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import logging | |
import pprint | |
from huggingface_hub import snapshot_download | |
from src.backend.run_eval_suite_harness import run_evaluation | |
from src.backend.manage_requests import ( | |
check_completed_evals, | |
get_eval_requests, | |
set_eval_request, | |
PENDING_STATUS, | |
RUNNING_STATUS, | |
FINISHED_STATUS, | |
FAILED_STATUS, | |
) | |
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.envs import TASKS_HARNESS, NUM_FEWSHOT | |
from src.logging import setup_logger | |
logging.getLogger("openai").setLevel(logging.WARNING) | |
# logging.basicConfig(level=logging.ERROR) | |
logger = setup_logger(__name__) | |
pp = pprint.PrettyPrinter(width=80) | |
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] | |
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, | |
) | |
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="auto", | |
device=DEVICE, | |
limit=LIMIT, | |
) | |
if __name__ == "__main__": | |
run_auto_eval() | |