Decouple RFOpenAIAPIModelConfig and RFGeminiAPIModelConfig from vLLM availability

rapidfireai/automl/model_config.py

@@ -293,7 +293,8 @@ def __setattr__(self, name, value):
     RFPromptManager = None
 
 
-# Conditionally define evals model config classes only if dependencies are available
+# RFvLLMModelConfig requires vLLM (a self-hosted local inference engine).
+# vLLM is unavailable on macOS Apple Silicon and other CPU-only platforms.
 if (
     _VLLM_AVAILABLE
     and _EVALS_MODULES_AVAILABLE
@@ -357,6 +358,17 @@ def sampling_params_to_dict(self) -> dict[str, Any]:
             # This works across different vLLM versions
             return dict(vars(self.sampling_params))
 
+else:
+    RFvLLMModelConfig = None
+
+
+# RFOpenAIAPIModelConfig and RFGeminiAPIModelConfig only require the evals modules
+# (LangChainRagSpec, PromptManager, OpenAIInferenceEngine / GoogleGeminiInferenceEngine).
+# They do NOT require vLLM because OpenAI and Gemini are remote APIs and never run a
+# local inference engine. Gating them behind _VLLM_AVAILABLE makes them unusable on
+# CPU-only platforms (macOS Apple Silicon, Datahub CPU pods) for no reason.
+if _EVALS_MODULES_AVAILABLE and InferenceEngine is not None:
+
     class RFOpenAIAPIModelConfig(ModelConfig):
         """OpenAI API model configuration for evals mode."""
 
@@ -514,7 +526,6 @@ def sampling_params_to_dict(self) -> dict[str, Any]:
             return {k: v for k, v in self.model_config.items() if k not in _non_sampling_keys}
 
 else:
-    # Define placeholder classes if dependencies are not available
-    RFvLLMModelConfig = None
+    # Evals modules unavailable: API configs cannot be defined.
     RFOpenAIAPIModelConfig = None
     RFGeminiAPIModelConfig = None

rapidfireai/evals/scheduling/interactive_control.py

@@ -370,11 +370,14 @@ def _handle_clone(
         pipeline_type = edited_json.get("pipeline_type")
         if not pipeline_type:
             # If not specified in JSON, infer from parent
-            if isinstance(parent_model_config, RFvLLMModelConfig):
+            # Each isinstance() guarded because the class is None on platforms
+            # where the corresponding inference engine isn't available
+            # (e.g. RFvLLMModelConfig is None on macOS Apple Silicon).
+            if RFvLLMModelConfig is not None and isinstance(parent_model_config, RFvLLMModelConfig):
                 pipeline_type = "vllm"
-            elif isinstance(parent_model_config, RFOpenAIAPIModelConfig):
+            elif RFOpenAIAPIModelConfig is not None and isinstance(parent_model_config, RFOpenAIAPIModelConfig):
                 pipeline_type = "openai"
-            elif isinstance(parent_model_config, RFGeminiAPIModelConfig):
+            elif RFGeminiAPIModelConfig is not None and isinstance(parent_model_config, RFGeminiAPIModelConfig):
                 pipeline_type = "gemini"
             else:
                 raise ValueError("Cannot determine pipeline type from parent")
@@ -576,7 +579,11 @@ def _handle_clone(
         pipeline = pipeline_config["pipeline"]
 
         # Extract model name
-        if isinstance(model_config, (RFOpenAIAPIModelConfig, RFGeminiAPIModelConfig, RFvLLMModelConfig)):
+        # Filter out classes that are None on this platform (vLLM unavailable on macOS, etc.)
+        _model_cfg_classes = tuple(
+            c for c in (RFOpenAIAPIModelConfig, RFGeminiAPIModelConfig, RFvLLMModelConfig) if c is not None
+        )
+        if _model_cfg_classes and isinstance(model_config, _model_cfg_classes):
             model_name = model_config.model_config.get("model", "Unknown")
         elif hasattr(pipeline, "model_config") and pipeline.model_config is not None:
             model_name = pipeline.model_config.get("model", "Unknown")

rapidfireai/evals/utils/serialize.py

@@ -93,7 +93,9 @@ def extract_rag_params(rag_spec):
 
             return rag_config if rag_config else None
 
-        if isinstance(pipeline, RFvLLMModelConfig):
+        # RFvLLMModelConfig is None on platforms where vLLM cannot be installed
+        # (e.g., macOS Apple Silicon). Guard isinstance() to avoid TypeError.
+        if RFvLLMModelConfig is not None and isinstance(pipeline, RFvLLMModelConfig):
             json_config["pipeline_type"] = "vllm"
 
             # Extract model_config (dict)