main_backend.py

import logging
import pprint

from huggingface_hub import snapshot_download

import src.backend.run_eval_suite as run_eval_suite
import src.backend.manage_requests as manage_requests
import src.backend.sort_queue as sort_queue
import src.envs as envs

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=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)


def run_auto_eval():
    current_pending_status = [PENDING_STATUS]

    manage_requests.check_completed_evals(
        api=envs.API,
        checked_status=RUNNING_STATUS,
        completed_status=FINISHED_STATUS,
        failed_status=FAILED_STATUS,
        hf_repo=envs.QUEUE_REPO,
        local_dir=envs.EVAL_REQUESTS_PATH_BACKEND,
        hf_repo_results=envs.RESULTS_REPO,
        local_dir_results=envs.EVAL_RESULTS_PATH_BACKEND
    )
    logging.info("Checked completed evals")
    eval_requests = manage_requests.get_eval_requests(job_status=current_pending_status,
                                                    hf_repo=envs.QUEUE_REPO,
                                                    local_dir=envs.EVAL_REQUESTS_PATH_BACKEND)
    logging.info("Got eval requests")
    eval_requests = sort_queue.sort_models_by_priority(api=envs.API, models=eval_requests)
    logging.info("Sorted eval requests")

    print(f"Found {len(eval_requests)} {','.join(current_pending_status)} eval requests")

    if len(eval_requests) == 0:
        print("No eval requests found. Exiting.")
        return

    eval_request = eval_requests[0]
    pp.pprint(eval_request)

    manage_requests.set_eval_request(
        api=envs.API,
        eval_request=eval_request,
        new_status=RUNNING_STATUS,
        hf_repo=envs.QUEUE_REPO,
        local_dir=envs.EVAL_REQUESTS_PATH_BACKEND
    )
    logging.info("Set eval request to running, now running eval")

    run_eval_suite.run_evaluation(
        eval_request=eval_request,
        local_dir=envs.EVAL_RESULTS_PATH_BACKEND,
        results_repo=envs.RESULTS_REPO,
        batch_size=1,
        device=envs.DEVICE,
        no_cache=True,
    )
    logging.info("Eval finished, now setting status to finished")


if __name__ == "__main__":
    run_auto_eval()