File size: 21,891 Bytes
21c7db9 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# PolyGuard SFT + GRPO One-Run Runner\n",
"\n",
"`POLYGUARD_ONE_RUN_RUNNER`\n",
"\n",
"Run this notebook from top to bottom to execute the PolyGuard pipeline from data build through SFT baseline training, GRPO environment-reward training, artifact pull, inference validation, report/chart generation, and Hugging Face Space deployment.\n",
"\n",
"Default behavior uses Hugging Face Spaces for GPU training, not local Ollama or local GPU training. Keep `HF_TOKEN` in an environment variable or notebook secret; do not paste it into a cell output or commit it.\n",
"\n",
"Reward values are expected to remain numeric, rounded to 3 decimals, and clamped to `[0.001, 0.999]` throughout the API, reports, and charts."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 0) Configuration Notes\n",
"\n",
"The notebook is intentionally root-level in `polyguard-rl/`. If opened from Colab without the rest of the repo, the first cell clones the GitHub repo and changes into `polyguard-rl/`.\n",
"\n",
"Useful overrides:\n",
"\n",
"- `HF_TOKEN`: write token for Spaces, model artifact repos, and private artifact pulls.\n",
"- `HF_USERNAME`: target Hub namespace. If omitted, the authenticated username is used.\n",
"- `POLYGUARD_MODEL_SWEEP`: comma-separated models, default Qwen 0.5B, 1.5B, and 3B instruct.\n",
"- `POLYGUARD_SFT_EPOCHS`, `POLYGUARD_GRPO_EPOCHS`: training epochs.\n",
"- `POLYGUARD_SFT_MAX_STEPS=0`, `POLYGUARD_GRPO_MAX_STEPS=0`, `POLYGUARD_GRPO_MAX_PROMPTS=0`: full-corpus/full-epoch mode.\n",
"- `POLYGUARD_WAIT_FOR_REMOTE_TRAINING=1`: keep polling until artifacts are pulled or timeout hits.\n",
"- `POLYGUARD_RUN_LOCAL_SMOKE=1`: also run a tiny local SFT/GRPO smoke loop."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from __future__ import annotations\n",
"\n",
"import json\n",
"import os\n",
"from pathlib import Path\n",
"import subprocess\n",
"import sys\n",
"import time\n",
"\n",
"PROJECT_SUBDIR = \"polyguard-rl\"\n",
"DEFAULT_REPO_URL = \"https://github.com/Vishwa-docs/Meta_Pytorch_OpenEnv_Scaler_VK.git\"\n",
"REPO_URL = os.getenv(\"POLYGUARD_GITHUB_REPO_URL\", DEFAULT_REPO_URL)\n",
"\n",
"cwd = Path.cwd().resolve()\n",
"if (cwd / \"pyproject.toml\").exists() and (cwd / \"scripts\").exists():\n",
" ROOT = cwd\n",
"elif (cwd / PROJECT_SUBDIR / \"pyproject.toml\").exists():\n",
" ROOT = cwd / PROJECT_SUBDIR\n",
"else:\n",
" clone_root = Path(os.getenv(\"POLYGUARD_REPO_DIR\", \"/content/Meta_Pytorch_OpenEnv_Scaler_VK\")).resolve()\n",
" if not clone_root.exists():\n",
" subprocess.run([\"git\", \"clone\", REPO_URL, str(clone_root)], check=True)\n",
" ROOT = clone_root / PROJECT_SUBDIR\n",
"\n",
"os.chdir(ROOT)\n",
"print(f\"PolyGuard root: {ROOT}\")\n",
"\n",
"def run(cmd: list[str] | str, *, check: bool = True, env: dict[str, str] | None = None) -> subprocess.CompletedProcess[str]:\n",
" printable = cmd if isinstance(cmd, str) else \" \".join(cmd)\n",
" print(f\"\\n$ {printable}\")\n",
" merged_env = os.environ.copy()\n",
" if env:\n",
" merged_env.update(env)\n",
" completed = subprocess.run(cmd, check=False, text=True, env=merged_env)\n",
" if check and completed.returncode != 0:\n",
" raise RuntimeError(f\"command_failed:{printable}\")\n",
" return completed\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Install local runtime dependencies. This keeps the notebook kernel light while project commands run through uv.\n",
"run([sys.executable, \"-m\", \"pip\", \"install\", \"-q\", \"-U\", \"uv\", \"huggingface_hub\", \"gradio_client\"])\n",
"run([\"uv\", \"sync\"])\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def read_colab_secret(name: str) -> str:\n",
" try:\n",
" from google.colab import userdata # type: ignore\n",
" except Exception:\n",
" return \"\"\n",
" try:\n",
" return str(userdata.get(name) or \"\")\n",
" except Exception:\n",
" return \"\"\n",
"\n",
"HF_TOKEN = os.getenv(\"HF_TOKEN\", \"\") or read_colab_secret(\"HF_TOKEN\")\n",
"if HF_TOKEN:\n",
" os.environ[\"HF_TOKEN\"] = HF_TOKEN\n",
"\n",
"if os.getenv(\"POLYGUARD_REQUIRE_HF_TOKEN\", \"1\") == \"1\" and not HF_TOKEN:\n",
" raise RuntimeError(\"Set HF_TOKEN as an environment variable or Colab secret before running the remote training cells.\")\n",
"\n",
"HF_USERNAME = os.getenv(\"HF_USERNAME\", \"\")\n",
"if HF_TOKEN and not HF_USERNAME:\n",
" from huggingface_hub import HfApi\n",
"\n",
" whoami = HfApi(token=HF_TOKEN).whoami(token=HF_TOKEN)\n",
" HF_USERNAME = str(whoami.get(\"name\") or whoami.get(\"fullname\") or \"\")\n",
"\n",
"if not HF_USERNAME:\n",
" HF_USERNAME = \"TheJackBright\"\n",
"\n",
"MODEL_SWEEP = os.getenv(\n",
" \"POLYGUARD_MODEL_SWEEP\",\n",
" \"Qwen/Qwen2.5-0.5B-Instruct,Qwen/Qwen2.5-1.5B-Instruct,Qwen/Qwen2.5-3B-Instruct\",\n",
")\n",
"TRAINING_SPACE_REPO_ID = os.getenv(\"POLYGUARD_TRAINING_SPACE_REPO_ID\", f\"{HF_USERNAME}/polyguard-openenv-training-full\")\n",
"ARTIFACT_REPO_ID = os.getenv(\"POLYGUARD_ARTIFACT_REPO_ID\", f\"{HF_USERNAME}/polyguard-openenv-training-full-artifacts\")\n",
"PRODUCT_SPACE_REPO_ID = os.getenv(\"POLYGUARD_PRODUCT_SPACE_REPO_ID\", f\"{HF_USERNAME}/polyguard-openenv\")\n",
"\n",
"SFT_EPOCHS = os.getenv(\"POLYGUARD_SFT_EPOCHS\", \"2\")\n",
"GRPO_EPOCHS = os.getenv(\"POLYGUARD_GRPO_EPOCHS\", \"1\")\n",
"SFT_MAX_STEPS = os.getenv(\"POLYGUARD_SFT_MAX_STEPS\", \"0\")\n",
"GRPO_MAX_STEPS = os.getenv(\"POLYGUARD_GRPO_MAX_STEPS\", \"0\")\n",
"GRPO_MAX_PROMPTS = os.getenv(\"POLYGUARD_GRPO_MAX_PROMPTS\", \"0\")\n",
"GRPO_NUM_GENERATIONS = os.getenv(\"POLYGUARD_GRPO_NUM_GENERATIONS\", \"2\")\n",
"DATA_PROFILE = os.getenv(\"POLYGUARD_DATA_PROFILE\", \"massive\")\n",
"\n",
"RUN_REMOTE_TRAINING = os.getenv(\"POLYGUARD_RUN_REMOTE_TRAINING\", \"1\") == \"1\"\n",
"WAIT_FOR_REMOTE_TRAINING = os.getenv(\"POLYGUARD_WAIT_FOR_REMOTE_TRAINING\", \"1\") == \"1\"\n",
"RUN_LOCAL_SMOKE = os.getenv(\"POLYGUARD_RUN_LOCAL_SMOKE\", \"0\") == \"1\"\n",
"DEPLOY_PRODUCT_SPACE = os.getenv(\"POLYGUARD_DEPLOY_PRODUCT_SPACE\", \"1\") == \"1\"\n",
"PRODUCT_SPACE_PRIVATE = os.getenv(\"POLYGUARD_PRODUCT_SPACE_PRIVATE\", \"0\") == \"1\"\n",
"REMOTE_TIMEOUT_HOURS = float(os.getenv(\"POLYGUARD_REMOTE_TIMEOUT_HOURS\", \"12\"))\n",
"REMOTE_POLL_SECONDS = int(os.getenv(\"POLYGUARD_REMOTE_POLL_SECONDS\", \"300\"))\n",
"\n",
"print(json.dumps({\n",
" \"hf_username\": HF_USERNAME,\n",
" \"model_sweep\": MODEL_SWEEP,\n",
" \"training_space_repo_id\": TRAINING_SPACE_REPO_ID,\n",
" \"artifact_repo_id\": ARTIFACT_REPO_ID,\n",
" \"product_space_repo_id\": PRODUCT_SPACE_REPO_ID,\n",
" \"data_profile\": DATA_PROFILE,\n",
" \"run_remote_training\": RUN_REMOTE_TRAINING,\n",
" \"wait_for_remote_training\": WAIT_FOR_REMOTE_TRAINING,\n",
" \"run_local_smoke\": RUN_LOCAL_SMOKE,\n",
" \"deploy_product_space\": DEPLOY_PRODUCT_SPACE,\n",
"}, indent=2))\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1) Build Data And Training Corpora\n",
"\n",
"This builds processed data, scenario artifacts, SFT records, and GRPO prompt episodes. The training Space repeats the full build inside its container so remote training is reproducible."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"run([\"uv\", \"run\", \"python\", \"scripts/bootstrap_data.py\"])\n",
"run([\n",
" \"uv\", \"run\", \"python\", \"scripts/build_training_corpus.py\",\n",
" \"--profile\", DATA_PROFILE,\n",
" \"--with-local\",\n",
" \"--with-synthetic\",\n",
" \"--with-hf\",\n",
"])\n",
"summary_path = Path(\"data/processed/training_corpus_summary.json\")\n",
"print(summary_path.read_text(encoding=\"utf-8\") if summary_path.exists() else \"training_corpus_summary_missing\")\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2) Local Contract Checks\n",
"\n",
"These checks verify the package, OpenEnv contract, reward bounds, and report-generation surfaces before spending GPU time."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"run([\"uv\", \"run\", \"pytest\"])\n",
"run([\"uv\", \"run\", \"openenv\", \"validate\", \".\"])\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3) Optional Local Smoke SFT And GRPO\n",
"\n",
"The final training path is the HF Space below. Set `POLYGUARD_RUN_LOCAL_SMOKE=1` only if you want a tiny local compliance run before the remote job."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"if RUN_LOCAL_SMOKE:\n",
" local_model = os.getenv(\"POLYGUARD_LOCAL_SMOKE_MODEL\", \"Qwen/Qwen2.5-0.5B-Instruct\")\n",
" run([\n",
" \"uv\", \"run\", \"python\", \"scripts/train_sft_trl.py\",\n",
" \"--model-id\", local_model,\n",
" \"--dataset-path\", \"data/processed/training_corpus_sft.json\",\n",
" \"--output-dir\", \"checkpoints/sft_adapter\",\n",
" \"--report-path\", \"outputs/reports/sft_trl_run.json\",\n",
" \"--epochs\", \"1\",\n",
" \"--max-steps\", \"20\",\n",
" \"--batch-size\", \"1\",\n",
" \"--use-unsloth\",\n",
" ])\n",
" run([\n",
" \"uv\", \"run\", \"python\", \"scripts/train_grpo_trl.py\",\n",
" \"--model-id\", local_model,\n",
" \"--prompts-path\", \"data/processed/training_corpus_grpo_prompts.jsonl\",\n",
" \"--output-dir\", \"checkpoints/grpo_adapter\",\n",
" \"--report-path\", \"outputs/reports/grpo_trl_run.json\",\n",
" \"--max-steps\", \"20\",\n",
" \"--max-prompts\", \"64\",\n",
" \"--num-generations\", \"2\",\n",
" \"--batch-size\", \"1\",\n",
" \"--use-unsloth\",\n",
" ])\n",
"else:\n",
" print(\"Local smoke skipped. Remote HF Space training remains the main path.\")\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 4) Start SFT Baseline And GRPO Training On Hugging Face Spaces\n",
"\n",
"This deploys the private training Space and artifact repo, starts the Docker runner, builds the full corpus inside the Space, trains SFT as the baseline, trains GRPO with environment-backed rewards, runs post-save inference and ablations, then uploads reports, plots, adapters, and manifests."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"if RUN_REMOTE_TRAINING:\n",
" deploy_cmd = [\n",
" \"uv\", \"run\", \"python\", \"scripts/deploy_training_space.py\",\n",
" \"--repo-id\", TRAINING_SPACE_REPO_ID,\n",
" \"--artifact-repo-id\", ARTIFACT_REPO_ID,\n",
" \"--hardware\", os.getenv(\"POLYGUARD_HF_HARDWARE\", \"a10g-large\"),\n",
" \"--model-sweep\", MODEL_SWEEP,\n",
" \"--training-mode\", os.getenv(\"POLYGUARD_TRAINING_MODE\", \"full\"),\n",
" \"--sft-epochs\", SFT_EPOCHS,\n",
" \"--grpo-epochs\", GRPO_EPOCHS,\n",
" \"--sft-max-steps\", SFT_MAX_STEPS,\n",
" \"--grpo-max-steps\", GRPO_MAX_STEPS,\n",
" \"--grpo-max-prompts\", GRPO_MAX_PROMPTS,\n",
" \"--grpo-num-generations\", GRPO_NUM_GENERATIONS,\n",
" ]\n",
" if os.getenv(\"POLYGUARD_TRAINING_SPACE_PUBLIC\", \"0\") == \"1\":\n",
" deploy_cmd.append(\"--public\")\n",
" run(deploy_cmd)\n",
" print(f\"Training Space: https://huggingface.co/spaces/{TRAINING_SPACE_REPO_ID}\")\n",
" print(f\"Artifact repo: https://huggingface.co/{ARTIFACT_REPO_ID}\")\n",
"else:\n",
" print(\"Remote training deployment skipped by POLYGUARD_RUN_REMOTE_TRAINING=0\")\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 5) Monitor Space And Pull Artifacts\n",
"\n",
"If `POLYGUARD_WAIT_FOR_REMOTE_TRAINING=1`, this cell keeps polling until `scripts/pull_training_artifacts.py` succeeds or the timeout is reached. It never prints the token."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"monitor_output = \"outputs/reports/training_space_runtime_status.json\"\n",
"\n",
"def monitor_once() -> int:\n",
" return run([\n",
" \"uv\", \"run\", \"python\", \"scripts/monitor_training_space_status.py\",\n",
" \"--space-id\", TRAINING_SPACE_REPO_ID,\n",
" \"--artifact-repo-id\", ARTIFACT_REPO_ID,\n",
" \"--output\", monitor_output,\n",
" ], check=False).returncode\n",
"\n",
"def pull_once() -> bool:\n",
" return run([\n",
" \"uv\", \"run\", \"python\", \"scripts/pull_training_artifacts.py\",\n",
" \"--artifact-repo-id\", ARTIFACT_REPO_ID,\n",
" ], check=False).returncode == 0\n",
"\n",
"pulled = False\n",
"if RUN_REMOTE_TRAINING and WAIT_FOR_REMOTE_TRAINING:\n",
" deadline = time.time() + REMOTE_TIMEOUT_HOURS * 3600\n",
" attempt = 0\n",
" while time.time() < deadline:\n",
" attempt += 1\n",
" print(f\"Remote poll {attempt}\")\n",
" monitor_once()\n",
" pulled = pull_once()\n",
" if pulled:\n",
" print(\"Remote training artifacts pulled successfully.\")\n",
" break\n",
" print(f\"Artifacts not ready yet. Sleeping {REMOTE_POLL_SECONDS} seconds.\")\n",
" time.sleep(REMOTE_POLL_SECONDS)\n",
" if not pulled:\n",
" raise TimeoutError(\"Remote training did not produce pullable artifacts before timeout.\")\n",
"else:\n",
" monitor_once()\n",
" pulled = pull_once()\n",
" print(f\"Single pull attempt success: {pulled}\")\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 6) Generate Reports, Charts, And Evidence Bundles\n",
"\n",
"This creates SFT-vs-GRPO charts, Qwen model comparison charts, reward component bars, anti-hacking/overfit checks, basic-LLM-vs-PolyGuard evidence, action traces, and curated submission evidence folders."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"run([\"uv\", \"run\", \"python\", \"scripts/generate_hf_training_report.py\", \"--mode\", os.getenv(\"POLYGUARD_TRAINING_MODE\", \"full\")], check=False)\n",
"run([\"uv\", \"run\", \"python\", \"scripts/evaluate_policy_ablations.py\", \"--episodes\", os.getenv(\"POLYGUARD_ABLATION_EPISODES\", \"8\")], check=False)\n",
"run([\n",
" \"uv\", \"run\", \"python\", \"scripts/generate_submission_evidence.py\",\n",
" \"--models\", os.getenv(\"POLYGUARD_EVIDENCE_MODELS\", \"qwen-qwen2-5-0-5b-instruct,qwen-qwen2-5-1-5b-instruct\"),\n",
" \"--artifact-repo-id\", ARTIFACT_REPO_ID,\n",
" \"--training-space-url\", f\"https://{TRAINING_SPACE_REPO_ID.replace('/', '-').lower()}.hf.space\",\n",
" \"--episodes\", os.getenv(\"POLYGUARD_EVIDENCE_EPISODES\", \"8\"),\n",
"], check=False)\n",
"run([\"uv\", \"run\", \"python\", \"scripts/build_improvement_evidence_bundle.py\"], check=False)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 7) Activate A Model For Product Inference And Validate Post-Save Inference\n",
"\n",
"The app reads `checkpoints/active/active_model_manifest.json`. The default active run is Qwen 0.5B because it is the smallest practical implementation target; switch `POLYGUARD_ACTIVE_RUN_ID` to the 1.5B or 3B run after those artifacts are pulled."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ACTIVE_RUN_ID = os.getenv(\"POLYGUARD_ACTIVE_RUN_ID\", \"qwen-qwen2-5-0-5b-instruct\")\n",
"run([\n",
" \"uv\", \"run\", \"python\", \"scripts/activate_sweep_model.py\",\n",
" \"--source\", \"sweep\",\n",
" \"--run-id\", ACTIVE_RUN_ID,\n",
" \"--preferred-artifact\", os.getenv(\"POLYGUARD_PREFERRED_ARTIFACT\", \"grpo_adapter\"),\n",
"], check=False)\n",
"run([\"uv\", \"run\", \"python\", \"scripts/test_inference_postsave.py\", \"--samples\", os.getenv(\"POLYGUARD_INFERENCE_SAMPLES\", \"3\")], check=False)\n",
"run([\"uv\", \"run\", \"python\", \"scripts/benchmark_inference.py\"], check=False)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 8) Deploy The Product OpenEnv Space\n",
"\n",
"This deploys the FastAPI/OpenEnv product Space. It is separate from the private GPU training Space."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"if DEPLOY_PRODUCT_SPACE:\n",
" product_cmd = [\"uv\", \"run\", \"python\", \"scripts/deploy_space_api.py\", \"--repo-id\", PRODUCT_SPACE_REPO_ID]\n",
" if PRODUCT_SPACE_PRIVATE:\n",
" product_cmd.append(\"--private\")\n",
" run(product_cmd)\n",
" runtime_url = f\"https://{PRODUCT_SPACE_REPO_ID.replace('/', '-').lower()}.hf.space\"\n",
" run([\"uv\", \"run\", \"openenv\", \"validate\", \"--url\", runtime_url], check=False)\n",
" print(f\"Product Space: https://huggingface.co/spaces/{PRODUCT_SPACE_REPO_ID}\")\n",
" print(f\"Runtime URL: {runtime_url}\")\n",
"else:\n",
" print(\"Product Space deploy skipped by POLYGUARD_DEPLOY_PRODUCT_SPACE=0\")\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 9) Final Acceptance Gate And Output Summary"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"run([\"uv\", \"run\", \"python\", \"scripts/acceptance_gate.py\"], check=False)\n",
"\n",
"summary = {\n",
" \"training_space\": f\"https://huggingface.co/spaces/{TRAINING_SPACE_REPO_ID}\",\n",
" \"artifact_repo\": f\"https://huggingface.co/{ARTIFACT_REPO_ID}\",\n",
" \"product_space\": f\"https://huggingface.co/spaces/{PRODUCT_SPACE_REPO_ID}\",\n",
" \"reports\": [\n",
" \"outputs/reports/hf_sweep_summary.json\",\n",
" \"outputs/reports/anti_hacking_overfit_report.json\",\n",
" \"outputs/reports/postsave_inference.json\",\n",
" \"docs/results/submission_evidence_qwen_0_5b_1_5b/README.md\",\n",
" \"docs/results/model_improvement_evidence_qwen_0_5b_1_5b/README.md\",\n",
" ],\n",
" \"plots_dir\": \"outputs/plots\",\n",
" \"active_model_manifest\": \"checkpoints/active/active_model_manifest.json\",\n",
"}\n",
"print(json.dumps(summary, indent=2))\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
|