Spaces:
Running on A10G
Running on A10G
File size: 22,119 Bytes
95cbc5b | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# CommitGuard GRPO Training Notebook\n",
"\n",
"Train Llama-3.2-3B-Instruct to detect exploitable vulnerabilities in code commits using GRPO (Group Relative Policy Optimization).\n",
"\n",
"**Requirements:** NVIDIA GPU with 16 GB VRAM (L4/A100/T4). Run this notebook on a GCP VM with GPU attached.\n",
"\n",
"## Setup\n",
"Connect to this notebook via SSH tunnel:\n",
"```bash\n",
"# On GCP VM:\n",
"jupyter notebook --no-browser --port=8888\n",
"\n",
"# On your local machine:\n",
"gcloud compute ssh commitguard-train --zone=us-central1-a -- -NL 8888:localhost:8888\n",
"# Then open http://localhost:8888 in browser\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Cell 1 Install Dependencies"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"<3>WSL (3364 - Relay) ERROR: CreateProcessCommon:800: execvpe(/bin/bash) failed: No such file or directory\n"
]
},
{
"ename": "CalledProcessError",
"evalue": "Command 'b'# Install uv for fast, reliable dependency resolution\\ncurl -LsSf https://astral.sh/uv/install.sh | sh\\nexport PATH=\"$HOME/.local/bin:$PATH\"\\n\\nuv pip install -q \\\\\\n \"unsloth[cu124-torch240]\" \\\\\\n \"trl>=0.12\" \\\\\\n \"peft>=0.13\" \\\\\\n \"bitsandbytes>=0.44\" \\\\\\n \"transformers>=4.46\" \\\\\\n \"datasets>=3.0\" \\\\\\n \"accelerate>=1.0\" \\\\\\n \"wandb\" \\\\\\n \"fastapi\" \\\\\\n \"uvicorn[standard]\" \\\\\\n \"requests\" \\\\\\n \"matplotlib\"\\n'' returned non-zero exit status 1.",
"output_type": "error",
"traceback": [
"\u001b[31m---------------------------------------------------------------------------\u001b[39m",
"\u001b[31mCalledProcessError\u001b[39m Traceback (most recent call last)",
"\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[3]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m get_ipython().run_cell_magic(\u001b[33m'bash'\u001b[39m, \u001b[33m''\u001b[39m, \u001b[33m'# Install uv for fast, reliable dependency resolution\\ncurl -LsSf https://astral.sh/uv/install.sh | sh\\nexport PATH=\"$HOME/.local/bin:$PATH\"\\n\\nuv pip install -q \\\\\\n \"unsloth[cu124-torch240]\" \\\\\\n \"trl>=0.12\" \\\\\\n \"peft>=0.13\" \\\\\\n \"bitsandbytes>=0.44\" \\\\\\n \"transformers>=4.46\" \\\\\\n \"datasets>=3.0\" \\\\\\n \"accelerate>=1.0\" \\\\\\n \"wandb\" \\\\\\n \"fastapi\" \\\\\\n \"uvicorn[standard]\" \\\\\\n \"requests\" \\\\\\n \"matplotlib\"\\n'\u001b[39m)\n",
"\u001b[31mCalledProcessError\u001b[39m: Command 'b'# Install uv for fast, reliable dependency resolution\\ncurl -LsSf https://astral.sh/uv/install.sh | sh\\nexport PATH=\"$HOME/.local/bin:$PATH\"\\n\\nuv pip install -q \\\\\\n \"unsloth[cu124-torch240]\" \\\\\\n \"trl>=0.12\" \\\\\\n \"peft>=0.13\" \\\\\\n \"bitsandbytes>=0.44\" \\\\\\n \"transformers>=4.46\" \\\\\\n \"datasets>=3.0\" \\\\\\n \"accelerate>=1.0\" \\\\\\n \"wandb\" \\\\\\n \"fastapi\" \\\\\\n \"uvicorn[standard]\" \\\\\\n \"requests\" \\\\\\n \"matplotlib\"\\n'' returned non-zero exit status 1."
]
}
],
"source": [
"!pip install -q unsloth\n",
"!pip uninstall unsloth -y && pip install -q --upgrade --no-cache-dir \"unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git\"\n",
"!pip install -q trl>=0.12 peft bitsandbytes transformers datasets accelerate wandb fastapi uvicorn[standard] requests matplotlib"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Cell 2 Verify GPU"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import torch\n",
"print(f\"PyTorch: {torch.__version__}\")\n",
"print(f\"CUDA: {torch.cuda.is_available()}\")\n",
"if torch.cuda.is_available():\n",
" print(f\"GPU: {torch.cuda.get_device_name(0)}\")\n",
" print(f\"VRAM: {torch.cuda.get_device_properties(0).total_memory / 1024**3:.1f} GB\")\n",
" print(f\"BF16: {torch.cuda.is_bf16_supported()}\")\n",
"else:\n",
" raise RuntimeError(\"No GPU detected this notebook requires a CUDA GPU.\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Cell 3 Clone Repo & Start Env Server"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import os, subprocess, time, requests, sys\n",
"\n",
"# Check if running in Google Colab\n",
"if \"google.colab\" in sys.modules:\n",
" print(\"Running in Google Colab.\")\n",
" # Reset to base directory in case cell is run multiple times\n",
" os.chdir(\"/content\")\n",
" \n",
" if not os.path.exists(\"/content/project.zip\"):\n",
" from google.colab import files\n",
" print(\"\\n--- WE NEED YOUR PROJECT.ZIP ---\")\n",
" print(\"Please click 'Choose Files' below and select project.zip from your computer:\\n\")\n",
" uploaded = files.upload()\n",
" \n",
" if os.path.exists(\"/content/project.zip\"):\n",
" print(\"Extracting project.zip...\")\n",
" !unzip -q -o /content/project.zip -d /content/commitguard\n",
" else:\n",
" print(\"\\n*** ERROR: project.zip still not found! ***\\n\")\n",
" sys.exit(1)\n",
" \n",
" os.chdir(\"/content/commitguard\")\n",
" REPO_DIR = os.getcwd()\n",
"else:\n",
" if os.path.basename(os.getcwd()) == \"notebooks\":\n",
" REPO_DIR = os.path.abspath(\"..\")\n",
" else:\n",
" REPO_DIR = os.getcwd()\n",
" os.chdir(REPO_DIR)\n",
"\n",
"print(f\"Using REPO_DIR: {REPO_DIR}\")\n",
"\n",
"# 2. Install current project in editable mode\n",
"!pip install -e . -q\n",
"\n",
"# 3. Start env server in background\n",
"server_proc = subprocess.Popen(\n",
" [sys.executable, \"-m\", \"commitguard_env.server\"],\n",
" stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True\n",
")\n",
"time.sleep(5)\n",
"\n",
"try:\n",
" r = requests.get(\"http://localhost:8000/health\")\n",
" print(f\"Env server: {r.json()}\")\n",
"except Exception as e:\n",
" print(f\"Server failed to start: {e}\")\n",
" stdout, stderr = server_proc.communicate(timeout=1)\n",
" print(f\"STDOUT: {stdout}\")\n",
" print(f\"STDERR: {stderr}\")\n",
"\n",
"# Quick sanity reset + step\n",
"r = requests.post(\"http://localhost:8000/reset\", json={})\n",
"obs = r.json()[\"observation\"]\n",
"print(f\"Sample diff length: {len(obs['diff'])} chars, files: {obs['available_files']}\")\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Cell 4 HuggingFace Login (for gated Llama model)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from huggingface_hub import login\n",
"\n",
"HF_TOKEN = os.getenv(\"HF_TOKEN\")\n",
"if HF_TOKEN:\n",
" login(token=HF_TOKEN)\n",
" print(\"Logged in via token.\")\n",
"else:\n",
" login()\n" ]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Cell 5 Wandb Login (optional but recommended)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import wandb\n",
"\n",
"USE_WANDB = False\n",
"os.environ[\"WANDB_DISABLED\"] = \"true\"\n",
"print(\"Wandb disabled.\")\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Cell 6 Load Model with Unsloth (4-bit LoRA)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from unsloth import FastLanguageModel, PatchFastRL\n",
"from trl import GRPOConfig, GRPOTrainer\n",
"\n",
"PatchFastRL(\"GRPO\", FastLanguageModel)\n",
"\n",
"MODEL_NAME = \"meta-llama/Llama-3.2-3B-Instruct\"\n",
"\n",
"print(f\"Loading {MODEL_NAME} in 4-bit...\")\n",
"model, tokenizer = FastLanguageModel.from_pretrained(\n",
" model_name=MODEL_NAME,\n",
" max_seq_length=2048,\n",
" load_in_4bit=True,\n",
" fast_inference=False,\n",
" max_lora_rank=16,\n",
")\n",
"\n",
"model = FastLanguageModel.get_peft_model(\n",
" model,\n",
" r=8,\n",
" target_modules=[\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\",\n",
" \"gate_proj\", \"up_proj\", \"down_proj\"],\n",
" lora_alpha=16,\n",
" lora_dropout=0,\n",
" bias=\"none\",\n",
" use_gradient_checkpointing=\"unsloth\",\n",
" random_state=3407,\n",
")\n",
"\n",
"print(f\"Model loaded. Trainable params: {model.print_trainable_parameters()}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Cell 7 Build Training Dataset from Env"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import sys, requests\n",
"from datasets import Dataset\n",
"\n",
"sys.path.insert(0, os.path.join(REPO_DIR, \"scripts\"))\n",
"from agent_prompt import SYSTEM_PROMPT, get_agent_prompt\n",
"\n",
"ENV_URL = \"http://localhost:8000\"\n",
"N_SAMPLES = 200 # Number of training prompts (updated)\n",
"\n",
"samples = []\n",
"for i in range(N_SAMPLES):\n",
" r = requests.post(f\"{ENV_URL}/reset\", json={}, timeout=10)\n",
" if r.status_code != 200:\n",
" continue\n",
" obs = r.json()[\"observation\"]\n",
" state_r = requests.get(f\"{ENV_URL}/state\").json()\n",
" current_sample_id = state_r.get(\"state\", {}).get(\"current_sample_id\", \"unknown\")\n",
" user_msg = get_agent_prompt(obs[\"diff\"], obs[\"available_files\"], obs.get(\"step_idx\", 0))\n",
" samples.append({\n",
" \"prompt\": [\n",
" {\"role\": \"system\", \"content\": SYSTEM_PROMPT},\n",
" {\"role\": \"user\", \"content\": user_msg},\n",
" ],\n",
" \"sample_id\": current_sample_id,\n",
" })\n",
" if (i + 1) % 50 == 0:\n",
" print(f\" fetched {i + 1}/{N_SAMPLES}\")\n",
"\n",
"dataset = Dataset.from_list(samples)\n",
"print(f\"\\nDataset ready: {len(dataset)} samples\")\n",
"print(f\"Sample prompt preview: {str(dataset[0]['prompt'][1]['content'])[:200]}...\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Cell 8 Define Reward Function"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def get_reward_from_env(prompts, completions, sample_id, **kwargs) -> list[float]:\n",
" \"\"\"Send each completion to the env as an action, collect reward.\"\"\"\n",
" rewards = []\n",
" for p_id, completion in zip(sample_id, completions):\n",
" try:\n",
" requests.post(f\"{ENV_URL}/reset\", json={\"sample_id\": p_id}, timeout=10)\n",
" text = completion[-1][\"content\"] if isinstance(completion, list) else str(completion)\n",
" r = requests.post(f\"{ENV_URL}/step\", json={\"action\": text}, timeout=10)\n",
" if r.status_code == 200:\n",
" rewards.append(float(r.json().get(\"reward\", 0.0)))\n",
" else:\n",
" rewards.append(-0.5)\n",
" except Exception:\n",
" rewards.append(-1.0)\n",
" return rewards\n",
"\n",
"# Quick test\n",
"test_r = get_reward_from_env(\n",
" [\"test\"],\n",
" [\"<action><action_type>verdict</action_type><is_vulnerable>true</is_vulnerable><vuln_type>CWE-119</vuln_type><exploit_sketch>buffer overflow</exploit_sketch></action>\"],\n",
" [\"test_id\"]\n",
")\n",
"print(f\"Reward function test: {test_r}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Cell 9 Configure & Launch GRPO Training\n",
"\n",
"This is the main training loop. ~2-3 hours on L4 for 300 steps."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"OUTPUT_DIR = \"outputs/commitguard-llama-3b\"\n",
"\n",
"training_args = GRPOConfig(\n",
" output_dir=OUTPUT_DIR,\n",
" num_generations=4,\n",
" max_completion_length=512,\n",
" per_device_train_batch_size=1,\n",
" gradient_accumulation_steps=4,\n",
" learning_rate=5e-6,\n",
" logging_steps=1,\n",
" save_steps=50,\n",
" max_steps=300,\n",
" report_to=\"wandb\" if USE_WANDB else \"none\",\n",
" bf16=torch.cuda.is_bf16_supported(),\n",
" fp16=not torch.cuda.is_bf16_supported(),\n",
")\n",
"\n",
"trainer = GRPOTrainer(\n",
" model=model,\n",
" processing_class=tokenizer,\n",
" reward_funcs=[get_reward_from_env],\n",
" args=training_args,\n",
" train_dataset=dataset,\n",
")\n",
"\n",
"print(\"Starting GRPO training...\")\n",
"print(f\" Steps: {training_args.max_steps}\")\n",
"print(f\" Generations per prompt: {training_args.num_generations}\")\n",
"print(f\" Save every: {training_args.save_steps} steps\")\n",
"print(f\" Output: {OUTPUT_DIR}\")\n",
"print(\"=\"*50)\n",
"\n",
"trainer.train()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Cell 10 Save Final LoRA Adapter"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"FINAL_DIR = f\"{OUTPUT_DIR}/final\"\n",
"model.save_pretrained_merged(FINAL_DIR, tokenizer, save_method=\"lora\")\n",
"print(f\"LoRA adapter saved to {FINAL_DIR}\")\n",
"\n",
"# List saved files\n",
"for f in sorted(os.listdir(FINAL_DIR)):\n",
" size_mb = os.path.getsize(os.path.join(FINAL_DIR, f)) / 1024**2\n",
" print(f\" {f}: {size_mb:.1f} MB\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Cell 11 Quick Evaluation (Baseline vs Trained)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import json\n",
"\n",
"# Load test set\n",
"test_path = os.path.join(REPO_DIR, \"data\", \"devign_test.jsonl\")\n",
"with open(test_path) as f:\n",
" test_samples = [json.loads(l) for l in f if l.strip()]\n",
"\n",
"print(f\"Evaluating on {len(test_samples)} held-out samples...\")\n",
"\n",
"# Run trained model on test set\n",
"FastLanguageModel.for_inference(model)\n",
"\n",
"correct = 0\n",
"results = []\n",
"\n",
"for i, sample in enumerate(test_samples):\n",
" user_msg = get_agent_prompt(sample[\"diff\"], sample[\"available_files\"], 0)\n",
" messages = [\n",
" {\"role\": \"system\", \"content\": SYSTEM_PROMPT},\n",
" {\"role\": \"user\", \"content\": user_msg},\n",
" ]\n",
" inputs = tokenizer.apply_chat_template(messages, return_tensors=\"pt\", add_generation_prompt=True).to(model.device)\n",
" with torch.no_grad():\n",
" output = model.generate(inputs, max_new_tokens=512, temperature=0.1, do_sample=True)\n",
" response = tokenizer.decode(output[0][inputs.shape[1]:], skip_special_tokens=True)\n",
"\n",
" # Parse verdict\n",
" sys.path.insert(0, os.path.join(REPO_DIR, \"commitguard_env\"))\n",
" from commitguard_env.parse_action import parse_action\n",
" action = parse_action(response)\n",
"\n",
" pred_vuln = bool(action.is_vulnerable) if action.is_vulnerable is not None else False\n",
" truth_vuln = sample[\"is_vulnerable\"]\n",
"\n",
" if pred_vuln == truth_vuln:\n",
" correct += 1\n",
"\n",
" results.append({\n",
" \"sample_id\": sample[\"sample_id\"],\n",
" \"pred\": pred_vuln,\n",
" \"truth\": truth_vuln,\n",
" \"cwe\": sample.get(\"cwe\"),\n",
" \"vuln_type\": action.vuln_type,\n",
" })\n",
"\n",
" if (i + 1) % 20 == 0:\n",
" print(f\" {i+1}/{len(test_samples)} running accuracy: {100*correct/(i+1):.1f}%\")\n",
"\n",
"accuracy = 100 * correct / len(test_samples)\n",
"print(f\"\\nFinal trained accuracy: {accuracy:.1f}%\")\n",
"\n",
"with open(os.path.join(REPO_DIR, \"eval_trained.json\"), \"w\") as f:\n",
" json.dump(results, f, indent=2)\n",
"print(\"Results saved to eval_trained.json\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Cell 12 Generate Plots"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"from collections import Counter\n",
"\n",
"os.makedirs(os.path.join(REPO_DIR, \"plots\"), exist_ok=True)\n",
"\n",
"# --- Plot 1: Training reward curve (from trainer logs) ---\n",
"if hasattr(trainer, 'state') and trainer.state.log_history:\n",
" steps = [l[\"step\"] for l in trainer.state.log_history if \"loss\" in l]\n",
" losses = [l[\"loss\"] for l in trainer.state.log_history if \"loss\" in l]\n",
" \n",
" fig, ax = plt.subplots(figsize=(10, 5))\n",
" ax.plot(steps, losses, color=\"#2ecc71\", linewidth=2)\n",
" ax.set_xlabel(\"Training Step\")\n",
" ax.set_ylabel(\"Loss\")\n",
" ax.set_title(\"CommitGuard GRPO Training Loss\")\n",
" ax.grid(True, linestyle=\"--\", alpha=0.5)\n",
" fig.savefig(os.path.join(REPO_DIR, \"plots\", \"reward_curve.png\"), dpi=150)\n",
" plt.show()\n",
" print(\"Saved plots/reward_curve.png\")\n",
"\n",
" # --- Plot 2: Accuracy comparison ---\n",
" with open(os.path.join(REPO_DIR, \"eval_baseline.json\")) as f:\n",
" b_data = json.load(f)\n",
" baseline_acc = 100 * sum(1 for x in b_data if x['pred'] == x['truth']) / len(b_data)\n",
" trained_acc = accuracy\n",
"\n",
" fig, ax = plt.subplots(figsize=(8, 5))\n",
" bars = ax.bar([\"Baseline (Untrained)\", \"CommitGuard (Trained)\"],\n",
" [baseline_acc, trained_acc],\n",
" color=[\"#95a5a6\", \"#3498db\"])\n",
" ax.set_ylabel(\"Detection Accuracy (%)\")\n",
" ax.set_title(\"Vulnerability Detection: Baseline vs. Trained\")\n",
" ax.set_ylim(0, 100)\n",
" for bar in bars:\n",
" h = bar.get_height()\n",
" ax.text(bar.get_x() + bar.get_width()/2., h + 1, f\"{h:.1f}%\",\n",
" ha=\"center\", fontweight=\"bold\")\n",
" fig.savefig(os.path.join(REPO_DIR, \"plots\", \"baseline_vs_trained.png\"), dpi=150)\n",
" plt.show()\n",
" print(\"Saved plots/baseline_vs_trained.png\")\n",
"\n",
" # --- Plot 3: Per-CWE breakdown ---\n",
" cwe_correct = Counter()\n",
" cwe_total = Counter()\n",
" for r in results:\n",
" if r[\"cwe\"]:\n",
" cwe_total[r[\"cwe\"]] += 1\n",
" if r[\"pred\"] == r[\"truth\"]:\n",
" cwe_correct[r[\"cwe\"]] += 1\n",
"\n",
" cwes = sorted(cwe_total.keys())\n",
" accs = [100 * cwe_correct[c] / cwe_total[c] if cwe_total[c] > 0 else 0 for c in cwes]\n",
"\n",
" if cwes:\n",
" fig, ax = plt.subplots(figsize=(10, 5))\n",
" ax.bar(cwes, accs, color=\"#e67e22\")\n",
" ax.set_ylabel(\"Accuracy (%)\")\n",
" ax.set_title(\"Trained Model Accuracy by CWE Type\")\n",
" ax.set_ylim(0, 100)\n",
" plt.xticks(rotation=45)\n",
" plt.tight_layout()\n",
" fig.savefig(os.path.join(REPO_DIR, \"plots\", \"per_cwe.png\"), dpi=150)\n",
" plt.show()\n",
" print(\"Saved plots/per_cwe.png\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Cell 13 Cleanup\n",
"\n",
"Stop the env server and print final summary."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"server_proc.terminate()\n",
"print(\"Env server stopped.\")\n",
"\n",
"print(\"\\n\" + \"=\"*50)\n",
"print(\" TRAINING COMPLETE\")\n",
"print(\"=\"*50)\n",
"print(f\" Model: {MODEL_NAME}\")\n",
"print(f\" Steps: {training_args.max_steps}\")\n",
"print(f\" Accuracy: {baseline_acc:.1f}% {trained_acc:.1f}% (+{trained_acc - baseline_acc:.1f}pp)\")\n",
"print(f\" Adapter: {FINAL_DIR}\")\n",
"print(f\" Plots: plots/reward_curve.png, baseline_vs_trained.png, per_cwe.png\")\n",
"\n",
"print(\"\\nNext: copy outputs/ and plots/ back to your local machine.\")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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": "3.13.13"
}
},
"nbformat": 4,
"nbformat_minor": 4
} |