Merge branch 'main' into aqua_deployment_tests

Author: VipulMascarenhas

.github/workflows/run-operators-unit-tests.yml

@@ -31,7 +31,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        python-version: ["3.8", "3.10.8"]
+        python-version: ["3.8"]
 
     steps:
       - uses: actions/checkout@v4

ads/common/serializer.py

@@ -464,7 +464,7 @@ def from_dict(
             )
 
         obj = cls(
-            **{key: obj_dict.get(key) for key in allowed_fields if key in obj_dict}
+            **{key: obj_dict.get(key) for key in allowed_fields}
         )
 
         for key, value in obj_dict.items():

ads/opctl/operator/lowcode/anomaly/model/automlx.py

@@ -26,8 +26,9 @@ class AutoMLXOperatorModel(AnomalyOperatorBaseModel):
     )
     def _build_model(self) -> pd.DataFrame:
         from automlx import init
+        import logging
         try:
-            init(engine="ray", engine_opts={"ray_setup": {"_temp_dir": "/tmp/ray-temp"}})
+            init(engine="ray", engine_opts={"ray_setup": {"_temp_dir": "/tmp/ray-temp"}}, loglevel=logging.CRITICAL)
         except Exception as e:
             logger.info("Ray already initialized")
         date_column = self.spec.datetime_column.name

ads/opctl/operator/lowcode/forecast/environment.yaml

@@ -18,3 +18,4 @@ dependencies:
       - optuna==3.1.0
       - oracle-automlx==23.4.1
       - oracle-automlx[forecasting]==23.4.1
+      - fire

ads/opctl/operator/lowcode/forecast/model/automlx.py

@@ -70,17 +70,15 @@ def preprocess(self, data, series_id=None):
     )
     def _build_model(self) -> pd.DataFrame:
         from automlx import init
-        from sktime.forecasting.model_selection import temporal_train_test_split
+        import logging
         try:
-            init(engine="ray", engine_opts={"ray_setup": {"_temp_dir": "/tmp/ray-temp"}})
+            init(engine="ray", engine_opts={"ray_setup": {"_temp_dir": "/tmp/ray-temp"}}, loglevel=logging.CRITICAL)
         except Exception as e:
             logger.info("Ray already initialized")
 
-
         full_data_dict = self.datasets.get_data_by_series()
 
         self.models = dict()
-        date_column = self.spec.datetime_column.name
         horizon = self.spec.horizon
         self.spec.confidence_interval_width = self.spec.confidence_interval_width or 0.8
         self.forecast_output = ForecastOutput(

docs/source/index.rst

@@ -79,6 +79,13 @@ Oracle Accelerated Data Science (ADS)
 
    modules
 
+.. admonition:: Introducing AI Quick Actions
+   :class: note
+
+   Deploy, Fine Tune and Evaluate Large language models such as `Mistral-7B-Instruct-v0.2 <https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2>`__, `CodeLlama-13b-Instruct-hf <https://huggingface.co/codellama/CodeLlama-13b-Instruct-hf>`__, etc with just a couple of clicks.
+
+   Visit `AI Quick Actions documentation <https://github.com/oracle-samples/oci-data-science-ai-samples/tree/main/ai-quick-actions>`__ for the latest information
+  
 .. admonition:: Oracle Accelerated Data Science (ADS)
    :class: note
 

test-requirements-operators.txt

@@ -1,5 +1,5 @@
 -r test-requirements.txt
 -e ".[forecast]"
 -e ".[feature-store-marketplace]"
-darts
+darts>=0.28
 plotly