remove unneded py file

src/paddle_ocr_raytune_rest.ipynb

@@ -188,62 +188,7 @@
    "id": "trainable",
    "metadata": {},
    "outputs": [],
-   "source": [
-    "def trainable_paddle_ocr(config):\n",
-    "    \"\"\"Call PaddleOCR REST API with the given hyperparameter config.\n",
-    "    \n",
-    "    Uses trial index to deterministically assign a worker (round-robin),\n",
-    "    ensuring only 1 request per container at a time.\n",
-    "    \"\"\"\n",
-    "    import requests  # Must be inside function for Ray workers\n",
-    "    from ray import train\n",
-    "\n",
-    "    # Worker URLs - round-robin assignment based on trial index\n",
-    "    WORKER_PORTS = [8001, 8002]\n",
-    "    NUM_WORKERS = len(WORKER_PORTS)\n",
-    "    \n",
-    "    # Get trial context for deterministic worker assignment\n",
-    "    context = train.get_context()\n",
-    "    trial_id = context.get_trial_id() if context else \"0\"\n",
-    "    # Extract numeric part from trial ID (e.g., \"trainable_paddle_ocr_abc123_00001\" -> 1)\n",
-    "    try:\n",
-    "        trial_num = int(trial_id.split(\"_\")[-1])\n",
-    "    except (ValueError, IndexError):\n",
-    "        trial_num = hash(trial_id)\n",
-    "    \n",
-    "    worker_idx = trial_num % NUM_WORKERS\n",
-    "    api_url = f\"http://localhost:{WORKER_PORTS[worker_idx]}\"\n",
-    "\n",
-    "    payload = {\n",
-    "        \"pdf_folder\": \"/app/dataset\",\n",
-    "        \"use_doc_orientation_classify\": config.get(\"use_doc_orientation_classify\", False),\n",
-    "        \"use_doc_unwarping\": config.get(\"use_doc_unwarping\", False),\n",
-    "        \"textline_orientation\": config.get(\"textline_orientation\", True),\n",
-    "        \"text_det_thresh\": config.get(\"text_det_thresh\", 0.0),\n",
-    "        \"text_det_box_thresh\": config.get(\"text_det_box_thresh\", 0.0),\n",
-    "        \"text_det_unclip_ratio\": config.get(\"text_det_unclip_ratio\", 1.5),\n",
-    "        \"text_rec_score_thresh\": config.get(\"text_rec_score_thresh\", 0.0),\n",
-    "        \"start_page\": 5,\n",
-    "        \"end_page\": 10,\n",
-    "    }\n",
-    "\n",
-    "    try:\n",
-    "        response = requests.post(f\"{api_url}/evaluate\", json=payload, timeout=None)  # No timeout\n",
-    "        response.raise_for_status()\n",
-    "        metrics = response.json()\n",
-    "        metrics[\"worker\"] = api_url\n",
-    "        train.report(metrics)\n",
-    "    except Exception as e:\n",
-    "        train.report({\n",
-    "            \"CER\": 1.0,\n",
-    "            \"WER\": 1.0,\n",
-    "            \"TIME\": 0.0,\n",
-    "            \"PAGES\": 0,\n",
-    "            \"TIME_PER_PAGE\": 0,\n",
-    "            \"worker\": api_url,\n",
-    "            \"ERROR\": str(e)[:500]\n",
-    "        })"
-   ]
+   "source": "def trainable_paddle_ocr(config):\n    \"\"\"Call PaddleOCR REST API with the given hyperparameter config.\"\"\"\n    import random\n    import requests\n    from ray import tune\n\n    # Worker URLs - random selection (load balances with 2 workers, 2 concurrent trials)\n    WORKER_PORTS = [8001, 8002]\n    api_url = f\"http://localhost:{random.choice(WORKER_PORTS)}\"\n\n    payload = {\n        \"pdf_folder\": \"/app/dataset\",\n        \"use_doc_orientation_classify\": config.get(\"use_doc_orientation_classify\", False),\n        \"use_doc_unwarping\": config.get(\"use_doc_unwarping\", False),\n        \"textline_orientation\": config.get(\"textline_orientation\", True),\n        \"text_det_thresh\": config.get(\"text_det_thresh\", 0.0),\n        \"text_det_box_thresh\": config.get(\"text_det_box_thresh\", 0.0),\n        \"text_det_unclip_ratio\": config.get(\"text_det_unclip_ratio\", 1.5),\n        \"text_rec_score_thresh\": config.get(\"text_rec_score_thresh\", 0.0),\n        \"start_page\": 5,\n        \"end_page\": 10,\n    }\n\n    try:\n        response = requests.post(f\"{api_url}/evaluate\", json=payload, timeout=None)\n        response.raise_for_status()\n        metrics = response.json()\n        metrics[\"worker\"] = api_url\n        tune.report(**metrics)\n    except Exception as e:\n        tune.report(\n            CER=1.0,\n            WER=1.0,\n            TIME=0.0,\n            PAGES=0,\n            TIME_PER_PAGE=0,\n            worker=api_url,\n            ERROR=str(e)[:500]\n        )"
   },
   {
    "cell_type": "markdown",
@@ -390,4 +335,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 5
-}
+}

src/run_raytune.py

@@ -1,124 +0,0 @@
-#!/usr/bin/env python3
-"""Ray Tune hyperparameter search for PaddleOCR via REST API."""
-
-import os
-from datetime import datetime
-
-import requests
-import pandas as pd
-
-import ray
-from ray import tune, train, air
-from ray.tune.search.optuna import OptunaSearch
-
-# Configuration
-WORKER_PORTS = [8001, 8002]
-OUTPUT_FOLDER = "results"
-os.makedirs(OUTPUT_FOLDER, exist_ok=True)
-
-# Search space
-search_space = {
-    "use_doc_orientation_classify": tune.choice([True, False]),
-    "use_doc_unwarping": tune.choice([True, False]),
-    "textline_orientation": tune.choice([True, False]),
-    "text_det_thresh": tune.uniform(0.0, 0.7),
-    "text_det_box_thresh": tune.uniform(0.0, 0.7),
-    "text_det_unclip_ratio": tune.choice([0.0]),
-    "text_rec_score_thresh": tune.uniform(0.0, 0.7),
-}
-
-
-def trainable_paddle_ocr(config):
-    """Call PaddleOCR REST API with the given hyperparameter config."""
-    import requests
-    from ray import train
-
-    NUM_WORKERS = len(WORKER_PORTS)
-
-    context = train.get_context()
-    trial_id = context.get_trial_id() if context else "0"
-    try:
-        trial_num = int(trial_id.split("_")[-1])
-    except (ValueError, IndexError):
-        trial_num = hash(trial_id)
-
-    worker_idx = trial_num % NUM_WORKERS
-    api_url = f"http://localhost:{WORKER_PORTS[worker_idx]}"
-
-    payload = {
-        "pdf_folder": "/app/dataset",
-        "use_doc_orientation_classify": config.get("use_doc_orientation_classify", False),
-        "use_doc_unwarping": config.get("use_doc_unwarping", False),
-        "textline_orientation": config.get("textline_orientation", True),
-        "text_det_thresh": config.get("text_det_thresh", 0.0),
-        "text_det_box_thresh": config.get("text_det_box_thresh", 0.0),
-        "text_det_unclip_ratio": config.get("text_det_unclip_ratio", 1.5),
-        "text_rec_score_thresh": config.get("text_rec_score_thresh", 0.0),
-        "start_page": 5,
-        "end_page": 10,
-    }
-
-    try:
-        response = requests.post(f"{api_url}/evaluate", json=payload, timeout=None)
-        response.raise_for_status()
-        metrics = response.json()
-        metrics["worker"] = api_url
-        train.report(metrics)
-    except Exception as e:
-        train.report({
-            "CER": 1.0,
-            "WER": 1.0,
-            "TIME": 0.0,
-            "PAGES": 0,
-            "TIME_PER_PAGE": 0,
-            "worker": api_url,
-            "ERROR": str(e)[:500]
-        })
-
-
-def main():
-    # Check workers
-    print("Checking workers...")
-    for port in WORKER_PORTS:
-        try:
-            r = requests.get(f"http://localhost:{port}/health", timeout=10)
-            print(f"  Worker {port}: {r.json().get('status', 'unknown')}")
-        except Exception as e:
-            print(f"  Worker {port}: ERROR - {e}")
-
-    print("\nStarting Ray Tune...")
-    ray.init(ignore_reinit_error=True)
-
-    tuner = tune.Tuner(
-        trainable_paddle_ocr,
-        tune_config=tune.TuneConfig(
-            metric="CER",
-            mode="min",
-            search_alg=OptunaSearch(),
-            num_samples=64,
-            max_concurrent_trials=len(WORKER_PORTS),
-        ),
-        run_config=air.RunConfig(verbose=2, log_to_file=True),
-        param_space=search_space,
-    )
-
-    results = tuner.fit()
-
-    # Save results
-    df = results.get_dataframe()
-    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
-    filepath = os.path.join(OUTPUT_FOLDER, f"raytune_paddle_results_{timestamp}.csv")
-    df.to_csv(filepath, index=False)
-    print(f"\nResults saved: {filepath}")
-
-    # Best config
-    if len(df) > 0 and "CER" in df.columns:
-        best = df.loc[df["CER"].idxmin()]
-        print(f"\nBest CER: {best['CER']:.6f}")
-        print(f"Best WER: {best['WER']:.6f}")
-
-    ray.shutdown()
-
-
-if __name__ == "__main__":
-    main()