structure saas with tools

.venv/lib/python3.10/site-packages/vertex_ray/predict/xgboost/__init__.py

@@ -0,0 +1,22 @@
+"""Ray on Vertex AI Prediction Tensorflow."""
+
+# -*- coding: utf-8 -*-
+
+# Copyright 2023 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+from .register import register_xgboost
+
+__all__ = ("register_xgboost",)

.venv/lib/python3.10/site-packages/vertex_ray/predict/xgboost/register.py

@@ -0,0 +1,189 @@
+"""Regsiter XGBoost for Ray on Vertex AI."""
+
+# -*- coding: utf-8 -*-
+
+# Copyright 2023 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import os
+import pickle
+import ray
+import tempfile
+from typing import Optional, TYPE_CHECKING
+import warnings
+from google.cloud import aiplatform
+from google.cloud.aiplatform import initializer
+from google.cloud.aiplatform import utils
+from google.cloud.aiplatform.utils import gcs_utils
+from google.cloud.aiplatform.vertex_ray.predict.util import constants
+from google.cloud.aiplatform.vertex_ray.predict.util import (
+    predict_utils,
+)
+from google.cloud.aiplatform.vertex_ray.util._validation_utils import (
+    _V2_4_WARNING_MESSAGE,
+    _V2_9_WARNING_MESSAGE,
+)
+
+
+try:
+    from ray.train import xgboost as ray_xgboost
+
+    if TYPE_CHECKING:
+        import xgboost
+
+except ModuleNotFoundError as mnfe:
+    if ray.__version__ == "2.9.3":
+        raise ModuleNotFoundError("XGBoost isn't installed.") from mnfe
+    else:
+        xgboost = None
+
+
+def register_xgboost(
+    checkpoint: "ray_xgboost.XGBoostCheckpoint",
+    artifact_uri: Optional[str] = None,
+    display_name: Optional[str] = None,
+    xgboost_version: Optional[str] = None,
+    **kwargs,
+) -> aiplatform.Model:
+    """Uploads a Ray XGBoost Checkpoint as XGBoost Model to Model Registry.
+
+    Example usage:
+        from vertex_ray.predict import xgboost
+        from ray.train.xgboost import XGBoostCheckpoint
+
+        trainer = XGBoostTrainer(...)
+        result = trainer.fit()
+        xgboost_checkpoint = XGBoostCheckpoint.from_checkpoint(result.checkpoint)
+
+        my_model = xgboost.register_xgboost(
+            checkpoint=xgboost_checkpoint,
+            artifact_uri="gs://{gcs-bucket-name}/path/to/store",
+            display_name="my-ray-on-vertex-xgboost-model",
+        )
+
+
+    Args:
+        checkpoint: XGBoostCheckpoint instance.
+        artifact_uri (str):
+            The path to the directory where Model Artifacts will be saved. If
+            not set, will use staging bucket set in aiplatform.init().
+        display_name (str):
+            Optional. The display name of the Model. The name can be up to 128
+            characters long and can be consist of any UTF-8 characters.
+        xgboost_version (str): Optional. The version of the XGBoost serving container.
+                Supported versions:
+                https://cloud.google.com/vertex-ai/docs/predictions/pre-built-containers
+                If the version is not specified, the version 1.6 is used.
+        **kwargs:
+            Any kwargs will be passed to aiplatform.Model registration.
+
+    Returns:
+        model (aiplatform.Model):
+                Instantiated representation of the uploaded model resource.
+
+    Raises:
+        ValueError: Invalid Argument.
+        RuntimeError: Only Ray version 2.9.3 is supported.
+    """
+    ray_version = ray.__version__
+    if ray_version != "2.9.3":
+        raise RuntimeError(
+            f"Ray version {ray_version} is not supported to upload XGBoost"
+            " model to Vertex Model Registry yet. Please use Ray 2.9.3."
+        )
+    if ray_version == "2.9.3":
+        warnings.warn(_V2_9_WARNING_MESSAGE, DeprecationWarning, stacklevel=1)
+    artifact_uri = artifact_uri or initializer.global_config.staging_bucket
+    predict_utils.validate_artifact_uri(artifact_uri)
+    display_model_name = (
+        (f"ray-on-vertex-registered-xgboost-model-{utils.timestamped_unique_name()}")
+        if display_name is None
+        else display_name
+    )
+    model = _get_xgboost_model_from(checkpoint)
+
+    model_dir = os.path.join(artifact_uri, display_model_name)
+    file_path = os.path.join(model_dir, constants._PICKLE_FILE_NAME)
+    if xgboost_version is None:
+        xgboost_version = constants._XGBOOST_VERSION
+
+    with tempfile.NamedTemporaryFile(suffix=constants._PICKLE_EXTENTION) as temp_file:
+        pickle.dump(model, temp_file)
+        gcs_utils.upload_to_gcs(temp_file.name, file_path)
+        return aiplatform.Model.upload_xgboost_model_file(
+            model_file_path=temp_file.name,
+            display_name=display_model_name,
+            xgboost_version=xgboost_version,
+            **kwargs,
+        )
+
+
+def _get_xgboost_model_from(
+    checkpoint: "ray_xgboost.XGBoostCheckpoint",
+) -> "xgboost.Booster":
+    """Converts a XGBoostCheckpoint to XGBoost model.
+
+    Args:
+        checkpoint: XGBoostCheckpoint instance.
+
+    Returns:
+        A XGBoost core Booster
+
+    Raises:
+        ValueError: Invalid Argument.
+        ModuleNotFoundError: XGBoost isn't installed.
+        RuntimeError: Model not found.
+        RuntimeError: Ray version 2.4 is not supported.
+        RuntimeError: Only Ray version 2.9.3 is supported.
+    """
+    ray_version = ray.__version__
+    if ray_version == "2.4.0":
+        raise RuntimeError(_V2_4_WARNING_MESSAGE)
+    if ray_version != "2.9.3":
+        raise RuntimeError(
+            f"Ray version {ray_version} is not supported to convert a XGBoost"
+            " checkpoint to XGBoost model on Vertex yet. Please use Ray 2.9.3."
+        )
+
+    try:
+        # This works for Ray v2.5
+        return checkpoint.get_model()
+    except AttributeError:
+        # This works for Ray v2.9
+        model_file_name = ray.train.xgboost.XGBoostCheckpoint.MODEL_FILENAME
+
+    model_path = os.path.join(checkpoint.path, model_file_name)
+
+    try:
+        import xgboost
+
+    except ModuleNotFoundError as mnfe:
+        raise ModuleNotFoundError("XGBoost isn't installed.") from mnfe
+
+    booster = xgboost.Booster()
+    if os.path.exists(model_path):
+        booster.load_model(model_path)
+        return booster
+
+    try:
+        # Download from GCS to temp and then load_model
+        with tempfile.TemporaryDirectory() as temp_dir:
+            gcs_utils.download_from_gcs("gs://" + checkpoint.path, temp_dir)
+            booster.load_model(f"{temp_dir}/{model_file_name}")
+            return booster
+    except Exception as e:
+        raise RuntimeError(
+            f"{model_file_name} not found in this checkpoint due to: {e}."
+        )