def load(self, filename, create_if_missing=True):
        filename = Path(filename)
        if not filename.exists() and create_if_missing:
            self.load_pip_requirements()
            with filename.open("w") as f:
                f.write(yaml.dump(self.serialized, default_flow_style=False,
                                  encoding=None))
            self.filename = filename
            return self.save(filename)
        with filename.open() as f:
            for group, requirements in yaml.load(f.read()).items():
                for requirement in requirements:
                    self[group].add(Requirement.coerce(requirement))
        self.filename = filename
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