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| import os | |
| import asyncio | |
| from dotenv import load_dotenv | |
| from openai import OpenAI | |
| from environment import CloudCostEnv | |
| load_dotenv() | |
| def log_start(task: str, env: str, model: str) -> None: | |
| print(f"[START] task={task} env={env} model={model}", flush=True) | |
| def log_step(step: int, action: str, reward: float, done: bool, error=None) -> None: | |
| error_val = error if error else "null" | |
| done_val = str(done).lower() | |
| print(f"[STEP] step={step} action={action} reward={reward:.2f} done={done_val} error={error_val}", flush=True) | |
| def log_end(success: bool, steps: int, score: float, rewards: list) -> None: | |
| rewards_str = ",".join(f"{r:.2f}" for r in rewards) | |
| print(f"[END] success={str(success).lower()} steps={steps} score={score:.3f} rewards={rewards_str}", flush=True) | |
| SYSTEM_PROMPT = "You are a Cloud Infrastructure AI. Manage the cluster by choosing actions: 0(Stay), 1(Add Server), 2(Remove), 3(Toggle Spot). Reply with ONLY the integer." | |
| async def get_action(state, client, model_name): | |
| state_data = state.model_dump_json() if hasattr(state, 'model_dump_json') else str(state) | |
| try: | |
| response = client.chat.completions.create( | |
| model=model_name, | |
| messages=[ | |
| {"role": "system", "content": SYSTEM_PROMPT}, | |
| {"role": "user", "content": f"Current State: {state_data}"} | |
| ], | |
| max_tokens=10 | |
| ) | |
| action_text = response.choices[0].message.content.strip() | |
| action_int = int(''.join(filter(str.isdigit, action_text))[0]) | |
| return action_int | |
| except Exception as e: | |
| return 0 | |
| async def run_task(task_id, difficulty, target_score, client, model_name): | |
| env = CloudCostEnv(task=difficulty) | |
| log_start(task=task_id, env="CloudEnv-v1", model=model_name) | |
| rewards = [] | |
| state = await env.reset() | |
| try: | |
| for step in range(1, 21): | |
| action_int = await get_action(state, client, model_name) | |
| state, reward, done = await env.step(action_int) | |
| log_step(step=step, action=str(action_int), reward=reward, done=done) | |
| rewards.append(reward) | |
| if done: | |
| break | |
| avg_score = sum(rewards) / len(rewards) if rewards else 0 | |
| log_end( | |
| success=(avg_score >= target_score), | |
| steps=len(rewards), | |
| score=avg_score, | |
| rewards=rewards | |
| ) | |
| except Exception as e: | |
| log_step(step=0, action="error", reward=0.0, done=True, error=str(e)) | |
| finally: | |
| await env.close() | |
| async def main(): | |
| api_base = os.getenv("API_BASE_URL") | |
| api_key = os.getenv("HF_TOKEN") | |
| model_name = os.getenv("MODEL_NAME") | |
| client = OpenAI(base_url=api_base, api_key=api_key) | |
| # Run all 3 tasks — checker needs at least 3 | |
| await run_task("daily_peaks", "easy", 0.8, client, model_name) | |
| await run_task("weekly_budget", "medium", 0.75, client, model_name) | |
| await run_task("chaos_mode", "hard", 0.7, client, model_name) | |
| if __name__ == "__main__": | |
| asyncio.run(main()) |