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| import os | |
| from pathlib import Path | |
| import typer | |
| from datasets import load_dataset | |
| from dotenv import load_dotenv | |
| from rich import print | |
| from utils import http_get, http_post | |
| if Path(".env").is_file(): | |
| load_dotenv(".env") | |
| HF_TOKEN = os.getenv("HF_TOKEN") | |
| AUTOTRAIN_TOKEN = os.getenv("AUTOTRAIN_TOKEN") | |
| AUTOTRAIN_USERNAME = os.getenv("AUTOTRAIN_USERNAME") | |
| AUTOTRAIN_BACKEND_API = os.getenv("AUTOTRAIN_BACKEND_API") | |
| if "staging" in AUTOTRAIN_BACKEND_API: | |
| AUTOTRAIN_ENV = "staging" | |
| else: | |
| AUTOTRAIN_ENV = "prod" | |
| def main(): | |
| logs_df = load_dataset("autoevaluate/evaluation-job-logs", use_auth_token=True, split="train").to_pandas() | |
| # Filter out | |
| # - legacy AutoTrain submissions prior to project approvals was implemented | |
| # - submissions for appropriate AutoTrain environment (staging vs prod) | |
| projects_df = logs_df.copy()[ | |
| (~logs_df["project_id"].isnull()) & (logs_df.query(f"autotrain_env == '{AUTOTRAIN_ENV}'")) | |
| ] | |
| projects_to_approve = projects_df["project_id"].astype(int).tolist() | |
| for project_id in projects_to_approve: | |
| try: | |
| project_info = http_get( | |
| path=f"/projects/{project_id}", | |
| token=HF_TOKEN, | |
| domain=AUTOTRAIN_BACKEND_API, | |
| ).json() | |
| if project_info["status"] == 3 and project_info["training_status"] == "not_started": | |
| train_job_resp = http_post( | |
| path=f"/projects/{project_id}/start_training", | |
| token=HF_TOKEN, | |
| domain=AUTOTRAIN_BACKEND_API, | |
| ).json() | |
| print(f"🏃♂️ Project {project_id} approval response: {train_job_resp}") | |
| except: | |
| print(f"There was a problem obtaining the project info for project ID {project_id}") | |
| pass | |
| if __name__ == "__main__": | |
| typer.run(main) | |