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import logging
import pprint
import re
from huggingface_hub import snapshot_download
logging.getLogger("openai").setLevel(logging.DEBUG)
from src.backend.inference_endpoint import create_endpoint
from src.backend.run_toxicity_eval import main
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, TOKEN)
#, LIMIT, ACCELERATOR, VENDOR, REGION
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,
)
logger.info(f'Starting Evaluation of {eval_request.json_filepath} on Inference endpoints')
model_repository = eval_request.model
endpoint_name = re.sub("/", "-", model_repository.lower()) + "-toxicity-eval"
endpoint_url = create_endpoint(endpoint_name, model_repository)
logger.info("Created an endpoint url at %s" % endpoint_url)
results = main(endpoint_url, model_repository)
logger.debug("FINISHED!")
#local_dir = EVAL_RESULTS_PATH_BACKEND,
#limit=LIMIT
# )
logger.info(f'Completed Evaluation of {eval_request.json_filepath}')
if __name__ == "__main__":
run_auto_eval() |