Spaces:
Running
on
CPU Upgrade
Running
on
CPU Upgrade
add sync code in cli
Browse files- cli/sync-open-llm-cli.py +91 -0
- src/backend/envs.py +1 -0
cli/sync-open-llm-cli.py
ADDED
@@ -0,0 +1,91 @@
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1 |
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import os
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import json
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import glob
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from tqdm import tqdm
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from huggingface_hub import HfApi, snapshot_download
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from src.backend.manage_requests import EvalRequest
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from src.backend.envs import EVAL_REQUESTS_PATH_BACKEND_SYNC
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from src.envs import QUEUE_REPO, API
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from src.envs import EVAL_REQUESTS_PATH_OPEN_LLM, QUEUE_REPO_OPEN_LLM
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from src.utils import my_snapshot_download
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def my_set_eval_request(api, json_filepath, hf_repo, local_dir):
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for i in range(10):
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try:
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set_eval_request(api=api, json_filepath=json_filepath, hf_repo=hf_repo, local_dir=local_dir)
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return
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except Exception:
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time.sleep(60)
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return
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def set_eval_request(api: HfApi, json_filepath: str, hf_repo: str, local_dir: str):
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"""Updates a given eval request with its new status on the hub (running, completed, failed, ...)"""
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with open(json_filepath) as fp:
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data = json.load(fp)
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with open(json_filepath, "w") as f:
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f.write(json.dumps(data))
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api.upload_file(path_or_fileobj=json_filepath, path_in_repo=json_filepath.replace(local_dir, ""),
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repo_id=hf_repo, repo_type="dataset")
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def get_request_file_for_model(data, requests_path):
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model_name = data["model"]
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precision = data["precision"]
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"""Selects the correct request file for a given model. Only keeps runs tagged as FINISHED and RUNNING"""
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request_files = os.path.join(
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requests_path,
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f"{model_name}_eval_request_*.json",
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)
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request_files = glob.glob(request_files)
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# Select correct request file (precision)
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request_file = ""
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request_files = sorted(request_files, reverse=True)
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for tmp_request_file in request_files:
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with open(tmp_request_file, "r") as f:
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req_content = json.load(f)
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if req_content["precision"] == precision.split(".")[-1]:
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request_file = tmp_request_file
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return request_file
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def update_model_type(data, requests_path):
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open_llm_request_file = get_request_file_for_model(data, requests_path)
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try:
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with open(open_llm_request_file, "r") as f:
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open_llm_request = json.load(f)
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data["model_type"] = open_llm_request["model_type"]
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return True, data
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except:
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return False, data
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def read_and_write_json_files(directory, requests_path_open_llm):
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# Walk through the directory
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for subdir, dirs, files in tqdm(os.walk(directory), desc="updating model type according to open llm leaderboard"):
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for file in files:
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# Check if the file is a JSON file
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if file.endswith('.json'):
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file_path = os.path.join(subdir, file)
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# Open and read the JSON file
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with open(file_path, 'r') as json_file:
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data = json.load(json_file)
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sucess, data = update_model_type(data, requests_path_open_llm)
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if sucess:
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with open(file_path, 'w') as json_file:
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json.dump(data, json_file)
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my_set_eval_request(api=API, json_filepath=file_path, hf_repo=QUEUE_REPO, local_dir=EVAL_REQUESTS_PATH_BACKEND_SYNC)
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if __name__ == "__main__":
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my_snapshot_download(repo_id=QUEUE_REPO_OPEN_LLM, revision="main", local_dir=EVAL_REQUESTS_PATH_OPEN_LLM, repo_type="dataset", max_workers=60)
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my_snapshot_download(repo_id=QUEUE_REPO, revision="main", local_dir=EVAL_REQUESTS_PATH_BACKEND_SYNC, repo_type="dataset", max_workers=60)
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read_and_write_json_files(EVAL_REQUESTS_PATH_BACKEND_SYNC, EVAL_REQUESTS_PATH_OPEN_LLM)
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src/backend/envs.py
CHANGED
@@ -59,6 +59,7 @@ class Tasks(Enum):
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EVAL_REQUESTS_PATH_BACKEND = os.path.join(CACHE_PATH, "eval-queue-bk")
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EVAL_RESULTS_PATH_BACKEND = os.path.join(CACHE_PATH, "eval-results-bk")
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DEVICE = "cuda" if torch.cuda.is_available() else 'cpu'
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EVAL_REQUESTS_PATH_BACKEND = os.path.join(CACHE_PATH, "eval-queue-bk")
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EVAL_REQUESTS_PATH_BACKEND_SYNC = os.path.join(CACHE_PATH, "eval-queue-bk-sync")
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EVAL_RESULTS_PATH_BACKEND = os.path.join(CACHE_PATH, "eval-results-bk")
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DEVICE = "cuda" if torch.cuda.is_available() else 'cpu'
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