Merge branch 'main' into feature/model_group

mrDzurb · web-flow · commit ed7a9724d66b · 2025-10-27T20:49:09.000-07:00
diff --git a/ads/aqua/model/model.py b/ads/aqua/model/model.py
@@ -1030,6 +1030,11 @@ def list(
         aqua_models = []
         inference_containers = self.get_container_config().to_dict().get("inference")
         for model in models:
+            # Skip models without required tags early
+            freeform_tags = model.freeform_tags or {}
+            if Tags.AQUA_TAG.lower() not in {tag.lower() for tag in freeform_tags}:
+                continue
+
             aqua_models.append(
                 AquaModelSummary(
                     **self._process_model(
@@ -1040,6 +1045,8 @@ def list(
                     project_id=project_id or UNKNOWN,
                 )
             )
+
+        # Adds service models to cache
         if category == SERVICE:
             self._service_models_cache.__setitem__(
                 key=AQUA_SERVICE_MODELS, value=aqua_models
diff --git a/ads/opctl/backend/ads_ml_job.py b/ads/opctl/backend/ads_ml_job.py
@@ -11,11 +11,16 @@
 import shutil
 import tempfile
 import time
+import re
 from distutils import dir_util
 from typing import Dict, Tuple, Union
 
 from ads.common.auth import AuthContext, AuthType, create_signer
 from ads.common.oci_client import OCIClientFactory
+from ads.config import (
+    CONDA_BUCKET_NAME,
+    CONDA_BUCKET_NS,
+)
 from ads.jobs import (
     ContainerRuntime,
     DataScienceJob,
@@ -65,6 +70,32 @@ def __init__(self, config: Dict) -> None:
         self.auth_type = config["execution"].get("auth")
         self.profile = config["execution"].get("oci_profile", None)
         self.client = OCIClientFactory(**self.oci_auth).data_science
+        self.object_storage = OCIClientFactory(**self.oci_auth).object_storage
+
+    def _get_latest_conda_pack(self,
+                               prefix,
+                               python_version,
+                               base_conda) -> str:
+        """
+        get the latest conda pack.
+        """
+        try:
+            objects = self.object_storage.list_objects(namespace_name=CONDA_BUCKET_NS,
+                                                       bucket_name=CONDA_BUCKET_NAME,
+                                                       prefix=prefix).data.objects
+            py_str = python_version.replace(".", "")
+            py_filter = [obj for obj in objects if f"p{py_str}" in obj.name]
+
+            def extract_version(obj_name):
+                match = re.search(rf"{prefix}([\d.]+)/", obj_name)
+                return tuple(map(int, match.group(1).split("."))) if match else (0,)
+
+            latest_obj = max(py_filter, key=lambda obj: extract_version(obj.name))
+            return latest_obj.name.split("/")[-1]
+        except Exception as e:
+            logger.warning(f"Error while fetching latest conda pack: {e}")
+            return base_conda
+
 
     def init(
         self,
@@ -100,6 +131,16 @@ def init(
             or ""
         ).lower()
 
+        # If a tag is present
+        if ":" in conda_slug:
+            base_conda = conda_slug.split(":")[0]
+            conda_slug = self._get_latest_conda_pack(
+                self.config["prefix"],
+                self.config["python_version"],
+                base_conda
+            )
+            logger.info(f"Proceeding with the {conda_slug} conda pack.")
+
         # if conda slug contains '/' then the assumption is that it is a custom conda pack
         # the conda prefix needs to be added
         if "/" in conda_slug:
diff --git a/ads/opctl/operator/common/backend_factory.py b/ads/opctl/operator/common/backend_factory.py
@@ -362,6 +362,8 @@ def _init_backend_config(
                         if operator_info.conda_type == PACK_TYPE.SERVICE
                         else operator_info.conda_prefix,
                         "freeform_tags": freeform_tags,
+                        "python_version": operator_info.python_version,
+                        "prefix": operator_info.prefix,
                     }
                 },
                 {
diff --git a/ads/opctl/operator/common/operator_loader.py b/ads/opctl/operator/common/operator_loader.py
@@ -152,6 +152,8 @@ class OperatorInfo(DataClassSerializable):
     description: str = ""
     version: str = ""
     conda: str = ""
+    prefix: str = ""
+    python_version: str = "3.11"
     conda_type: str = ""
     path: str = ""
     keywords: List[str] = None
diff --git a/ads/opctl/operator/lowcode/anomaly/MLoperator b/ads/opctl/operator/lowcode/anomaly/MLoperator
@@ -2,7 +2,9 @@ type: anomaly
 version: v1
 conda_type: service
 name: Anomaly Detection Operator
-conda: anomaly_p310_cpu_x86_64_v1
+conda: anomaly_p311_cpu_x86_64_v2:latest
+prefix: service_pack/cpu/AI_Anomaly_Detection_Operator/
+python_version: "3.11"
 gpu: no
 keywords:
   - Anomaly Detection
diff --git a/ads/opctl/operator/lowcode/common/utils.py b/ads/opctl/operator/lowcode/common/utils.py
@@ -218,17 +218,12 @@ def _write_file(local_filename, remote_filename, storage_options, **kwargs):
 
 
 def load_pkl(filepath):
-    return _safe_write(fn=_load_pkl, filepath=filepath)
-
-
-def _load_pkl(filepath):
     storage_options = {}
     if ObjectStorageDetails.is_oci_path(filepath):
         storage_options = default_signer()
 
     with fsspec.open(filepath, "rb", **storage_options) as f:
         return cloudpickle.load(f)
-    return None
 
 
 def write_pkl(obj, filename, output_dir, storage_options):
diff --git a/ads/opctl/operator/lowcode/forecast/MLoperator b/ads/opctl/operator/lowcode/forecast/MLoperator
@@ -2,7 +2,9 @@ type: forecast
 version: v1
 name: Forecasting Operator
 conda_type: service
-conda: forecast_p310_cpu_x86_64_v4
+conda: forecast_p311_cpu_x86_64_v10:latest
+prefix: service_pack/cpu/AI_Forecasting_Operator/
+python_version: "3.11"
 gpu: no
 jobs_default_params:
   shape_name: VM.Standard.E4.Flex
diff --git a/ads/opctl/operator/lowcode/forecast/model/arima.py b/ads/opctl/operator/lowcode/forecast/model/arima.py
@@ -85,7 +85,11 @@ def _train_model(self, i, s_id, df, model_kwargs):
             X_pred = self.get_horizon(data).drop(target, axis=1)
 
             if self.loaded_models is not None and s_id in self.loaded_models:
-                model = self.loaded_models[s_id]
+                model = self.loaded_models[s_id]["model"]
+                order = model.order
+                seasonal_order = model.seasonal_order
+                model = pm.ARIMA(order=order, seasonal_order=seasonal_order)
+                model.fit(y=y, X=X_in)
             else:
                 # Build and fit model
                 model = pm.auto_arima(y=y, X=X_in, **model_kwargs)
diff --git a/ads/opctl/operator/lowcode/forecast/model/automlx.py b/ads/opctl/operator/lowcode/forecast/model/automlx.py
@@ -142,17 +142,20 @@ def _build_model(self) -> pd.DataFrame:
                 )
 
                 if self.loaded_models is not None and s_id in self.loaded_models:
-                    model = self.loaded_models[s_id]
-                else:
-                    model = Pipeline(
-                        task="forecasting",
-                        **model_kwargs,
-                    )
-                    model.fit(
-                        X=data_i.drop(target, axis=1),
-                        y=data_i[[target]],
-                        time_budget=time_budget,
-                    )
+                    model = self.loaded_models[s_id]["model"]
+                    model_kwargs["model_list"] = [model.selected_model_]
+                    model_kwargs["search_space"]={}
+                    model_kwargs["search_space"][model.selected_model_] = model.selected_model_params_
+
+                model = Pipeline(
+                    task="forecasting",
+                    **model_kwargs,
+                )
+                model.fit(
+                    X=data_i.drop(target, axis=1),
+                    y=data_i[[target]],
+                    time_budget=time_budget,
+                )
                 logger.debug(f"Selected model: {model.selected_model_}")
                 logger.debug(f"Selected model params: {model.selected_model_params_}")
                 summary_frame = model.forecast(
diff --git a/ads/opctl/operator/lowcode/forecast/model/prophet.py b/ads/opctl/operator/lowcode/forecast/model/prophet.py
@@ -9,6 +9,7 @@
 import matplotlib as mpl
 import numpy as np
 import optuna
+import inspect
 import pandas as pd
 from joblib import Parallel, delayed
 
@@ -39,6 +40,22 @@ def _add_unit(num, unit):
     return f"{num} {unit}"
 
 
+def _extract_parameter(model):
+    """
+    extract Prophet initialization parameters
+    """
+    from prophet import Prophet
+    sig = inspect.signature(Prophet.__init__)
+    param_names = list(sig.parameters.keys())
+    params = {}
+    for name in param_names:
+        if hasattr(model, name):
+            value = getattr(model, name)
+            if isinstance(value, (int, float, str, bool, type(None), dict, list)):
+                params[name] = value
+    return params
+
+
 def _fit_model(data, params, additional_regressors):
     from prophet import Prophet
 
@@ -96,16 +113,17 @@ def _train_model(self, i, series_id, df, model_kwargs):
             data = self.preprocess(df, series_id)
             data_i = self.drop_horizon(data)
             if self.loaded_models is not None and series_id in self.loaded_models:
-                model = self.loaded_models[series_id]
+                previous_model = self.loaded_models[series_id]["model"]
+                model_kwargs.update(_extract_parameter(previous_model))
             else:
                 if self.perform_tuning:
                     model_kwargs = self.run_tuning(data_i, model_kwargs)
 
-                model = _fit_model(
-                    data=data,
-                    params=model_kwargs,
-                    additional_regressors=self.additional_regressors,
-                )
+            model = _fit_model(
+                data=data,
+                params=model_kwargs,
+                additional_regressors=self.additional_regressors,
+            )
 
             # Get future df for prediction
             future = data.drop("y", axis=1)
diff --git a/docs/source/release_notes.rst b/docs/source/release_notes.rst
@@ -2,6 +2,13 @@
 Release Notes
 =============
 
+2.13.21
+-------
+Release date: Oct 23, 2025
+* Support editing multi-model deployment.
+* AI Quick Actions fixes and enhancements.
+
+
 2.13.20
 -------
 Release date: Sep 29, 2025
diff --git a/pyproject.toml b/pyproject.toml
@@ -21,7 +21,7 @@ build-backend = "flit_core.buildapi"
 
 # Required
 name = "oracle_ads" # the install (PyPI) name; name for local build in [tool.flit.module] section below
-version = "2.13.20"
+version = "2.13.21"
 
 # Optional
 description = "Oracle Accelerated Data Science SDK"
@@ -181,7 +181,8 @@ forecast = [
   "oci-cli",
   "optuna",
   "pmdarima",
-  "prophet",
+  "prophet==1.1.7",
+  "cmdstanpy==1.2.5",
   "shap",
   "sktime",
   "statsmodels",
@@ -229,7 +230,8 @@ aqua = [
   "cachetools",
   "huggingface_hub",
   "python-dotenv",
-  "rich"
+  "rich",
+  "openai==1.109.1"
 ]
 
 # To reduce backtracking (decrese deps install time) during test/dev env setup reducing number of versions pip is
diff --git a/tests/unitary/with_extras/aqua/test_model.py b/tests/unitary/with_extras/aqua/test_model.py
@@ -1713,3 +1713,44 @@ def test_build_search_text(self, description, tags, expected_output):
             self.app._build_search_text(tags=tags, description=description)
             == expected_output
         )
+
+    @pytest.mark.parametrize(
+        "remove_indices, expected_len",
+        [
+            ([], 2),  # All models have AQUA_TAG -> include both
+            ([1], 1),  # Second model missing AQUA_TAG -> include first only
+            ([0, 1], 0),  # Both missing AQUA_TAG -> include none
+        ],
+    )
+    @patch.object(AquaApp, "get_container_config")
+    def test_list_service_models_filters_missing_aqua_tag(
+        self,
+        mock_get_container_config,
+        remove_indices,
+        expected_len,
+    ):
+        """Ensure list() excludes models that do not have AQUA_TAG in freeform_tags."""
+        mock_get_container_config.return_value = get_container_config()
+
+        import copy
+
+        items = copy.deepcopy(TestDataset.model_summary_objects)
+        for idx in remove_indices:
+            # remove AQUA tag entirely to validate filter behavior
+            items[idx]["freeform_tags"].pop("OCI_AQUA", None)
+
+        self.app.list_resource = MagicMock(
+            return_value=[
+                oci.data_science.models.ModelSummary(**item) for item in items
+            ]
+        )
+
+        # Clear service models cache
+        self.app.clear_model_list_cache()
+
+        results = self.app.list(
+            compartment_id=TestDataset.SERVICE_COMPARTMENT_ID,
+            category=ads.config.SERVICE,
+        )
+
+        assert len(results) == expected_len
diff --git a/tests/unitary/with_extras/model/test_model_framework_pytorch_model.py b/tests/unitary/with_extras/model/test_model_framework_pytorch_model.py

Original file line number	Diff line number	Diff line change
`@@ -362,6 +362,8 @@ def _init_backend_config(`
`362`	`362`	`if operator_info.conda_type == PACK_TYPE.SERVICE`
`363`	`363`	`else operator_info.conda_prefix,`
`364`	`364`	`"freeform_tags": freeform_tags,`
	`365`	`+ "python_version": operator_info.python_version,`
	`366`	`+ "prefix": operator_info.prefix,`
`365`	`367`	`}`
`366`	`368`	`},`
`367`	`369`	`{`