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/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