allow users to specify engine type for automlx

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

@@ -81,22 +81,6 @@ def _build_model(self) -> pd.DataFrame:
 
         from automlx import Pipeline, init
 
-        cpu_count = os.cpu_count()
-        try:
-            if cpu_count < 4:
-                engine = "local"
-                engine_opts = None
-            else:
-                engine = "ray"
-                engine_opts = ({"ray_setup": {"_temp_dir": "/tmp/ray-temp"}},)
-            init(
-                engine=engine,
-                engine_opts=engine_opts,
-                loglevel=logging.CRITICAL,
-            )
-        except Exception as e:
-            logger.info(f"Error. Has Ray already been initialized? Skipping. {e}")
-
         full_data_dict = self.datasets.get_data_by_series()
 
         self.models = {}
@@ -112,6 +96,26 @@ def _build_model(self) -> pd.DataFrame:
         # Clean up kwargs for pass through
         model_kwargs_cleaned, time_budget = self.set_kwargs()
 
+        cpu_count = os.cpu_count()
+        try:
+            engine_type = model_kwargs_cleaned.pop(
+                "engine", "local" if cpu_count <= 4 else "ray"
+            )
+            engine_opts = (
+                None
+                if engine_type == "local"
+                else ({"ray_setup": {"_temp_dir": "/tmp/ray-temp"}},)
+            )
+            init(
+                engine=engine_type,
+                engine_opts=engine_opts,
+                loglevel=logging.CRITICAL,
+            )
+        except Exception as e:
+            logger.info(
+                f"Error initializing automlx. Has Ray already been initialized? Skipping. {e}"
+            )
+
         for s_id, df in full_data_dict.items():
             try:
                 logger.debug(f"Running automlx on series {s_id}")

pyproject.toml

@@ -157,27 +157,26 @@ forecast = [
   "oci-cli",
   "py-cpuinfo",
   "rich",
-  "autots[additional]",
+  "autots",
   "mlforecast",
   "neuralprophet>=0.7.0",
   "numpy<2.0.0",
   "oci-cli",
   "optuna",
-  "oracle-ads",
   "pmdarima",
   "prophet",
   "shap",
   "sktime",
   "statsmodels",
   "plotly",
   "oracledb",
-  "report-creator==1.0.28",
+  "report-creator==1.0.32",
 ]
 anomaly  = [
   "oracle_ads[opctl]",
   "autots",
   "oracledb",
-  "report-creator==1.0.28",
+  "report-creator==1.0.32",
   "rrcf==0.4.4",
   "scikit-learn",
   "salesforce-merlion[all]==2.0.4"
@@ -186,7 +185,7 @@ recommender = [
   "oracle_ads[opctl]",
   "scikit-surprise",
   "plotly",
-  "report-creator==1.0.28",
+  "report-creator==1.0.32",
 ]
 feature-store-marketplace = [
     "oracle-ads[opctl]",
@@ -202,7 +201,7 @@ pii = [
   "scrubadub_spacy",
   "spacy-transformers==1.2.5",
   "spacy==3.6.1",
-  "report-creator==1.0.28",
+  "report-creator==1.0.32",
 ]
 llm = ["langchain>=0.2", "langchain-community", "langchain_openai", "pydantic>=2,<3", "evaluate>=0.4.0"]
 aqua = ["jupyter_server"]