def main()

in bayesmark/experiment_launcher.py [0:0]

• 30 lines of code
• 10 McCabe index (conditional complexity)

def main():
    """See README for instructions on calling launcher.
    """
    description = "Launch series of studies across functions and optimizers"
    args = cmd.parse_args(cmd.launcher_parser(description))

    logger.setLevel(logging.INFO)  # Note this is the module-wide logger
    if args[CmdArgs.verbose]:
        logger.addHandler(logging.StreamHandler())

    # Get optimizer settings, says which file to call for each optimizer
    settings = cmd.load_optimizer_settings(args[CmdArgs.optimizer_root])
    opt_file_lookup = {optimizer: wrapper_file for optimizer, (wrapper_file, _) in settings.items()}

    # Setup uuid
    if args[CmdArgs.uuid] is None:
        args[CmdArgs.uuid] = pyuuid.uuid4().hex  # debatable if uuid1 or uuid4 is better here
    else:
        warnings.warn(
            "User UUID supplied. This is only desired for debugging. Careless use could lead to study id conflicts.",
            UserWarning,
        )
    run_uuid = pyuuid.UUID(hex=args[CmdArgs.uuid])
    assert run_uuid.hex == args[CmdArgs.uuid]
    logger.info("Supply --uuid %s to reproduce this run." % run_uuid.hex)

    # Log all the options
    print("Launcher options (JSON):\n")
    print(json.dumps({"bayesmark-launch-args": cmd.serializable_dict(args)}))
    print("\n")

    # Set the master seed (derive from the uuid we just setup)
    pyrandom.seed(run_uuid.int)
    np.random.seed(random_seed(pyrandom))

    # Now run it, either to dry run file or executes sub-processes
    if args[CmdArgs.dry_run]:
        with absopen(args[CmdArgs.jobs_file], "w") as fp:
            dry_run(args, opt_file_lookup, run_uuid, fp)
    else:
        timeout = args[CmdArgs.timeout] if args[CmdArgs.timeout] > 0 else None
        real_run(args, opt_file_lookup, run_uuid, timeout)

    logger.info("done")