feat: Support Model Serialization in Vertex Experiments(xgboost)

PiperOrigin-RevId: 501505712

google/cloud/aiplatform/metadata/_models.py

@@ -15,6 +15,7 @@
 # limitations under the License.
 #
 
+import importlib
 import os
 import pickle
 import tempfile
@@ -45,12 +46,17 @@
         "save_method": "_save_sklearn_model",
         "load_method": "_load_sklearn_model",
         "model_file": "model.pkl",
-    }
+    },
+    "xgboost": {
+        "save_method": "_save_xgboost_model",
+        "load_method": "_load_xgboost_model",
+        "model_file": "model.bst",
+    },
 }
 
 
 def save_model(
-    model: "sklearn.base.BaseEstimator",  # noqa: F821
+    model: Union["sklearn.base.BaseEstimator", "xgb.Booster"],  # noqa: F821
     artifact_id: Optional[str] = None,
     *,
     uri: Optional[str] = None,
@@ -63,7 +69,7 @@ def save_model(
 ) -> google_artifact_schema.ExperimentModel:
     """Saves a ML model into a MLMD artifact.
 
-    Supported model frameworks: sklearn.
+    Supported model frameworks: sklearn, xgboost.
 
     Example usage:
         aiplatform.init(project="my-project", location="my-location", staging_bucket="gs://my-bucket")
@@ -72,7 +78,7 @@ def save_model(
         aiplatform.save_model(model, "my-sklearn-model")
 
     Args:
-        model (sklearn.base.BaseEstimator):
+        model (Union["sklearn.base.BaseEstimator", "xgb.Booster"]):
             Required. A machine learning model.
         artifact_id (str):
             Optional. The resource id of the artifact. This id must be globally unique
@@ -116,10 +122,23 @@ def save_model(
     except ImportError:
         pass
     else:
-        if isinstance(model, sklearn.base.BaseEstimator):
+        # An instance of sklearn.base.BaseEstimator might be a sklearn model
+        # or a xgboost/lightgbm model implemented on top of sklearn.
+        if isinstance(
+            model, sklearn.base.BaseEstimator
+        ) and model.__class__.__module__.startswith("sklearn"):
             framework_name = "sklearn"
             framework_version = sklearn.__version__
 
+    try:
+        import xgboost as xgb
+    except ImportError:
+        pass
+    else:
+        if isinstance(model, (xgb.Booster, xgb.XGBModel)):
+            framework_name = "xgboost"
+            framework_version = xgb.__version__
+
     if framework_name not in _FRAMEWORK_SPECS:
         raise ValueError(
             f"Model type {model.__class__.__module__}.{model.__class__.__name__} not supported."
@@ -305,9 +324,24 @@ def _save_sklearn_model(
         pickle.dump(model, f, protocol=_PICKLE_PROTOCOL)
 
 
+def _save_xgboost_model(
+    model: Union["xgb.Booster", "xgb.XGBModel"],  # noqa: F821
+    path: str,
+):
+    """Saves a xgboost model.
+
+    Args:
+        model (Union[xgb.Booster, xgb.XGBModel]):
+            Requred. A xgboost model.
+        path (str):
+            Required. The local path to save the model.
+    """
+    model.save_model(path)
+
+
 def load_model(
     model: Union[str, google_artifact_schema.ExperimentModel]
-) -> "sklearn.base.BaseEstimator":  # noqa: F821
+) -> Union["sklearn.base.BaseEstimator", "xgb.Booster"]:  # noqa: F821
     """Retrieves the original ML model from an ExperimentModel resource.
 
     Args:
@@ -375,7 +409,44 @@ def _load_sklearn_model(
     return sk_model
 
 
-# TODO(b/264893283)
+def _load_xgboost_model(
+    model_file: str,
+    model_artifact: google_artifact_schema.ExperimentModel,
+) -> Union["xgb.Booster", "xgb.XGBModel"]:  # noqa: F821
+    """Loads a xgboost model from local path.
+
+    Args:
+        model_file (str):
+            Required. A local model file to load.
+        model_artifact (google_artifact_schema.ExperimentModel):
+            Required. The artifact that saved the model.
+    Returns:
+        The xgboost model instance.
+
+    Raises:
+        ImportError: if xgboost is not installed.
+    """
+    try:
+        import xgboost as xgb
+    except ImportError:
+        raise ImportError(
+            "xgboost is not installed and is required for loading models."
+        ) from None
+
+    if xgb.__version__ < model_artifact.framework_version:
+        _LOGGER.warning(
+            f"The original model was saved via xgboost {model_artifact.framework_version}. "
+            f"You are using xgboost {xgb.__version__}."
+            "Attempting to load model..."
+        )
+
+    module, class_name = model_artifact.model_class.rsplit(".", maxsplit=1)
+    xgb_model = getattr(importlib.import_module(module), class_name)()
+    xgb_model.load_model(model_file)
+
+    return xgb_model
+
+
 def register_model(
     model: Union[str, google_artifact_schema.ExperimentModel],
     *,

google/cloud/aiplatform/metadata/experiment_run_resource.py

@@ -1106,7 +1106,7 @@ def log_classification_metrics(
     @_v1_not_supported
     def log_model(
         self,
-        model: "sklearn.base.BaseEstimator",  # noqa: F821
+        model: Union["sklearn.base.BaseEstimator", "xgb.Booster"],  # noqa: F821
         artifact_id: Optional[str] = None,
         *,
         uri: Optional[str] = None,
@@ -1121,7 +1121,7 @@ def log_model(
     ) -> google_artifact_schema.ExperimentModel:
         """Saves a ML model into a MLMD artifact and log it to this ExperimentRun.
 
-        Supported model frameworks: sklearn.
+        Supported model frameworks: sklearn, xgboost.
 
         Example usage:
             model = LinearRegression()
@@ -1136,7 +1136,7 @@ def log_model(
                 aiplatform.log_model(model, "my-sklearn-model")
 
         Args:
-            model (sklearn.base.BaseEstimator):
+            model (Union["sklearn.base.BaseEstimator", "xgb.Booster"]):
                 Required. A machine learning model.
             artifact_id (str):
                 Optional. The resource id of the artifact. This id must be globally unique

google/cloud/aiplatform/metadata/metadata.py

@@ -474,7 +474,7 @@ def log_classification_metrics(
 
     def log_model(
         self,
-        model: "sklearn.base.BaseEstimator",  # noqa: F821
+        model: Union["sklearn.base.BaseEstimator", "xgb.Booster"],  # noqa: F821
         artifact_id: Optional[str] = None,
         *,
         uri: Optional[str] = None,
@@ -489,7 +489,7 @@ def log_model(
     ) -> google_artifact_schema.ExperimentModel:
         """Saves a ML model into a MLMD artifact and log it to this ExperimentRun.
 
-        Supported model frameworks: sklearn.
+        Supported model frameworks: sklearn, xgboost.
 
         Example usage:
             model = LinearRegression()
@@ -504,7 +504,7 @@ def log_model(
                 aiplatform.log_model(model, "my-sklearn-model")
 
         Args:
-            model (sklearn.base.BaseEstimator):
+            model (Union["sklearn.base.BaseEstimator", "xgb.Booster"]):
                 Required. A machine learning model.
             artifact_id (str):
                 Optional. The resource id of the artifact. This id must be globally unique

google/cloud/aiplatform/metadata/schema/google/artifact_schema.py

@@ -15,7 +15,7 @@
 # limitations under the License.
 
 import copy
-from typing import Optional, Dict, List, Sequence
+from typing import Optional, Dict, List, Sequence, Union
 
 from google.auth import credentials as auth_credentials
 from google.cloud.aiplatform import explain
@@ -742,7 +742,9 @@ def framework_version(self) -> Optional[str]:
     def model_class(self) -> Optional[str]:
         return self.metadata.get("modelClass")
 
-    def load_model(self) -> "sklearn.base.BaseEstimator":  # noqa: F821
+    def load_model(
+        self,
+    ) -> Union["sklearn.base.BaseEstimator", "xgb.Booster"]:  # noqa: F821
         """Retrieves the original ML model from an ExperimentModel.
 
         Example usage:

setup.py

@@ -93,7 +93,7 @@
 testing_extra_require = (
     full_extra_require
     + profiler_extra_require
-    + ["grpcio-testing", "pytest-asyncio", "pytest-xdist", "ipython", "kfp"]
+    + ["grpcio-testing", "pytest-asyncio", "pytest-xdist", "ipython", "kfp", "xgboost"]
 )