import os import json from datetime import datetime from huggingface_hub import snapshot_download 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.envs import Tasks, EVAL_REQUESTS_PATH_BACKEND,EVAL_RESULTS_PATH_BACKEND, DEVICE, LIMIT from src.envs import QUEUE_REPO, RESULTS_REPO, API import logging import pprint # TASKS_HARNESS = [task.value.benchmark for task in Tasks] logging.getLogger("openai").setLevel(logging.WARNING) logging.basicConfig(level=logging.ERROR) pp = pprint.PrettyPrinter(width=80) PENDING_STATUS = "PENDING" RUNNING_STATUS = "RUNNING" FINISHED_STATUS = "FINISHED" FAILED_STATUS = "FAILED" snapshot_download(repo_id=RESULTS_REPO, revision="main", local_dir=EVAL_RESULTS_PATH_BACKEND, repo_type="dataset", max_workers=60) snapshot_download(repo_id=QUEUE_REPO, revision="main", local_dir=EVAL_REQUESTS_PATH_BACKEND, repo_type="dataset", max_workers=60) 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) # results = run_evaluation(eval_request=eval_request, task_names=TASKS_HARNESS, num_fewshot=NUM_FEWSHOT, # batch_size=1, device=DEVICE, no_cache=True, limit=LIMIT) TASKS_HARNESS = [task.value for task in Tasks] for task in TASKS_HARNESS: results = run_evaluation(eval_request=eval_request, task_names=[task.benchmark], num_fewshot=task.num_fewshot, batch_size=1, device=DEVICE, no_cache=True, limit=LIMIT) dumped = json.dumps(results, indent=2) print(dumped) output_path = os.path.join(EVAL_RESULTS_PATH_BACKEND, *eval_request.model.split("/"), f"results_{datetime.now()}.json") os.makedirs(os.path.dirname(output_path), exist_ok=True) with open(output_path, "w") as f: f.write(dumped) API.upload_file(path_or_fileobj=output_path, path_in_repo=f"{eval_request.model}/results_{datetime.now()}.json", repo_id=RESULTS_REPO, repo_type="dataset") set_eval_request(api=API, eval_request=eval_request, set_to_status=FINISHED_STATUS, hf_repo=QUEUE_REPO, local_dir=EVAL_REQUESTS_PATH_BACKEND) # breakpoint() if __name__ == "__main__": run_auto_eval()