| import logging | |
| import pprint | |
| 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.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 | |
| TASKS_HARNESS = [task.value.benchmark for task in Tasks] | |
| logging.basicConfig(level=logging.ERROR) | |
| pp = pprint.PrettyPrinter(width=80) | |
| PENDING_STATUS = "PENDING" | |
| 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() |