MastersThesis/src/paddle_ocr_raytune_rest.ipynb

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   "source": [
    "# PaddleOCR Hyperparameter Optimization via REST API\n",
    "\n",
    "Uses Ray Tune + Optuna to find optimal PaddleOCR parameters.\n",
    "\n",
    "## Prerequisites\n",
    "\n",
    "```bash\n",
    "cd src/paddle_ocr\n",
    "docker compose -f docker-compose.workers.yml up  # GPU workers on 8001-8002\n",
    "# or: docker compose -f docker-compose.workers.yml --profile cpu up\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "deps",
   "metadata": {},
   "outputs": [],
   "source": [
    "%pip install -q -U \"ray[tune]\" optuna requests pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "setup",
   "metadata": {},
   "outputs": [],
   "source": [
    "from raytune_ocr import (\n",
    "    check_workers, create_trainable, run_tuner, analyze_results, correlation_analysis,\n",
    "    paddle_ocr_payload, PADDLE_OCR_SEARCH_SPACE, PADDLE_OCR_CONFIG_KEYS,\n",
    ")\n",
    "\n",
    "# Worker ports\n",
    "PORTS = [8001, 8002]\n",
    "\n",
    "# Check workers are running\n",
    "healthy = check_workers(PORTS, \"PaddleOCR\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "tune",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create trainable and run tuning\n",
    "trainable = create_trainable(PORTS, paddle_ocr_payload)\n",
    "\n",
    "results = run_tuner(\n",
    "    trainable=trainable,\n",
    "    search_space=PADDLE_OCR_SEARCH_SPACE,\n",
    "    num_samples=64,\n",
    "    num_workers=len(healthy),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "analysis",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Analyze results\n",
    "df = analyze_results(\n",
    "    results,\n",
    "    prefix=\"raytune_paddle\",\n",
    "    config_keys=PADDLE_OCR_CONFIG_KEYS,\n",
    ")\n",
    "\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "correlation",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Correlation analysis\n",
    "correlation_analysis(df, PADDLE_OCR_CONFIG_KEYS)"
   ]
  }
 ],
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