Features/fix staging

neurons/validator.py

@@ -64,13 +64,13 @@ async def spawn_loops(task_queue, scoring_queue, reward_events):
         logger.info("Starting WeightSetter...")
         asyncio.create_task(weight_setter.start(reward_events))
 
-        # while True:
-        #     await asyncio.sleep(5)
+        while True:
+            await asyncio.sleep(5)
 
-        #     # Check if all tasks are still running
-        #     logger.debug(f"Number of tasks in Task Queue: {len(task_queue)}")
-        #     logger.debug(f"Number of tasks in Scoring Queue: {len(scoring_queue)}")
-        #     logger.debug(f"Number of tasks in Reward Events: {len(reward_events)}")
+            # Check if all tasks are still running
+            logger.debug(f"Number of tasks in Task Queue: {len(task_queue)}")
+            logger.debug(f"Number of tasks in Scoring Queue: {len(scoring_queue)}")
+            logger.debug(f"Number of tasks in Reward Events: {len(reward_events)}")
 
     asyncio.run(spawn_loops(task_queue, scoring_queue, reward_events))
 

prompting/llms/hf_llm.py

@@ -2,7 +2,6 @@
 
 import numpy as np
 import torch
-from loguru import logger
 from transformers import AutoModelForCausalLM, AutoTokenizer, PreTrainedModel, pipeline
 
 from shared.settings import shared_settings
@@ -62,14 +61,14 @@ def generate(self, messages: list[str] | list[dict], sampling_params=None, seed=
             skip_special_tokens=True,
         )[0]
 
-        logger.debug(
-            f"""{self.__class__.__name__} queried:
-            prompt: {messages}\n
-            responses: {results}\n
-            sampling params: {params}\n
-            seed: {seed}
-            """
-        )
+        # logger.debug(
+        #     f"""{self.__class__.__name__} queried:
+        #     prompt: {messages}\n
+        #     responses: {results}\n
+        #     sampling params: {params}\n
+        #     seed: {seed}
+        #     """
+        # )
 
         return results if len(results) > 1 else results[0]
 

shared/epistula.py

@@ -152,8 +152,8 @@ async def query_miners(
                 logger.error(f"Unknown response type: {response}")
                 results.append(SynapseStreamResult(uid=uid, exception=f"Unknown response type: {response}"))
         return results
-    except Exception as e:
-        logger.error(f"Error in query_miners: {e}")
+    except Exception:
+        # logger.error(f"Error in query_miners: {e}")
         return []