Implement: multiple save and load for mlflow registry

.gitignore

@@ -78,6 +78,8 @@ target/
 #mlflow
 /.mlruns
 *.db
+mlruns/
+mlartifacts/
 
 # Jupyter Notebook
 .ipynb_checkpoints
@@ -169,4 +171,7 @@ cython_debug/
 # Mac related
 *.DS_Store
 
+# vscode
+.vscode/
+
 .python-version

numalogic/registry/mlflow_registry.py

@@ -18,6 +18,7 @@
 import mlflow.pyfunc
 import mlflow.pytorch
 import mlflow.sklearn
+import mlflow
 from mlflow.entities.model_registry import ModelVersion
 from mlflow.exceptions import RestException
 from mlflow.protos.databricks_pb2 import ErrorCode, RESOURCE_DOES_NOT_EXIST
@@ -187,6 +188,43 @@ def load(
                 self._save_in_cache(model_key, artifact_data)
             return artifact_data
 
+    def load_multiple(
+        self,
+        skeys: KEYS,
+        dkeys: KEYS,
+    ) -> Optional[ArtifactData]:
+        """
+        Load multiple artifacts from the registry for pyfunc models.
+        Args:
+            skeys (KEYS): The source keys of the artifacts to load.
+            dkeys: dynamic key fields as list/tuple of strings.
+
+        Returns
+        -------
+            Optional[ArtifactData]: The loaded ArtifactData object if available otherwise None.
+            ArtifactData should contain a dictionary of artifacts.
+        """
+        loaded_model = self.load(skeys=skeys, dkeys=dkeys, artifact_type="pyfunc")
+        if loaded_model is None:
+            return None
+
+        try:
+            unwrapped_composite_model = loaded_model.artifact.unwrap_python_model()
+        except mlflow.exceptions.MlflowException as e:
+            raise TypeError("The loaded model is not a valid pyfunc Python model.") from e
+        except AttributeError:
+            _LOGGER.exception("The loaded model does not have an unwrap_python_model method")
+            return None
+        except Exception:
+            _LOGGER.exception("Unexpected error occurred while unwrapping python model.")
+            return None
+        else:
+            return ArtifactData(
+                artifact=unwrapped_composite_model.dict_artifacts,
+                metadata=loaded_model.metadata,
+                extras=loaded_model.extras,
+            )
+
     @staticmethod
     def __log_mlflow_err(mlflow_err: RestException, model_key: str) -> None:
         if ErrorCode.Value(mlflow_err.error_code) == RESOURCE_DOES_NOT_EXIST:
@@ -225,7 +263,10 @@ def save(
         handler = self.handler_from_type(artifact_type)
         try:
             mlflow.start_run(run_id=run_id)
-            handler.log_model(artifact, "model", registered_model_name=model_key)
+            if artifact_type == "pyfunc":
+                handler.log_model("model", python_model=artifact, registered_model_name=model_key)
+            else:
+                handler.log_model(artifact, "model", registered_model_name=model_key)
             if metadata:
                 mlflow.log_params(metadata)
             model_version = self.transition_stage(skeys=skeys, dkeys=dkeys)
@@ -238,6 +279,40 @@ def save(
         finally:
             mlflow.end_run()
 
+    def save_multiple(
+        self,
+        skeys: KEYS,
+        dkeys: KEYS,
+        dict_artifacts: dict[str, artifact_t],
+        **metadata: META_VT,
+    ) -> Optional[ModelVersion]:
+        """
+        Saves multiple artifacts into mlflow registry. The last save stores all the
+        artifact versions in the metadata.
+
+        Args:
+        ----
+            skeys (KEYS): Static key fields as a list or tuple of strings.
+            dkeys (KEYS): Dynamic key fields as a list or tuple of strings.
+            dict_artifacts (dict[str, artifact_t]): Dictionary of artifacts to save.
+            **metadata (META_VT): Additional metadata to be saved with the artifacts.
+
+        Returns
+        -------
+            Optional[ModelVersion]: An instance of the MLflow ModelVersion.
+
+        """
+        if len(dict_artifacts) == 1:
+            _LOGGER.warning("Only one element in dict_artifacts. Please use save directly.")
+        multiple_artifacts = CompositeModel(skeys=skeys, dict_artifacts=dict_artifacts, **metadata)
+        return self.save(
+            skeys=skeys,
+            dkeys=dkeys,
+            artifact=multiple_artifacts,
+            artifact_type="pyfunc",
+            **metadata,
+        )
+
     @staticmethod
     def is_artifact_stale(artifact_data: ArtifactData, freq_hr: int) -> bool:
         """Returns whether the given artifact is stale or not, i.e. if
@@ -338,3 +413,45 @@ def __load_artifacts(
             version_info.version,
         )
         return model, metadata
+
+
+class CompositeModel(mlflow.pyfunc.PythonModel):
+    """A composite model that represents multiple artifacts.
+
+    This class extends the `mlflow.pyfunc.PythonModel` class and is used to store and load
+    multiple artifacts in the MLflow registry. It provides a convenient way to manage and
+    organize multiple artifacts associated with a single model.
+
+    Args:
+        skeys (KEYS): The static keys of the artifacts.
+        dict_artifacts (dict[str, KeyedArtifact]): A dictionary mapping dynamic keys to
+            `KeyedArtifact` objects.
+        **metadata (META_VT): Additional metadata associated with the artifacts.
+
+    Methods
+    -------
+        predict: Not implemented for our use case.
+
+    Attributes
+    ----------
+        skeys (KEYS): The static keys of the artifacts.
+        dict_artifacts (dict[str, KeyedArtifact]): A dictionary mapping dynamic keys to
+            `KeyedArtifact` objects.
+        metadata (META_VT): Additional metadata associated with the artifacts.
+    """
+
+    __slots__ = ("skeys", "dict_artifacts", "metadata")
+
+    def __init__(self, skeys: KEYS, dict_artifacts: dict[str, artifact_t], **metadata: META_VT):
+        self.skeys = skeys
+        self.dict_artifacts = dict_artifacts
+        self.metadata = metadata
+
+    def predict(self):
+        """
+        Predict method is not implemented for our use case.
+
+        The CompositeModel class is designed to store and load multiple artifacts,
+        and the predict method is not required for this functionality.
+        """
+        raise NotImplementedError("The predict method is not implemented for CompositeModel.")

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "numalogic"
-version = "0.13.2"
+version = "0.13.3"
 description = "Collection of operational Machine Learning models and tools."
 authors = ["Numalogic Developers"]
 packages = [{ include = "numalogic" }]

tests/registry/_mlflow_utils.py

@@ -9,8 +9,11 @@
 from mlflow.store.entities import PagedList
 from sklearn.preprocessing import StandardScaler
 from torch import tensor
+from mlflow.models import Model
 
+from numalogic.models.autoencoder.variants.vanilla import VanillaAE
 from numalogic.models.threshold import StdDevThreshold
+from numalogic.registry.mlflow_registry import CompositeModel
 
 
 def create_model():
@@ -135,6 +138,103 @@ def mock_log_model_sklearn(*_, **__):
     )
 
 
+def mock_log_model_pyfunc(*_, **__):
+    return ModelInfo(
+        artifact_path="model",
+        flavors={
+            "pyfunc": {"model_data": "data", "pyfunc_version": "1.11.0", "code": None},
+            "python_function": {
+                "pickle_module_name": "mlflow.pyfunc.pickle_module",
+                "loader_module": "mlflow.pyfunc",
+                "python_version": "3.8.5",
+                "data": "data",
+                "env": "conda.yaml",
+            },
+        },
+        model_uri="runs:/a7c0b376530b40d7b23e6ce2081c899c/model",
+        model_uuid="a7c0b376530b40d7b23e6ce2081c899c",
+        run_id="a7c0b376530b40d7b23e6ce2081c899c",
+        saved_input_example_info=None,
+        signature_dict=None,
+        utc_time_created="2022-05-23 22:35:59.557372",
+        mlflow_version="2.0.1",
+        signature=None,
+    )
+
+
+def mock_load_model_pyfunc(*_, **__):
+    artifact_path = "model"
+    flavors = {
+        "python_function": {
+            "cloudpickle_version": "3.0.0",
+            "code": None,
+            "env": {"conda": "conda.yaml", "virtualenv": "python_env.yaml"},
+            "loader_module": "mlflow.pyfunc.model",
+            "python_model": "python_model.pkl",
+            "python_version": "3.10.14",
+            "streamable": False,
+        }
+    }
+    model_size_bytes = 8912
+    model_uuid = "ae27ecc166c94c01a4f4dccaf84ca5dc"
+    run_id = "7e85a3fa46d44e668c840f3dddc909c3"
+    utc_time_created = "2024-09-18 17:12:41.501209"
+    model = Model(
+        artifact_path=artifact_path,
+        flavors=flavors,
+        model_size_bytes=model_size_bytes,
+        model_uuid=model_uuid,
+        run_id=run_id,
+        utc_time_created=utc_time_created,
+        mlflow_version="2.16.0",
+    )
+    return mlflow.pyfunc.PyFuncModel(
+        model_meta=model,
+        model_impl=TestObject(
+            python_model=CompositeModel(
+                skeys=["error"],
+                dict_artifacts={
+                    "inference": VanillaAE(10),
+                    "precrocessing": StandardScaler(),
+                    "threshold": StdDevThreshold(),
+                },
+                **{"learning_rate": 0.01},
+            )
+        ),
+    )
+
+
+def mock_load_model_pyfunc_type_error(*_, **__):
+    artifact_path = "model"
+    flavors = {
+        "python_function": {
+            "cloudpickle_version": "3.0.0",
+            "code": None,
+            "env": {"conda": "conda.yaml", "virtualenv": "python_env.yaml"},
+            "loader_module": "mlflow.pytorch.model",
+            "python_model": "python_model.pkl",
+            "python_version": "3.10.14",
+            "streamable": False,
+        }
+    }
+    model_size_bytes = 8912
+    model_uuid = "ae27ecc166c94c01a4f4dccaf84ca5dc"
+    run_id = "7e85a3fa46d44e668c840f3dddc909c3"
+    utc_time_created = "2024-09-18 17:12:41.501209"
+    model = Model(
+        artifact_path=artifact_path,
+        flavors=flavors,
+        model_size_bytes=model_size_bytes,
+        model_uuid=model_uuid,
+        run_id=run_id,
+        utc_time_created=utc_time_created,
+        mlflow_version="2.16.0",
+    )
+    return mlflow.pyfunc.PyFuncModel(
+        model_meta=model, model_impl=mlflow.pytorch._PyTorchWrapper(VanillaAE(10), device="cpu")
+    )
+
+
 def mock_transition_stage(*_, **__):
     return ModelVersion(
         creation_timestamp=1653402941169,
@@ -303,6 +403,25 @@ def return_sklearn_rundata():
     )
 
 
+def return_pyfunc_rundata():
+    return Run(
+        run_info=RunInfo(
+            artifact_uri="mlflow-artifacts:/0/a7c0b376530b40d7b23e6ce2081c899c/artifacts/model",
+            end_time=None,
+            experiment_id="0",
+            lifecycle_stage="active",
+            run_id="a7c0b376530b40d7b23e6ce2081c899c",
+            run_uuid="a7c0b376530b40d7b23e6ce2081c899c",
+            start_time=1658788772612,
+            status="RUNNING",
+            user_id="lol",
+        ),
+        run_data=RunData(
+            metrics={}, tags={}, params=[mlflow.entities.Param("learning_rate", "0.01")]
+        ),
+    )
+
+
 def return_pytorch_rundata_dict():
     return Run(
         run_info=RunInfo(
@@ -318,3 +437,8 @@ def return_pytorch_rundata_dict():
         ),
         run_data=RunData(metrics={}, tags={}, params=[mlflow.entities.Param("lr", "0.001")]),
     )
+
+
+class TestObject(mlflow.pyfunc.PythonModel):
+    def __init__(self, python_model):
+        self.python_model = python_model