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backend / main_backend_lighteval.py

derek-thomas HF staff

Adding logging visualizer

a130fc3 about 2 months ago

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	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
	from src.about import 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"

	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
	)


	if __name__ == "__main__":
	run_auto_eval()