def install(h, pip, groups, save, save_dev, packages):
    """Install a pip or bower package."""
    if groups:
        groups = [text_type.strip(group) for group in groups.split(",")]
    else:
        groups = h.requirements.keys()

    if not packages:
        for group in groups:
            if group not in h.requirements:
                warning("{} not in requirements".format(group))
                continue
            install = (h.install_bower if group.startswith("bower")
                       else h.install_pip)
            for requirement in h.requirements[group]:
                install(str(requirement), save=False, save_dev=False)
    if pip:
        for package in packages:
            h.install_pip(package, save=save, save_dev=save_dev)
    else:
        for package in packages:
            h.install_bower(package, save=save, save_dev=save_dev)
def recover_session(self,
                      master,
                      saver=None,
                      checkpoint_dir=None,
                      checkpoint_filename_with_path=None,
                      wait_for_checkpoint=False,
                      max_wait_secs=7200,
                      config=None):
    """Creates a `Session`, recovering if possible.

    Creates a new session on 'master'.  If the session is not initialized
    and can be recovered from a checkpoint, recover it.

    Args:
      master: `String` representation of the TensorFlow master to use.
      saver: A `Saver` object used to restore a model.
      checkpoint_dir: Path to the checkpoint files. The latest checkpoint in the
        dir will be used to restore.
      checkpoint_filename_with_path: Full file name path to the checkpoint file.
      wait_for_checkpoint: Whether to wait for checkpoint to become available.
      max_wait_secs: Maximum time to wait for checkpoints to become available.
      config: Optional `ConfigProto` proto used to configure the session.

    Returns:
      A pair (sess, initialized) where 'initialized' is `True` if
      the session could be recovered and initialized, `False` otherwise.

    Raises:
      ValueError: If both checkpoint_dir and checkpoint_filename_with_path are
        set.
    """

    sess, is_loaded_from_checkpoint = self._restore_checkpoint(
        master,
        saver,
        checkpoint_dir=checkpoint_dir,
        checkpoint_filename_with_path=checkpoint_filename_with_path,
        wait_for_checkpoint=wait_for_checkpoint,
        max_wait_secs=max_wait_secs,
        config=config)

    # Always try to run local_init_op
    local_init_success, msg = self._try_run_local_init_op(sess)

    if not is_loaded_from_checkpoint:
      # Do not need to run checks for readiness
      return sess, False

    restoring_file = checkpoint_dir or checkpoint_filename_with_path
    if not local_init_success:
      logging.info(
          "Restoring model from %s did not make model ready for local init:"
          " %s", restoring_file, msg)
      return sess, False

    is_ready, msg = self._model_ready(sess)
    if not is_ready:
      logging.info("Restoring model from %s did not make model ready: %s",
                   restoring_file, msg)
      return sess, False

    logging.info("Restored model from %s", restoring_file)
    return sess, is_loaded_from_checkpoint