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Sleeping
| """ | |
| Inference script with heuristic policy for hackathon submission compliance. | |
| This script runs environment episodes with strict logging format for validator compliance. | |
| It uses a simple heuristic policy based on CPU thresholds and outputs only [START], [STEP], | |
| and [END] logs to stdout. | |
| """ | |
| import os | |
| import asyncio | |
| from env.environment import CloudResourceEnv | |
| from env.async_wrapper import AsyncEnvWrapper | |
| # Environment variables | |
| API_BASE_URL = os.getenv("API_BASE_URL", "http://localhost:8000") | |
| MODEL_NAME = os.getenv("MODEL_NAME", "gpt-4") | |
| HF_TOKEN = os.getenv("HF_TOKEN", "") | |
| TASK_NAME = os.getenv("TASK_NAME", "medium") | |
| BENCHMARK = os.getenv("BENCHMARK", "cloud_resource_env") | |
| # Constants | |
| MAX_STEPS = 20 | |
| SUCCESS_SCORE_THRESHOLD = 0.5 | |
| def heuristic_policy(state) -> int: | |
| """ | |
| Simple CPU threshold-based policy. | |
| Decision rules: | |
| - CPU > 70%: Increase resources (action 2) | |
| - CPU < 40%: Decrease resources (action 0) | |
| - 40% <= CPU <= 70%: Maintain resources (action 1) | |
| Args: | |
| state: Environment state with cpu attribute | |
| Returns: | |
| Action: 0 (decrease), 1 (maintain), or 2 (increase) | |
| """ | |
| cpu = state.cpu | |
| if cpu > 70.0: | |
| return 2 # increase | |
| elif cpu < 40.0: | |
| return 0 # decrease | |
| else: | |
| return 1 # maintain | |
| async def main(): | |
| """ | |
| Main execution function. | |
| Runs one episode with strict logging format: | |
| - [START] log at episode beginning | |
| - [STEP] log for each step | |
| - [END] log at episode completion | |
| """ | |
| # Initialize OpenAI client (structure for future use) | |
| # Only initialize if HF_TOKEN is provided | |
| client = None | |
| if HF_TOKEN: | |
| try: | |
| from openai import OpenAI | |
| client = OpenAI(api_key=HF_TOKEN, base_url=API_BASE_URL) | |
| except ImportError: | |
| # OpenAI package not installed, continue without it | |
| pass | |
| # Create environment with async wrapper | |
| env = CloudResourceEnv(task_name=TASK_NAME) | |
| async_env = AsyncEnvWrapper(env) | |
| # Log [START] | |
| print(f"[START] task={TASK_NAME} env={BENCHMARK} model={MODEL_NAME}") | |
| # Run episode | |
| state = await async_env.reset() | |
| rewards = [] | |
| for step in range(MAX_STEPS): | |
| # Get action from heuristic policy | |
| action = heuristic_policy(env.state()) | |
| # Execute step | |
| try: | |
| obs, reward, done, info = await async_env.step(action) | |
| rewards.append(reward) | |
| # Log [STEP] | |
| print(f"[STEP] step={step+1} action={action} reward={reward:.2f} done={done} error=None") | |
| if done: | |
| break | |
| except Exception as e: | |
| # Log error | |
| print(f"[STEP] step={step+1} action={action} reward=0.00 done=True error={str(e)}") | |
| break | |
| # Calculate success | |
| avg_reward = sum(rewards) / len(rewards) if rewards else 0.0 | |
| success = avg_reward >= SUCCESS_SCORE_THRESHOLD | |
| # Log [END] | |
| rewards_str = ",".join([f"{r:.2f}" for r in rewards]) | |
| print(f"[END] success={success} steps={len(rewards)} score={avg_reward:.2f} rewards={rewards_str}") | |
| await async_env.close() | |
| if __name__ == "__main__": | |
| asyncio.run(main()) | |