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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
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)
print(f"Found {len(eval_requests)} {','.join(current_pending_status)} eval requests")
if len(eval_requests) == 0:
return
eval_request = eval_requests[0]
pp.pprint(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
if not eval_request or eval_request.params < 0:
raise ValueError("Couldn't detect number of params, please make sure the metadata is available")
elif eval_request.params < 4:
instance_size, instance_type = "small", "g4dn.xlarge"
elif eval_request.params < 9:
instance_size, instance_type = "medium", "g5.2xlarge"
elif eval_request.params < 24:
instance_size, instance_type = "xxlarge", "g5.12xlarge"
else:
raise ValueError("Number of params too big, can't run this model")
# 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=25,
accelerator=ACCELERATOR,
region=REGION,
vendor=VENDOR,
instance_size=instance_size,
instance_type=instance_type,
limit=LIMIT
)
if __name__ == "__main__":
run_auto_eval() |