training: integrate /train into vLLM process (no separate daemon)

Remove standalone worker.py daemon. Training now runs inside vLLM:

- train_router.py: FastAPI router patched into vLLM's build_app()
- /train served on same port as /completions, /score
- Lazy-loads HF model with vLLM weight views on first request
- HOGWILD training: no pause, weights updated in-place

The previous architecture had a separate daemon on port 8080 that
communicated with vLLM via pause/resume endpoints. This was wrong -
training should run in-process, sharing GPU memory directly.

Co-Authored-By: Proof of Concept <poc@bcachefs.org>

training/apollo_plugin/export_hook.py

@@ -59,6 +59,10 @@ def _patch_model_runner():
         result = original_load(self, *args, **kwargs)
         try:
             export_model_weights(self.model_runner.model)
+            # Set model path for training router
+            model_path = self.vllm_config.model_config.model
+            from .train_router import set_model_path
+            set_model_path(model_path)
         except Exception as e:
             print(f"[apollo] Failed to export weights: {e}")
         return result