Method as_dict

tensorflow/python/training/server_lib.py:336–361

Returns a dictionary from job names to their tasks. For each job, if the task index space is dense, the corresponding value will be a list of network addresses; otherwise it will be a dictionary mapping (sparse) task indices to the corresponding addresses. Returns: A dict

(self)

    return "ClusterSpec({" + ", ".join(string_items) + "})"

  def as_dict(self):
    """Returns a dictionary from job names to their tasks.

    For each job, if the task index space is dense, the corresponding
    value will be a list of network addresses; otherwise it will be a
    dictionary mapping (sparse) task indices to the corresponding
    addresses.

    Returns:
      A dictionary mapping job names to lists or dictionaries
      describing the tasks in those jobs.
    """
    ret = {}
    for job in self.jobs:
      task_indices = self.task_indices(job)
      if len(task_indices) == 0:
        ret[job] = {}
        continue
      if max(task_indices) + 1 == len(task_indices):
        # Return a list because the task indices are dense. This
        # matches the behavior of `as_dict()` before support for
        # sparse jobs was added.
        ret[job] = self.job_tasks(job)
      else:
        ret[job] = {i: self.task_address(job, i) for i in task_indices}
    return ret

  def as_cluster_def(self):
    """Returns a `tf.train.ClusterDef` protocol buffer based on this cluster."""