fix(official-bots): prevent Colab OOM in NNUE training

Dense 98304-dim HalfKP features at batch_size=16384 cost ~6.4 GB/batch on the
host; with 8 hardcoded DataLoader workers and prefetch this OOM-killed the Colab
runtime.

- train.py: adaptive DataLoader workers (min(4, cpu_count), Colab free tier = 2),
  overridable via NNUE_LOADER_WORKERS; persistent_workers only when > 0.
- NNUETraining.ipynb: lower BATCH_SIZE 16384 -> 4096 with a memory-cost note.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

modules/official-bots/python/NNUETraining.ipynb

@@ -92,28 +92,7 @@
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": [
+   "source": "from train import train_nnue, burst_train, DEFAULT_HIDDEN_SIZES\n\nWEIGHTS_DIR = Path(DRIVE_ROOT) / 'weights'\nWEIGHTS_DIR.mkdir(parents=True, exist_ok=True)\nOUTPUT_FILE = str(WEIGHTS_DIR / 'nnue_weights.pt')\n\n# ── Training hyperparameters ──────────────────────────────────────────────────\nHIDDEN_SIZES      = DEFAULT_HIDDEN_SIZES\n# fen_to_features builds a DENSE 98304-dim input, so a batch costs\n# batch_size * 98304 * 4 bytes on the host (× DataLoader prefetch). On Colab's\n# ~12 GB RAM keep this small; raise it only if you have headroom.\nBATCH_SIZE        = 4096\nEPOCHS            = 100\nEARLY_STOPPING    = 10                     # None to disable\nSUBSAMPLE_RATIO   = 1.0\n\n# Resume from latest checkpoint if one exists\ncheckpoints = sorted(WEIGHTS_DIR.glob('nnue_weights_v*.pt'))\nCHECKPOINT = str(checkpoints[-1]) if checkpoints else None\nif CHECKPOINT:\n    print(f'Resuming from checkpoint: {CHECKPOINT}')\nelse:\n    print('Starting training from scratch.')",
-    "from train import train_nnue, burst_train, DEFAULT_HIDDEN_SIZES\n",
-    "\n",
-    "WEIGHTS_DIR = Path(DRIVE_ROOT) / 'weights'\n",
-    "WEIGHTS_DIR.mkdir(parents=True, exist_ok=True)\n",
-    "OUTPUT_FILE = str(WEIGHTS_DIR / 'nnue_weights.pt')\n",
-    "\n",
-    "# ── Training hyperparameters ──────────────────────────────────────────────────\n",
-    "HIDDEN_SIZES      = DEFAULT_HIDDEN_SIZES   # [1536, 1024, 512, 256]\n",
-    "BATCH_SIZE        = 16384\n",
-    "EPOCHS            = 100\n",
-    "EARLY_STOPPING    = 10                     # None to disable\n",
-    "SUBSAMPLE_RATIO   = 1.0\n",
-    "\n",
-    "# Resume from latest checkpoint if one exists\n",
-    "checkpoints = sorted(WEIGHTS_DIR.glob('nnue_weights_v*.pt'))\n",
-    "CHECKPOINT = str(checkpoints[-1]) if checkpoints else None\n",
-    "if CHECKPOINT:\n",
-    "    print(f'Resuming from checkpoint: {CHECKPOINT}')\n",
-    "else:\n",
-    "    print('Starting training from scratch.')"
-   ],
    "id": "train-config"
   },
   {

modules/official-bots/python/src/train.py

@@ -13,6 +13,11 @@ import chess
 from datetime import datetime, timedelta
 import re
 import numpy as np
+import os
+
+# DataLoader workers: cap to the machine's CPUs (Colab free tier = 2). Too many
+# workers each fork the dataset and OOM-kill the runtime.
+LOADER_WORKERS = int(os.environ.get("NNUE_LOADER_WORKERS", min(4, os.cpu_count() or 2)))
 
 
 def _shard_files(data_file):
@@ -256,17 +261,17 @@ def _setup_training(data_file, batch_size, subsample_ratio):
         train_dataset,
         batch_size=batch_size,
         sampler=train_sampler,
-        num_workers=8,
+        num_workers=LOADER_WORKERS,
         pin_memory=True,
-        persistent_workers=True
+        persistent_workers=LOADER_WORKERS > 0
     )
     val_loader = DataLoader(
         val_dataset,
         batch_size=batch_size,
         shuffle=False,
-        num_workers=8,
+        num_workers=LOADER_WORKERS,
         pin_memory=True,
-        persistent_workers=True
+        persistent_workers=LOADER_WORKERS > 0
     )
 
     return device, dataset, train_dataset, val_dataset, train_loader, val_loader, num_positions