class_docstr: Manages distributed hyperparameter optimisation across many gpus.
class_docstr_tok: ["Manages","distributed","hyperparameter","optimisation","across","many","gpus","."]
docstr: Loads serialsed hyperparameter ranges from file.
  
  Returns:
    DataFrame containing hyperparameter combinations.
docstr_tok: ["Loads","serialsed","hyperparameter","ranges","from","file",".","Returns",":","DataFrame","containing","hyperparameter","combinations","."]
code: def load_serialised_hyperparam_df(self):
      """Loads serialsed hyperparameter ranges from file.
  
      Returns:
        DataFrame containing hyperparameter combinations.
      """
      print(
          "Loading params for {} search iterations form {}".format(
              self.total_search_iterations, self.serialised_ranges_path
          )
      )
  
      if os.path.exists(self.serialised_ranges_folder):
          df = pd.read_csv(self.serialised_ranges_path, index_col=0)
      else:
          print("Unable to load - regenerating search ranges instead")
          df = self.update_serialised_hyperparam_df()
  
      return 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 4 for calling the DistributedHyperparamOptManager obj's underlying member method code with example usage: obj.load_serialised_hyperparam_df() and returns: df