feat(official-bots): standalone self-play + one-shot dataset builder for NNUE training

Add an easy local data pipeline feeding GPU training on Colab.

- SelfPlayMain: standalone NNUEBot self-play (no microservices) writing FENs
  for labeling; randomised openings for game diversity, sequential due to the
  shared EvaluationNNUE accumulator. Exposed via the `selfPlay` Gradle task and
  selfplay.sh.
- NNUEBot: optional fixedMoveTimeMs so self-play runs fast (default unchanged).
- NbaiLoader: honor `-Dnnue.weights=<path>` to load weights from a file before
  falling back to the bundled resource.
- build_dataset.py / dataset.sh: one command builds the entire dataset
  (Lichess eval-DB backbone + self-play + tactical + random filler), dedups,
  balances the eval histogram, writes append-only zstd shards + manifest, and
  rclone-pushes to Drive.
- train.py: NNUEDataset reads a directory of .jsonl.zst shards (streaming) in
  addition to a single file.
- NNUETraining.ipynb: clone to ephemeral /content, sync shards from Drive
  (cache-aware), train on the shards dir; removed Colab generation/upload steps.
- Concept + implementation plan docs.

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

modules/official-bots/python/dataset.sh

@@ -0,0 +1,17 @@
+#!/usr/bin/env bash
+# Build the ENTIRE NNUE training dataset + push to Drive. One command.
+#
+#   ./dataset.sh                 # build everything + rclone push
+#   ./dataset.sh --no-push       # build only
+#   ./dataset.sh --no-lichess    # skip the large Lichess backbone
+set -euo pipefail
+
+SCRIPT_DIR="$(cd "$(dirname "$0")" && pwd)"
+cd "$SCRIPT_DIR"
+
+PY="python3"
+if [[ -x "$SCRIPT_DIR/.venv/bin/python" ]]; then
+  PY="$SCRIPT_DIR/.venv/bin/python"
+fi
+
+exec "$PY" build_dataset.py "$@"