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Inference Script — GridOps Microgrid Environment
===================================
MANDATORY
- Before submitting, ensure the following variables are defined in your environment configuration:
API_BASE_URL The API endpoint for the LLM.
MODEL_NAME The model identifier to use for inference.
HF_TOKEN Your Hugging Face / API key.
- The inference script must be named `inference.py` and placed in the root directory of the project
- Participants must use OpenAI Client for all LLM calls using above variables
"""
import os
import sys
from openai import OpenAI
# ── Env vars (as required by hackathon) ──────────────────────────────────
API_BASE_URL = os.getenv("API_BASE_URL", "https://router.huggingface.co/v1")
API_KEY = os.getenv("HF_TOKEN") or os.getenv("API_KEY")
MODEL_NAME = os.getenv("MODEL_NAME", "meta-llama/Llama-3.3-70B-Instruct")
# ── Environment import (runs in-process, no server needed) ──────────────
sys.path.insert(0, os.path.dirname(__file__))
from gridops.server.environment import GridOpsEnvironment
from gridops.models import GridOpsAction
from gridops.prompting import SYSTEM_PROMPT, format_observation, parse_action
TASKS = ["task_1_normal", "task_2_heatwave", "task_3_crisis"]
MAX_STEPS = 72
TEMPERATURE = 0.1
MAX_TOKENS = 500 # higher to support reasoning models that emit thinking before JSON
def run_task(client: OpenAI, env: GridOpsEnvironment, task_id: str, seed: int = 42) -> dict:
"""Run one full episode on a task, return grade."""
obs = env.reset(seed=seed, task_id=task_id)
obs_dict = obs.model_dump()
# ── [START] structured output ──
print(f"[START] task={task_id}", flush=True)
messages = [{"role": "system", "content": SYSTEM_PROMPT}]
for step_idx in range(MAX_STEPS):
user_msg = format_observation(obs_dict)
messages.append({"role": "user", "content": user_msg})
# Keep context manageable
if len(messages) > 21:
messages = [messages[0]] + messages[-20:]
try:
completion = client.chat.completions.create(
model=MODEL_NAME,
messages=messages,
temperature=TEMPERATURE,
max_tokens=MAX_TOKENS,
)
reply = completion.choices[0].message.content or ""
except Exception as e:
reply = "{}"
messages.append({"role": "assistant", "content": reply})
action = parse_action(reply, default=GridOpsAction())
obs = env.step(action)
obs_dict = obs.model_dump()
# ── [STEP] structured output ──
reward = obs_dict.get("reward", 0.0)
print(f"[STEP] step={step_idx + 1} reward={reward:.4f}", flush=True)
if obs_dict.get("done", False):
break
grade = env.state.grade
score = grade["score"] if grade else 0.0
steps = step_idx + 1
# ── [END] structured output ──
print(f"[END] task={task_id} score={score:.4f} steps={steps}", flush=True)
return grade
def main():
client = OpenAI(base_url=API_BASE_URL, api_key=API_KEY)
env = GridOpsEnvironment()
results = {}
for task_id in TASKS:
grade = run_task(client, env, task_id)
results[task_id] = grade
# Summary
for task_id, grade in results.items():
score = grade["score"] if grade else 0.0
print(f"[SUMMARY] task={task_id} score={score:.4f}", flush=True)
if __name__ == "__main__":
main()
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