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"nbformat": 4,
"nbformat_minor": 4,
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"name": "python",
"version": "3.10.0"
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# `train_grpo_smoke.ipynb` — syntax & environment smoke test\n",
"\n",
"Companion to `train_grpo.ipynb`. **Fast** (~1–2 min): checks imports, repo layout, `TASK_HORIZON`, and one short env run.\n",
"\n",
"Run **all cells top to bottom** in Colab or locally before starting the full training notebook."
]
},
{
"cell_type": "code",
"metadata": {},
"execution_count": null,
"outputs": [],
"source": [
"# Cell 1: Minimal deps (quoted versions for zsh / shell safety)\n",
"!pip install -q pydantic httpx\n",
"!pip install -q \"openenv-core[core]>=0.2.2\""
]
},
{
"cell_type": "code",
"metadata": {},
"execution_count": null,
"outputs": [],
"source": [
"# Cell 2: Repo path (same logic as main notebook)\n",
"import os\n",
"import sys\n",
"import shutil\n",
"import subprocess\n",
"from pathlib import Path\n",
"\n",
"REPO_BRANCH = \"main\"\n",
"REPO_URL = \"https://github.com/VaibhavKhandare/viral-posts-env.git\"\n",
"COLAB_REPO = Path(\"/content/viral-posts-env\")\n",
"\n",
"\n",
"def _is_repo_root(p: Path) -> bool:\n",
" return (p / \"server\" / \"viraltest_environment.py\").is_file() and (p / \"models.py\").is_file()\n",
"\n",
"\n",
"def _find_local_root() -> Path:\n",
" here = Path.cwd().resolve()\n",
" for cand in (here, here.parent, here.parent.parent):\n",
" if _is_repo_root(cand):\n",
" return cand\n",
" raise FileNotFoundError(\n",
" \"Could not find project root. cd into viral-posts-env or use Colab.\"\n",
" )\n",
"\n",
"\n",
"if Path(\"/content\").is_dir():\n",
" if COLAB_REPO.exists():\n",
" shutil.rmtree(COLAB_REPO, ignore_errors=True)\n",
" p = subprocess.run(\n",
" [\"git\", \"clone\", \"--branch\", REPO_BRANCH, \"--depth\", \"1\", REPO_URL, str(COLAB_REPO)],\n",
" capture_output=True,\n",
" text=True,\n",
" )\n",
" if p.returncode != 0:\n",
" raise RuntimeError(f\"git clone failed:\\n{p.stderr}\")\n",
" os.chdir(COLAB_REPO)\n",
" print(\"Mode: Colab\")\n",
"else:\n",
" os.chdir(_find_local_root())\n",
" print(\"Mode: local\")\n",
"\n",
"REPO_DIR = str(Path.cwd().resolve())\n",
"if REPO_DIR not in sys.path:\n",
" sys.path.insert(0, REPO_DIR)\n",
"print(\"REPO_DIR =\", REPO_DIR)"
]
},
{
"cell_type": "code",
"metadata": {},
"execution_count": null,
"outputs": [],
"source": [
"# Cell 3: Core imports + TASK_HORIZON check\n",
"import os\n",
"import sys\n",
"from pathlib import Path\n",
"\n",
"if not Path(\"server/viraltest_environment.py\").is_file():\n",
" for cand in (Path.cwd(), Path.cwd().parent, Path.cwd().parent.parent):\n",
" if (cand / \"server\" / \"viraltest_environment.py\").is_file():\n",
" os.chdir(cand)\n",
" s = str(cand.resolve())\n",
" if s not in sys.path:\n",
" sys.path.insert(0, s)\n",
" print(\"Auto chdir:\", s)\n",
" break\n",
" else:\n",
" raise RuntimeError(\"Run Cell 2 first or open from repo root.\")\n",
"\n",
"from models import ScheduledAction, ToolCall, ViraltestAction\n",
"from server.viraltest_environment import (\n",
" ViraltestEnvironment,\n",
" TAG_POOL,\n",
" TASK_HORIZON,\n",
" TOPIC_CATEGORIES,\n",
")\n",
"\n",
"assert TASK_HORIZON == 30, f\"Expected TASK_HORIZON=30, got {TASK_HORIZON}\"\n",
"print(\"OK: TASK_HORIZON =\", TASK_HORIZON)\n",
"print(\"OK: tags =\", len(TAG_POOL), \"niches =\", len(TOPIC_CATEGORIES))"
]
},
{
"cell_type": "code",
"metadata": {},
"execution_count": null,
"outputs": [],
"source": [
"# Cell 4: One minimal episode (syntax + env wiring)\n",
"import random\n",
"\n",
"_rng = random.Random(42)\n",
"\n",
"\n",
"def plan_minimal(obs_dict, day):\n",
" topics = [t for topics in TOPIC_CATEGORIES.values() for t in topics]\n",
" topic = topics[day % len(topics)]\n",
" tags = [TAG_POOL[i % len(TAG_POOL)] for i in range(day, day + 3)]\n",
" return ViraltestAction(\n",
" scheduled_actions=[\n",
" ScheduledAction(\n",
" hour=12,\n",
" action_type=\"post\",\n",
" content_type=\"carousel\",\n",
" topic=topic,\n",
" tags=tags,\n",
" intent=\"save_bait\",\n",
" )\n",
" ]\n",
" )\n",
"\n",
"\n",
"def run_episode(task, plan_fn, seed=42):\n",
" env = ViraltestEnvironment()\n",
" obs = env.reset(task=task, seed=seed)\n",
" obs_dict = obs.model_dump()\n",
" rewards = []\n",
" for day in range(1, TASK_HORIZON + 1):\n",
" obs = env.step(plan_fn(obs_dict, day))\n",
" obs_dict = obs.model_dump()\n",
" rewards.append(obs.reward or 0.0)\n",
" if obs.done:\n",
" break\n",
" gs = (obs.metadata or {}).get(\"grader_score\", 0.0)\n",
" return {\"steps\": len(rewards), \"total_reward\": sum(rewards), \"grader_score\": gs}\n",
"\n",
"\n",
"r = run_episode(\"monthly_engage\", plan_minimal, seed=42)\n",
"print(\"Episode result:\", r)\n",
"assert r[\"steps\"] == TASK_HORIZON, f\"Expected {TASK_HORIZON} steps, got {r['steps']}\"\n",
"print(\"OK: full monthly episode completed\")"
]
},
{
"cell_type": "code",
"metadata": {},
"execution_count": null,
"outputs": [],
"source": [
"# Cell 5: Optional ML stack (no model download)\n",
"mods = [\n",
" \"torch\",\n",
" \"transformers\",\n",
" \"peft\",\n",
" \"trl\",\n",
" \"datasets\",\n",
" \"accelerate\",\n",
"]\n",
"for m in mods:\n",
" try:\n",
" __import__(m)\n",
" print(\"OK import:\", m)\n",
" except ImportError as e:\n",
" print(\"MISSING (install in full notebook):\", m, \"—\", e)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If all cells pass, open `train_grpo.ipynb` and run the full pipeline."
]
}
]
}
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