minor fixes in original RAG training (#12395)

examples/research_projects/rag/callbacks_rag.py

@@ -36,7 +36,7 @@ def get_checkpoint_callback(output_dir, metric):
         dirpath=output_dir,
         filename=exp,
         monitor=f"val_{metric}",
-        mode="min",
+        mode="max",
         save_top_k=3,
         period=1,  # maybe save a checkpoint every time val is run, not just end of epoch.
     )

examples/research_projects/rag/finetune_rag.py

@@ -532,8 +532,8 @@ def main(args=None, model=None) -> GenerativeQAModule:
             raise
 
         # Create Ray actors only for rank 0.
-        if ("LOCAL_RANK" not in os.environ or os.environ["LOCAL_RANK"] == 0) and (
-            "NODE_RANK" not in os.environ or os.environ["NODE_RANK"] == 0
+        if ("LOCAL_RANK" not in os.environ or int(os.environ["LOCAL_RANK"]) == 0) and (
+            "NODE_RANK" not in os.environ or int(os.environ["NODE_RANK"]) == 0
         ):
             remote_cls = ray.remote(RayRetriever)
             named_actors = [