disable unused code

app.py

@@ -34,7 +34,7 @@ from src.populate import get_evaluation_queue_df, get_leaderboard_df
 from src.submission.submit import add_new_eval
 
 
-subprocess.run(["python", "scripts/fix_harness_import.py"])
+# subprocess.run(["python", "scripts/fix_harness_import.py"])
 
 def restart_space():
     API.restart_space(repo_id=REPO_ID)

main_backend.py

@@ -5,9 +5,9 @@ from huggingface_hub import snapshot_download
 
 logging.getLogger("openai").setLevel(logging.WARNING)
 
-from src.backend.run_eval_suite import run_evaluation
-from src.backend.manage_requests import check_completed_evals, get_eval_requests, set_eval_request
-from src.backend.sort_queue import sort_models_by_priority
+# from src.backend.run_eval_suite import run_evaluation
+# from src.backend.manage_requests import check_completed_evals, get_eval_requests, set_eval_request
+# from src.backend.sort_queue import sort_models_by_priority
 
 from src.envs import QUEUE_REPO, EVAL_REQUESTS_PATH_BACKEND, RESULTS_REPO, EVAL_RESULTS_PATH_BACKEND, DEVICE, API, LIMIT, TOKEN
 from src.about import Tasks, NUM_FEWSHOT
@@ -21,58 +21,59 @@ RUNNING_STATUS = "RUNNING"
 FINISHED_STATUS = "FINISHED"
 FAILED_STATUS = "FAILED"
 
+print('Downloading results and requests.')
 snapshot_download(repo_id=RESULTS_REPO, revision="main", local_dir=EVAL_RESULTS_PATH_BACKEND, repo_type="dataset", max_workers=60, token=TOKEN)
 snapshot_download(repo_id=QUEUE_REPO, revision="main", local_dir=EVAL_REQUESTS_PATH_BACKEND, repo_type="dataset", max_workers=60, token=TOKEN)
 
-def run_auto_eval():
-    current_pending_status = [PENDING_STATUS]
-
-    # pull the eval dataset from the hub and parse any eval requests
-    # check completed evals and set them to finished
-    check_completed_evals(
-        api=API,
-        checked_status=RUNNING_STATUS,
-        completed_status=FINISHED_STATUS,
-        failed_status=FAILED_STATUS,
-        hf_repo=QUEUE_REPO,
-        local_dir=EVAL_REQUESTS_PATH_BACKEND,
-        hf_repo_results=RESULTS_REPO,
-        local_dir_results=EVAL_RESULTS_PATH_BACKEND
-    )
-
-    # Get all eval request that are PENDING, if you want to run other evals, change this parameter
-    eval_requests = get_eval_requests(job_status=current_pending_status, hf_repo=QUEUE_REPO, local_dir=EVAL_REQUESTS_PATH_BACKEND)
-    # Sort the evals by priority (first submitted first run)
-    eval_requests = sort_models_by_priority(api=API, models=eval_requests)
-
-    print(f"Found {len(eval_requests)} {','.join(current_pending_status)} eval requests")
-
-    if len(eval_requests) == 0:
-        return
-
-    eval_request = eval_requests[0]
-    pp.pprint(eval_request)
-
-    set_eval_request(
-        api=API,
-        eval_request=eval_request,
-        set_to_status=RUNNING_STATUS,
-        hf_repo=QUEUE_REPO,
-        local_dir=EVAL_REQUESTS_PATH_BACKEND,
-    )
-
-    run_evaluation(
-        eval_request=eval_request, 
-        task_names=TASKS_HARNESS, 
-        num_fewshot=NUM_FEWSHOT, 
-        local_dir=EVAL_RESULTS_PATH_BACKEND,
-        results_repo=RESULTS_REPO,
-        batch_size=1, 
-        device=DEVICE, 
-        no_cache=True, 
-        limit=LIMIT
-        )
-
-
-if __name__ == "__main__":
-    run_auto_eval()
+# def run_auto_eval():
+#     current_pending_status = [PENDING_STATUS]
+#
+#     # pull the eval dataset from the hub and parse any eval requests
+#     # check completed evals and set them to finished
+#     check_completed_evals(
+#         api=API,
+#         checked_status=RUNNING_STATUS,
+#         completed_status=FINISHED_STATUS,
+#         failed_status=FAILED_STATUS,
+#         hf_repo=QUEUE_REPO,
+#         local_dir=EVAL_REQUESTS_PATH_BACKEND,
+#         hf_repo_results=RESULTS_REPO,
+#         local_dir_results=EVAL_RESULTS_PATH_BACKEND
+#     )
+#
+#     # Get all eval request that are PENDING, if you want to run other evals, change this parameter
+#     eval_requests = get_eval_requests(job_status=current_pending_status, hf_repo=QUEUE_REPO, local_dir=EVAL_REQUESTS_PATH_BACKEND)
+#     # Sort the evals by priority (first submitted first run)
+#     eval_requests = sort_models_by_priority(api=API, models=eval_requests)
+#
+#     print(f"Found {len(eval_requests)} {','.join(current_pending_status)} eval requests")
+#
+#     if len(eval_requests) == 0:
+#         return
+#
+#     eval_request = eval_requests[0]
+#     pp.pprint(eval_request)
+#
+#     set_eval_request(
+#         api=API,
+#         eval_request=eval_request,
+#         set_to_status=RUNNING_STATUS,
+#         hf_repo=QUEUE_REPO,
+#         local_dir=EVAL_REQUESTS_PATH_BACKEND,
+#     )
+#
+#     run_evaluation(
+#         eval_request=eval_request,
+#         task_names=TASKS_HARNESS,
+#         num_fewshot=NUM_FEWSHOT,
+#         local_dir=EVAL_RESULTS_PATH_BACKEND,
+#         results_repo=RESULTS_REPO,
+#         batch_size=1,
+#         device=DEVICE,
+#         no_cache=True,
+#         limit=LIMIT
+#         )
+#
+#
+# if __name__ == "__main__":
+#     run_auto_eval()