feat: NCS-41 Bot Platform (#33)
Co-authored-by: Janis <janis@nowchess.de> Reviewed-on: #33 Co-authored-by: Janis <janis.e.20@gmx.de> Co-committed-by: Janis <janis.e.20@gmx.de>
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#!/usr/bin/env python3
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"""Generate random chess positions for NNUE training with multiprocessing."""
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import chess
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import random
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import sys
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from pathlib import Path
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from tqdm import tqdm
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from multiprocessing import Pool, Queue
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from datetime import datetime
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import time
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def _worker_generate_games(worker_id, games_per_worker, samples_per_game, min_move, max_move):
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"""Generate games for one worker.
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Returns:
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list of FENs generated by this worker
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"""
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positions = []
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for game_num in range(games_per_worker):
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board = chess.Board()
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move_history = []
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# Play a complete random game
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while not board.is_game_over() and len(move_history) < 200:
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legal_moves = list(board.legal_moves)
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if not legal_moves:
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break
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move = random.choice(legal_moves)
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board.push(move)
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move_history.append(board.copy())
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# Determine the range of moves to sample from
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game_length = len(move_history)
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valid_start = max(min_move, 0)
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valid_end = min(max_move, game_length)
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if valid_start >= valid_end:
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continue
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# Randomly sample positions from this game
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sample_count = min(samples_per_game, valid_end - valid_start)
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if sample_count > 0:
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sample_indices = random.sample(
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range(valid_start, valid_end),
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k=sample_count
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)
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for idx in sample_indices:
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sampled_board = move_history[idx]
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# Only filter truly invalid or terminal positions
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if not sampled_board.is_valid() or sampled_board.is_game_over():
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continue
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# Save position (include check, captures, all positions)
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fen = sampled_board.fen()
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positions.append(fen)
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return positions
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def play_random_game_and_collect_positions(
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output_file,
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total_positions=3000000,
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samples_per_game=1,
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min_move=1,
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max_move=50,
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num_workers=8
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):
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"""Generate positions using multiprocessing with multiple workers.
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Args:
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output_file: Output file for positions
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total_positions: Target number of positions to generate
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samples_per_game: Number of positions to sample per game (1-N)
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min_move: Minimum move number to start sampling from
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max_move: Maximum move number for sampling
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num_workers: Number of parallel worker processes
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Returns:
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Number of valid positions saved
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"""
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# Estimate games needed (roughly 1 position per game on average)
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total_games = max(total_positions // samples_per_game, num_workers)
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games_per_worker = total_games // num_workers
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print(f"Generating {total_positions:,} positions using {num_workers} workers")
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print(f"Total games: ~{total_games:,} ({games_per_worker:,} per worker)")
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print()
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start_time = datetime.now()
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# Generate positions in parallel
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worker_tasks = [
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(i, games_per_worker, samples_per_game, min_move, max_move)
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for i in range(num_workers)
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]
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positions_count = 0
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all_positions = []
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with Pool(num_workers) as pool:
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with tqdm(total=num_workers, desc="Workers generating games") as pbar:
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for positions in pool.starmap(_worker_generate_games, worker_tasks):
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all_positions.extend(positions)
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positions_count += len(positions)
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pbar.update(1)
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# Write all positions to file
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print(f"Writing {positions_count:,} positions to {output_file}...")
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with open(output_file, 'w') as f:
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for fen in all_positions:
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f.write(fen + '\n')
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elapsed_time = datetime.now() - start_time
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elapsed_seconds = elapsed_time.total_seconds()
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positions_per_second = positions_count / elapsed_seconds if elapsed_seconds > 0 else 0
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# Print summary
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print()
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print("=" * 60)
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print("POSITION GENERATION SUMMARY")
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print("=" * 60)
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print(f"Target positions: {total_positions:,}")
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print(f"Actual positions saved: {positions_count:,}")
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print(f"Workers: {num_workers}")
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print(f"Games per worker: {games_per_worker:,}")
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print(f"Samples per game: {samples_per_game}")
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print(f"Move range: {min_move}-{max_move}")
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print(f"Elapsed time: {elapsed_time}")
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print(f"Throughput: {positions_per_second:.0f} positions/second")
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print("=" * 60)
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print()
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if positions_count == 0:
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print("WARNING: No valid positions were generated!")
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return 0
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return positions_count
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if __name__ == "__main__":
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import argparse
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parser = argparse.ArgumentParser(description="Generate random chess positions for NNUE training")
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parser.add_argument("output_file", nargs="?", default="positions.txt",
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help="Output file for positions (default: positions.txt)")
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parser.add_argument("--positions", type=int, default=3000000,
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help="Target number of positions to generate (default: 3000000)")
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parser.add_argument("--samples-per-game", type=int, default=1,
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help="Number of positions to sample per game (default: 1)")
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parser.add_argument("--min-move", type=int, default=1,
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help="Minimum move number to sample from (default: 1)")
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parser.add_argument("--max-move", type=int, default=50,
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help="Maximum move number to sample from (default: 50)")
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parser.add_argument("--workers", type=int, default=8,
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help="Number of parallel worker processes (default: 8)")
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args = parser.parse_args()
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count = play_random_game_and_collect_positions(
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output_file=args.output_file,
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total_positions=args.positions,
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samples_per_game=args.samples_per_game,
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min_move=args.min_move,
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max_move=args.max_move,
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num_workers=args.workers
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)
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sys.exit(0 if count > 0 else 1)
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