Janis Eccarius
378 lines
12 KiB
Plaintext
378 lines
12 KiB
Plaintext
{
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"nbformat": 4,
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"nbformat_minor": 5,
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"name": "python",
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"version": "3.10.0"
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},
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"colab": {
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"provenance": [],
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"gpuType": "T4"
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},
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"accelerator": "GPU"
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},
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# NNUE Training Pipeline\n",
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"\n",
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"End-to-end notebook: data generation → Stockfish labeling → training → `.nbai` export.\n",
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"\n",
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"**Runtime:** GPU (T4 or better). Runtime → Change runtime type → T4 GPU.\n",
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"\n",
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"**Persistence:** Checkpoints and datasets are saved to Google Drive so training can resume after session timeout."
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],
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"id": "intro-md"
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"---\n",
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"## ⚙️ 1 — Setup"
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],
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"id": "setup-md"
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Mount Google Drive for checkpoint persistence\n",
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"from google.colab import drive\n",
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"drive.mount('/content/drive')"
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],
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"id": "mount-drive"
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"\n",
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"# ── Configure these paths once ───────────────────────────────────────────────\n",
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"REPO_URL = 'https://git.janis-eccarius.de/NowChess/NowChessSystems.git'\n",
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"DRIVE_ROOT = '/content/drive/MyDrive/NowChess'\n",
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"REPO_DIR = f'{DRIVE_ROOT}/NowChessSystems'\n",
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"PYTHON_DIR = f'{REPO_DIR}/modules/official-bots/python'\n",
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"# ─────────────────────────────────────────────────────────────────────────────\n",
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"\n",
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"os.makedirs(DRIVE_ROOT, exist_ok=True)\n",
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"\n",
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"if not os.path.isdir(REPO_DIR):\n",
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" !git clone --depth=1 \"{REPO_URL}\" \"{REPO_DIR}\"\n",
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" print('Repo cloned to Drive.')\n",
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"else:\n",
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" !git -C \"{REPO_DIR}\" pull --ff-only\n",
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" print('Repo updated.')"
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],
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"id": "clone-repo"
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Install Python dependencies\n",
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"!pip install -q chess tqdm rich zstandard\n",
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"\n",
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"# Stockfish for position labeling\n",
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"!apt-get install -q -y stockfish\n",
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"import shutil\n",
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"STOCKFISH_PATH = shutil.which('stockfish') or '/usr/games/stockfish'\n",
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"print(f'Stockfish: {STOCKFISH_PATH}')\n",
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"\n",
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"# Add pipeline source to path\n",
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"import sys\n",
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"sys.path.insert(0, f'{PYTHON_DIR}/src')\n",
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"sys.path.insert(0, PYTHON_DIR)\n",
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"print('Python path configured.')"
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],
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"id": "install-deps"
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"---\n",
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"## 🗄️ 2 — Data\n",
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"\n",
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"Choose **one** of the two options below:\n",
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"- **Option A** — generate FEN positions with random play, then label them with Stockfish.\n",
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"- **Option B** — upload an existing `labeled.jsonl` from your machine or Drive."
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],
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"id": "data-md"
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from pathlib import Path\n",
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"\n",
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"# Paths (all on Drive so they survive session restarts)\n",
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"DATA_DIR = Path(DRIVE_ROOT) / 'training_data'\n",
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"DATA_DIR.mkdir(parents=True, exist_ok=True)\n",
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"POSITIONS_FILE = DATA_DIR / 'positions.txt' # raw FENs\n",
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"LABELED_FILE = DATA_DIR / 'labeled.jsonl' # FEN + eval pairs\n",
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"\n",
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"print(f'Data directory: {DATA_DIR}')"
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],
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"id": "data-paths"
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# ── Option A: Generate + label ────────────────────────────────────────────────\n",
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"# Adjust NUM_POSITIONS to taste. 50 000 trains in ~10 min on T4;\n",
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"# 200 000+ gives better generalisation.\n",
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"NUM_POSITIONS = 50_000\n",
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"STOCKFISH_DEPTH = 12\n",
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"LABEL_WORKERS = 4 # parallel Stockfish processes\n",
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"MIN_MOVE = 5 # skip opening book moves\n",
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"MAX_MOVE = 60\n",
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"\n",
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"from generate import play_random_game_and_collect_positions\n",
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"from label import label_positions_with_stockfish\n",
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"\n",
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"print(f'Generating {NUM_POSITIONS:,} positions...')\n",
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"count = play_random_game_and_collect_positions(\n",
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" str(POSITIONS_FILE),\n",
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" total_positions=NUM_POSITIONS,\n",
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" samples_per_game=1,\n",
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" min_move=MIN_MOVE,\n",
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" max_move=MAX_MOVE,\n",
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" num_workers=4,\n",
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")\n",
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"print(f'{count:,} positions written to {POSITIONS_FILE}')\n",
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"\n",
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"print('Labeling with Stockfish (this is the slow step)...')\n",
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"ok = label_positions_with_stockfish(\n",
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" str(POSITIONS_FILE),\n",
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" str(LABELED_FILE),\n",
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" STOCKFISH_PATH,\n",
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" depth=STOCKFISH_DEPTH,\n",
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" num_workers=LABEL_WORKERS,\n",
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")\n",
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"if ok:\n",
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" print(f'Labeled dataset saved: {LABELED_FILE}')\n",
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"else:\n",
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" print('ERROR: labeling failed')"
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],
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"id": "option-a-generate"
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# ── Option B: Upload existing labeled.jsonl ───────────────────────────────────\n",
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"# Run this cell instead of Option A if you already have a labeled dataset.\n",
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"#\n",
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"# To upload from local machine:\n",
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"# from google.colab import files\n",
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"# uploaded = files.upload() # pick your labeled.jsonl\n",
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"# import shutil, os\n",
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"# shutil.move(next(iter(uploaded)), str(LABELED_FILE))\n",
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"#\n",
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"# Or copy from Drive:\n",
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"# import shutil\n",
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"# shutil.copy('/content/drive/MyDrive/path/to/labeled.jsonl', str(LABELED_FILE))\n",
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"\n",
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"import os\n",
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"if LABELED_FILE.exists():\n",
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" lines = sum(1 for _ in open(LABELED_FILE))\n",
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" print(f'Ready: {lines:,} labeled positions at {LABELED_FILE}')\n",
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"else:\n",
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" print('No labeled.jsonl found — run Option A first or upload one.')"
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],
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"id": "option-b-upload"
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"---\n",
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"## 🏋️ 3 — Train\n",
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"\n",
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"Standard training runs a fixed number of epochs. \n",
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"**Burst mode** is better for Colab: it repeatedly restarts from the best checkpoint within a time budget, surviving session disconnects gracefully."
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],
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"id": "train-md"
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from train import train_nnue, burst_train, DEFAULT_HIDDEN_SIZES\n",
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"\n",
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"WEIGHTS_DIR = Path(DRIVE_ROOT) / 'weights'\n",
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"WEIGHTS_DIR.mkdir(parents=True, exist_ok=True)\n",
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"OUTPUT_FILE = str(WEIGHTS_DIR / 'nnue_weights.pt')\n",
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"\n",
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"# ── Training hyperparameters ──────────────────────────────────────────────────\n",
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"HIDDEN_SIZES = DEFAULT_HIDDEN_SIZES # [1536, 1024, 512, 256]\n",
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"BATCH_SIZE = 16384\n",
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"EPOCHS = 100\n",
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"EARLY_STOPPING = 10 # None to disable\n",
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"SUBSAMPLE_RATIO = 1.0\n",
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"\n",
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"# Resume from latest checkpoint if one exists\n",
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"checkpoints = sorted(WEIGHTS_DIR.glob('nnue_weights_v*.pt'))\n",
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"CHECKPOINT = str(checkpoints[-1]) if checkpoints else None\n",
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"if CHECKPOINT:\n",
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" print(f'Resuming from checkpoint: {CHECKPOINT}')\n",
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"else:\n",
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" print('Starting training from scratch.')"
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],
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"id": "train-config"
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# ── Standard training ─────────────────────────────────────────────────────────\n",
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"# Use this when you have a reliable long-running session.\n",
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"\n",
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"train_nnue(\n",
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" data_file=str(LABELED_FILE),\n",
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" output_file=OUTPUT_FILE,\n",
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" epochs=EPOCHS,\n",
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" batch_size=BATCH_SIZE,\n",
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" checkpoint=CHECKPOINT,\n",
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" use_versioning=True,\n",
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" early_stopping_patience=EARLY_STOPPING,\n",
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" subsample_ratio=SUBSAMPLE_RATIO,\n",
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" hidden_sizes=HIDDEN_SIZES,\n",
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")"
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],
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"id": "standard-train"
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# ── Burst training (recommended for Colab free tier) ─────────────────────────\n",
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"# Restarts from the global best each time early stopping fires.\n",
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"# Set BURST_MINUTES to slightly less than the Colab session limit (~70 min).\n",
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"\n",
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"BURST_MINUTES = 70\n",
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"EPOCHS_PER_SEASON = 30\n",
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"BURST_PATIENCE = 8\n",
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"\n",
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"burst_train(\n",
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" data_file=str(LABELED_FILE),\n",
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" output_file=OUTPUT_FILE,\n",
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" duration_minutes=BURST_MINUTES,\n",
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" epochs_per_season=EPOCHS_PER_SEASON,\n",
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" early_stopping_patience=BURST_PATIENCE,\n",
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" batch_size=BATCH_SIZE,\n",
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" initial_checkpoint=CHECKPOINT,\n",
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" use_versioning=True,\n",
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" subsample_ratio=SUBSAMPLE_RATIO,\n",
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" hidden_sizes=HIDDEN_SIZES,\n",
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")"
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],
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"id": "burst-train"
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"---\n",
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"## 📦 4 — Export\n",
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"\n",
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"Convert the best `.pt` checkpoint to the `.nbai` binary format read by `NbaiLoader` in Scala."
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],
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"id": "export-md"
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from export import export_to_nbai\n",
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"\n",
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"NBAI_FILE = Path(DRIVE_ROOT) / 'nnue_weights.nbai'\n",
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"\n",
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"# Pick the latest versioned checkpoint\n",
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"checkpoints = sorted(WEIGHTS_DIR.glob('nnue_weights_v*.pt'))\n",
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"if not checkpoints:\n",
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" raise FileNotFoundError('No checkpoints found in ' + str(WEIGHTS_DIR))\n",
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"\n",
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"latest = checkpoints[-1]\n",
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"print(f'Exporting {latest.name} → {NBAI_FILE.name}')\n",
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"\n",
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"export_to_nbai(\n",
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" weights_file=str(latest),\n",
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" output_file=str(NBAI_FILE),\n",
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" trained_by='colab',\n",
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")\n",
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"print('Export complete.')"
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],
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"id": "export-cell"
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"---\n",
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"## ⬇️ 5 — Download\n",
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"\n",
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"Download the `.nbai` weights file and the latest `.pt` checkpoint to your local machine.\n",
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"\n",
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"Place `nnue_weights.nbai` in `modules/official-bots/src/main/resources/` and rebuild the native image."
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],
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"id": "download-md"
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from google.colab import files\n",
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"\n",
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"if NBAI_FILE.exists():\n",
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" files.download(str(NBAI_FILE))\n",
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" print(f'Downloading {NBAI_FILE.name}')\n",
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"else:\n",
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" print('No .nbai file found — run the Export cell first.')\n",
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"\n",
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"checkpoints = sorted(WEIGHTS_DIR.glob('nnue_weights_v*.pt'))\n",
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"if checkpoints:\n",
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" latest = checkpoints[-1]\n",
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" files.download(str(latest))\n",
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" print(f'Downloading checkpoint {latest.name}')\n",
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"else:\n",
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" print('No .pt checkpoint found.')"
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],
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"id": "download-cell"
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}
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]
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}
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