class_docstr: Manages distributed hyperparameter optimisation across many gpus.
class_docstr_tok: ["Manages","distributed","hyperparameter","optimisation","across","many","gpus","."]
docstr: Regenerates hyperparameter combinations and saves to file.
  
  Returns:
    DataFrame containing hyperparameter combinations.
docstr_tok: ["Regenerates","hyperparameter","combinations","and","saves","to","file",".","Returns",":","DataFrame","containing","hyperparameter","combinations","."]
code: def update_serialised_hyperparam_df(self):
      """Regenerates hyperparameter combinations and saves to file.
  
      Returns:
        DataFrame containing hyperparameter combinations.
      """
      search_df = self._generate_full_hyperparam_df()
  
      print(
          "Serialising params for {} search iterations to {}".format(
              self.total_search_iterations, self.serialised_ranges_path
          )
      )
  
      search_df.to_csv(self.serialised_ranges_path)
  
      return search_df
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import collections
  import os
  import shutil
  import libs.utils
  import numpy
  import pandas
label_desc: Use image node_id 5 for calling the DistributedHyperparamOptManager obj's underlying member method code with example usage: obj.update_serialised_hyperparam_df() and returns: search_df