Add clean cache option to forecaster

tests/test_forecaster.py

@@ -173,3 +173,20 @@ def test_mixed_models_unique_aliases():
     # This should not raise an error
     forecaster = TimeCopilotForecaster(models=[model1, model2, model3])
     assert len(forecaster.models) == 3
+
+
+def test_clean_cache_runs_after_each_model(monkeypatch, models):
+    calls = []
+
+    monkeypatch.setattr(
+        TimeCopilotForecaster,
+        "_clean_model_cache",
+        staticmethod(lambda: calls.append("cleaned")),
+    )
+
+    df = generate_series(n_series=1, freq="D", min_length=10)
+    forecaster = TimeCopilotForecaster(models=models, clean_cache=True)
+
+    forecaster.forecast(df=df, h=2, freq="D")
+
+    assert calls == ["cleaned"] * len(models)

timecopilot/forecaster.py

@@ -47,6 +47,7 @@ def __init__(
         self,
         models: list[Forecaster],
         fallback_model: Forecaster | None = None,
+        clean_cache: bool = False,
     ):
         """
         Initialize the TimeCopilotForecaster with a list of models.
@@ -59,13 +60,17 @@ def __init__(
                 compatible signatures.
             fallback_model (Forecaster, optional):
                 Model to use as a fallback when a model fails.
+            clean_cache (bool):
+                If True, run Python garbage collection and clear the CUDA cache
+                after each model call. Useful for memory-heavy foundation models.
 
         Raises:
             ValueError: If duplicate model aliases are found in the models list.
         """
         self._validate_unique_aliases(models)
         self.models = models
         self.fallback_model = fallback_model
+        self.clean_cache = clean_cache
 
     def _validate_unique_aliases(self, models: list[Forecaster]) -> None:
         """
@@ -88,6 +93,20 @@ def _validate_unique_aliases(self, models: list[Forecaster]) -> None:
                 f"same class."
             )
 
+    @staticmethod
+    def _clean_model_cache() -> None:
+        """Release temporary Python and CUDA memory between model calls."""
+        import gc
+
+        gc.collect()
+        try:
+            import torch
+
+            if torch.cuda.is_available():
+                torch.cuda.empty_cache()
+        except ImportError:
+            pass
+
     @staticmethod
     def _is_distributed_df(df: AnyDataFrame) -> bool:
         """
@@ -155,6 +174,8 @@ def _call_models(
                     # (the initial model)
                     res_df_model = res_df_model.drop(columns=["y"])
                 res_df = res_df.merge(res_df_model, on=merge_on, how="left")
+            if self.clean_cache:
+                self._clean_model_cache()
         return res_df
 
     def _forecast_pandas(