async def evaluate_model(eval_model_path, target_model_path, sgf_dir, seed):
  """Evaluate one model against a target.

  Args:
    eval_model_path: the path to the model to evaluate.
    target_model_path: the path to the model to compare to.
    sgf_dif: directory path to write SGF output to.
    seed: random seed to use when running eval.

  Returns:
    The win-rate of eval_model against target_model in the range [0, 1].
  """

  lines = await run(
      'bazel-bin/cc/eval',
      '--flagfile={}'.format(os.path.join(FLAGS.flags_dir, 'eval.flags')),
      '--model={}'.format(eval_model_path),
      '--model_two={}'.format(target_model_path),
      '--sgf_dir={}'.format(sgf_dir),
      '--seed={}'.format(seed))
  result = '\n'.join(lines[-7:])
  logging.info(result)
  eval_stats, target_stats = parse_win_stats_table(result, 2)
  num_games = eval_stats.total_wins + target_stats.total_wins
  win_rate = eval_stats.total_wins / num_games
  logging.info('Win rate %s vs %s: %.3f', eval_stats.model_name,
               target_stats.model_name, win_rate)
  return win_rate
async def evaluate_model ( eval_model_path , target_model_path , sgf_dir , seed ) : lines = await run ( 'bazel-bin/cc/eval' , '--flagfile={}' . format ( os . path . join ( FLAGS . flags_dir , 'eval.flags' ) ) , '--model={}' . format ( eval_model_path ) , '--model_two={}' . format ( target_model_path ) , '--sgf_dir={}' . format ( sgf_dir ) , '--seed={}' . format ( seed ) ) result = '\n' . join ( lines [ - 7 : ] ) logging . info ( result ) eval_stats , target_stats = parse_win_stats_table ( result , 2 ) num_games = eval_stats . total_wins + target_stats . total_wins win_rate = eval_stats . total_wins / num_games logging . info ( 'Win rate %s vs %s: %.3f' , eval_stats . model_name , target_stats . model_name , win_rate ) return win_rate
Evaluate one model against a target.

  Args:
    eval_model_path: the path to the model to evaluate.
    target_model_path: the path to the model to compare to.
    sgf_dif: directory path to write SGF output to.
    seed: random seed to use when running eval.

  Returns:
    The win-rate of eval_model against target_model in the range [0, 1].