src/doctr_raytune_rest.ipynb

@@ -25,9 +25,7 @@
    "id": "deps",
    "metadata": {},
    "outputs": [],
-   "source": [
-    "%pip install -q -U \"ray[tune]\" optuna requests pandas"
-   ]
+   "source": "# Pin Ray version for API stability (tune.report takes dict, not kwargs in 2.x)\n%pip install -q \"ray[tune]==2.53.0\" optuna requests pandas"
   },
   {
    "cell_type": "code",

src/easyocr_raytune_rest.ipynb

@@ -25,9 +25,7 @@
    "id": "deps",
    "metadata": {},
    "outputs": [],
-   "source": [
-    "%pip install -q -U \"ray[tune]\" optuna requests pandas"
-   ]
+   "source": "# Pin Ray version for API stability (tune.report takes dict, not kwargs in 2.x)\n%pip install -q \"ray[tune]==2.53.0\" optuna requests pandas"
   },
   {
    "cell_type": "code",

src/paddle_ocr/docker-compose.workers.yml

@@ -70,6 +70,30 @@ services:
     profiles:
       - gpu
 
+  ocr-worker-3:
+    <<: *ocr-gpu-common
+    container_name: paddle-ocr-worker-3
+    ports:
+      - "8003:8000"
+    profiles:
+      - gpu
+
+  ocr-worker-4:
+    <<: *ocr-gpu-common
+    container_name: paddle-ocr-worker-4
+    ports:
+      - "8004:8000"
+    profiles:
+      - gpu
+
+  ocr-worker-5:
+    <<: *ocr-gpu-common
+    container_name: paddle-ocr-worker-5
+    ports:
+      - "8005:8000"
+    profiles:
+      - gpu
+
   # CPU Workers (cpu profile) - for systems without GPU
   ocr-cpu-worker-1:
     <<: *ocr-cpu-common
@@ -87,6 +111,30 @@ services:
     profiles:
       - cpu
 
+  ocr-cpu-worker-3:
+    <<: *ocr-cpu-common
+    container_name: paddle-ocr-cpu-worker-3
+    ports:
+      - "8003:8000"
+    profiles:
+      - cpu
+
+  ocr-cpu-worker-4:
+    <<: *ocr-cpu-common
+    container_name: paddle-ocr-cpu-worker-4
+    ports:
+      - "8004:8000"
+    profiles:
+      - cpu
+
+  ocr-cpu-worker-5:
+    <<: *ocr-cpu-common
+    container_name: paddle-ocr-cpu-worker-5
+    ports:
+      - "8005:8000"
+    profiles:
+      - cpu
+
 volumes:
   paddlex-cache:
     name: paddlex-model-cache

src/paddle_ocr_raytune_rest.ipynb

@@ -24,9 +24,7 @@
    "id": "deps",
    "metadata": {},
    "outputs": [],
-   "source": [
-    "%pip install -q -U \"ray[tune]\" optuna requests pandas"
-   ]
+   "source": "# Pin Ray version for API stability (tune.report takes dict, not kwargs in 2.x)\n%pip install -q \"ray[tune]==2.53.0\" optuna requests pandas"
   },
   {
    "cell_type": "code",
@@ -34,18 +32,7 @@
    "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\")"
-   ]
+   "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 (3 workers to avoid OOM)\nPORTS = [8001, 8002, 8003]\n\n# Check workers are running\nhealthy = check_workers(PORTS, \"PaddleOCR\")"
   },
   {
    "cell_type": "code",
@@ -53,17 +40,7 @@
    "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",
-    ")"
-   ]
+   "source": "# Create trainable and run tuning\ntrainable = create_trainable(PORTS, paddle_ocr_payload)\n\nresults = run_tuner(\n    trainable=trainable,\n    search_space=PADDLE_OCR_SEARCH_SPACE,\n    num_samples=128,\n    num_workers=len(healthy),\n)"
   },
   {
    "cell_type": "code",

src/raytune_ocr.py

@@ -12,7 +12,7 @@ import requests
 import pandas as pd
 
 import ray
-from ray import tune, train
+from ray import tune
 from ray.tune.search.optuna import OptunaSearch
 
 
@@ -65,11 +65,15 @@ def create_trainable(ports: List[int], payload_fn: Callable[[Dict], Dict]) -> Ca
 
     Returns:
         Trainable function for Ray Tune
+
+    Note:
+        Ray Tune 2.x API: tune.report(metrics_dict) - pass dict directly, NOT kwargs.
+        See: https://docs.ray.io/en/latest/tune/api/doc/ray.tune.report.html
     """
     def trainable(config):
         import random
         import requests
-        from ray import train
+        from ray.tune import report  # Ray 2.x: report(dict), not report(**kwargs)
 
         api_url = f"http://localhost:{random.choice(ports)}"
         payload = payload_fn(config)
@@ -79,9 +83,9 @@ def create_trainable(ports: List[int], payload_fn: Callable[[Dict], Dict]) -> Ca
             response.raise_for_status()
             metrics = response.json()
             metrics["worker"] = api_url
-            train.report(metrics)
+            report(metrics)  # Ray 2.x API: pass dict directly
         except Exception as e:
-            train.report({
+            report({  # Ray 2.x API: pass dict directly
                 "CER": 1.0,
                 "WER": 1.0,
                 "TIME": 0.0,
@@ -116,7 +120,12 @@ def run_tuner(
     Returns:
         Ray Tune ResultGrid
     """
-    ray.init(ignore_reinit_error=True, include_dashboard=False)
+    ray.init(
+        ignore_reinit_error=True,
+        include_dashboard=False,
+        configure_logging=False,
+        _metrics_export_port=0,  # Disable metrics export to avoid connection warnings
+    )
     print(f"Ray Tune ready (version: {ray.__version__})")
 
     tuner = tune.Tuner(