class_docstr: Manages distributed hyperparameter optimisation across many gpus.
class_docstr_tok: ["Manages","distributed","hyperparameter","optimisation","across","many","gpus","."]
docstr: Returns next dictionary of hyperparameters to optimise.
docstr_tok: ["Returns","next","dictionary","of","hyperparameters","to","optimise","."]
code: def get_next_parameters(self):
      """Returns next dictionary of hyperparameters to optimise."""
      param_name = self.worker_search_queue.pop()
  
      params = self.global_hyperparam_df.loc[param_name, :].to_dict()
  
      # Always override!
      for k in self.fixed_params:
          print(
              "Overriding saved {}: {}".format(k, self.fixed_params[k])
          )
  
          params[k] = self.fixed_params[k]
  
      return params
  
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 3 for calling the DistributedHyperparamOptManager obj's underlying member method code with example usage: obj.get_next_parameters() and returns: params