)
-    mlflow_repository.emit_metric.assert_called_once_with(
-        metric_object=metrics.MODEL_READ_COUNT, tags=ANY
-    )
-
-
 @pytest.mark.asyncio
 async def test_load_transform_model_error(mlflow_repository):
     mlflow_repository.get_model_run_id = MagicMock()
@@ -1108,20 +1080,6 @@ async def test_log_model_pyfunc(tmp_path, mlflow, mlflow_repository):
     )
 
 
-@pytest.mark.asyncio
-async def test_log_model_pytorch(mlflow, mlflow_repository):
-    model_data = {'model': MagicMock(), 'artifact_path': 'artifact_path'}
-    await mlflow_repository.log_model(
-        model_data, 'pytorch', 'prediction_model', metadata['metadata']
-    )
-    mlflow.pytorch.log_model.assert_called_once_with(model_data['model'], 'prediction_model')
-
-    mlflow_repository.observe_lag.assert_called_once_with(ANY, metrics.MODEL_WRITE_LAG, ANY)
-    mlflow_repository.emit_metric.assert_called_once_with(
-        metric_object=metrics.MODEL_WRITE_COUNT, tags=ANY
-    )
-
-
 @pytest.mark.asyncio
 async def test_log_model_error(mlflow_repository):
     model_data = {'model': MagicMock(), 'artifact_path': 'artifact_path'}
@@ -1777,7 +1735,7 @@ Tests for the joblib flavor on the model load and write paths
 """
 
 JOBLIB_PATH = 'laborious.utils.repository.model_repository.joblib'
-FLAVOR_LOADERS = ('sklearn', 'pyfunc', 'pytorch')
+FLAVOR_LOADERS = ('sklearn', 'pyfunc')
 
 
 @pytest.fixture
@@ -1889,7 +1847,7 @@ async def test_load_transform_model_joblib_resolves_run_once(
 
 @patch(JOBLIB_PATH)
 @pytest.mark.asyncio
-@pytest.mark.parametrize('flavor', ['sklearn', 'pyfunc', 'pytorch'])
+@pytest.mark.parametrize('flavor', ['sklearn', 'pyfunc'])
 async def test_load_predict_model_flavor_failure_propagates(
     joblib_mock, mlflow, mlflow_repository, flavor
 ):
@@ -1910,7 +1868,7 @@ async def test_load_predict_model_flavor_failure_propagates(
 
 @patch(JOBLIB_PATH)
 @pytest.mark.asyncio
-@pytest.mark.parametrize('flavor', ['sklearn', 'pyfunc', 'pytorch'])
+@pytest.mark.parametrize('flavor', ['sklearn', 'pyfunc'])
 async def test_load_transform_model_flavor_failure_propagates(
     joblib_mock, mlflow, mlflow_repository, flavor
 ):
@@ -2361,10 +2319,57 @@ async def test_log_model_invalid_flavor(joblib_mock, mlflow, mlflow_repository):
     mlflow_repository.observe_lag.assert_not_called()
 
 
+@patch(JOBLIB_PATH)
+@pytest.mark.asyncio
+async def test_load_predict_model_rejects_pytorch(joblib_mock, mlflow, mlflow_repository):
+    with pytest.raises(ValueError) as exc_info:
+        await mlflow_repository.load_predict_model('test_model', {}, 'pytorch')
+
+    assert str(exc_info.value) == INVALID_FLAVOR_MESSAGE
+    mlflow.pytorch.load_model.assert_not_called()
+    joblib_mock.load.assert_not_called()
+    mlflow_repository.client.download_artifacts.assert_not_called()
+    mlflow_repository.emit_metric.assert_not_called()
+
+
+@patch(JOBLIB_PATH)
+@pytest.mark.asyncio
+async def test_load_transform_model_rejects_pytorch(joblib_mock, mlflow, mlflow_repository):
+    with pytest.raises(ValueError) as exc_info:
+        await mlflow_repository.load_transform_model('test_model', {}, 'pytorch')
+
+    assert str(exc_info.value) == INVALID_FLAVOR_MESSAGE
+    mlflow.pytorch.load_model.assert_not_called()
+    joblib_mock.load.assert_not_called()
+    mlflow_repository.client.download_artifacts.assert_not_called()
+    mlflow_repository.emit_metric.assert_not_called()
+
+
+@patch(JOBLIB_PATH)
+@pytest.mark.asyncio
+async def test_log_model_rejects_pytorch(joblib_mock, mlflow, mlflow_repository):
+    model_data = {'model': MagicMock(), 'artifact_path': 'artifact_path'}
+
+    with pytest.raises(ValueError) as exc_info:
+        await mlflow_repository.log_model(
+            model_data, 'pytorch', 'prediction_model', metadata['metadata']
+        )
+
+    assert str(exc_info.value) == INVALID_FLAVOR_MESSAGE
+    mlflow.pytorch.log_model.assert_not_called()
+    joblib_mock.dump.assert_not_called()
+    mlflow_repository.emit_metric.assert_called_once_with(
+        metric_object=metrics.MODEL_WRITE_ERROR_COUNT, tags=ANY
+    )
+    mlflow_repository.observe_lag.assert_not_called()
+
+
 def test_invalid_flavor_message_lists_every_accepted_flavor():
-    for flavor in ('sklearn', 'pyfunc', 'pytorch', 'joblib'):
+    for flavor in ('sklearn', 'pyfunc', 'joblib'):
         assert f"'{flavor}'" in INVALID_FLAVOR_MESSAGE
 
+    assert 'pytorch' not in INVALID_FLAVOR_MESSAGE
+
 
 @patch(JOBLIB_PATH)
 @pytest.mark.asyncio