import logging import pprint from huggingface_hub import snapshot_download logging.getLogger("openai").setLevel(logging.WARNING) from src.backend.run_eval_suite_lighteval 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, API, LIMIT, TOKEN, ACCELERATOR, VENDOR, REGION, TASKS_LIGHTEVAL from src.logging import setup_logger logger = setup_logger(__name__) # 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, 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) logger.info(f"Found {len(eval_requests)} {','.join(current_pending_status)} eval requests") if len(eval_requests) == 0: return eval_request = eval_requests[0] logger.info(pp.pformat(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, ) # This needs to be done #instance_size, instance_type = get_instance_for_model(eval_request) # For GPU # instance_size, instance_type = "small", "g4dn.xlarge" # For CPU instance_size, instance_type = "medium", "c6i" logger.info(f'Starting Evaluation of {eval_request.json_filepath} on Inference endpoints: {instance_size} {instance_type}') run_evaluation( eval_request=eval_request, task_names=TASKS_LIGHTEVAL, local_dir=EVAL_RESULTS_PATH_BACKEND, batch_size=1, accelerator=ACCELERATOR, region=REGION, vendor=VENDOR, instance_size=instance_size, instance_type=instance_type, limit=LIMIT ) logger.info(f'Completed Evaluation of {eval_request.json_filepath} on Inference endpoints: {instance_size} {instance_type}') if __name__ == "__main__": run_auto_eval()