debug set and locking

src/doctr_raytune_rest.ipynb

@@ -0,0 +1,111 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "header",
+   "metadata": {},
+   "source": [
+    "# DocTR Hyperparameter Optimization via REST API\n",
+    "\n",
+    "Uses Ray Tune + Optuna to find optimal DocTR parameters.\n",
+    "\n",
+    "## Prerequisites\n",
+    "\n",
+    "```bash\n",
+    "cd src/doctr_service\n",
+    "docker compose up ocr-cpu  # or ocr-gpu\n",
+    "```\n",
+    "\n",
+    "Service runs on port 8003."
+   ]
+  },
+  {
+   "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",
+    "    doctr_payload, DOCTR_SEARCH_SPACE, DOCTR_CONFIG_KEYS,\n",
+    ")\n",
+    "\n",
+    "# Worker ports\n",
+    "PORTS = [8003]\n",
+    "\n",
+    "# Check workers are running\n",
+    "healthy = check_workers(PORTS, \"DocTR\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "tune",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create trainable and run tuning\n",
+    "trainable = create_trainable(PORTS, doctr_payload)\n",
+    "\n",
+    "results = run_tuner(\n",
+    "    trainable=trainable,\n",
+    "    search_space=DOCTR_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_doctr\",\n",
+    "    config_keys=DOCTR_CONFIG_KEYS,\n",
+    ")\n",
+    "\n",
+    "df.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "correlation",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Correlation analysis\n",
+    "correlation_analysis(df, DOCTR_CONFIG_KEYS)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3.10.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}