2

main_backend.py

@@ -20,12 +20,11 @@ RUNNING_STATUS = "RUNNING"
 FINISHED_STATUS = "FINISHED"
 FAILED_STATUS = "FAILED"
 # import os
-# os.environ["HF_ENDPOINT"] = "https://hf-mirror.com"
-# snapshot_download(repo_id=envs.RESULTS_REPO, revision="main",
-#                 local_dir=envs.EVAL_RESULTS_PATH_BACKEND, repo_type="dataset", max_workers=60)
+snapshot_download(repo_id=envs.RESULTS_REPO, revision="main",
+                local_dir=envs.EVAL_RESULTS_PATH_BACKEND, repo_type="dataset", max_workers=60)
 
-# snapshot_download(repo_id=envs.QUEUE_REPO, revision="main",
-#                 local_dir=envs.EVAL_REQUESTS_PATH_BACKEND, repo_type="dataset", max_workers=60)
+snapshot_download(repo_id=envs.QUEUE_REPO, revision="main",
+                local_dir=envs.EVAL_REQUESTS_PATH_BACKEND, repo_type="dataset", max_workers=60)
 # exit()
 
 def run_auto_eval(args):
@@ -112,7 +111,7 @@ def main():
     parser = argparse.ArgumentParser(description="Run auto evaluation with optional reproducibility feature")
 
     # Optional arguments
-    parser.add_argument("--reproduce", type=bool, default=False, help="Reproduce the evaluation results")
+    parser.add_argument("--reproduce", type=bool, default=True, help="Reproduce the evaluation results")
     parser.add_argument("--model", type=str, default=None, help="Your Model ID")
     parser.add_argument("--precision", type=str, default="float16", help="Precision of your model")
     parser.add_argument("--publish", type=bool, default=False, help="whether directly publish the evaluation results on HF")