class_docstr: The DeepSpeed Autotuner automatically discovers the optimal DeepSpeed configuration that delivers good training speed. The Autotuner uses model information, system information, and heuristics to efficiently tune system knobs that affect compute and memory efficiencies, such as ZeRO optimization stages, micro-batch sizes, and many other ZeRO optimization configurations. It not only reduces the time and resources user spend on tuning, but also can discover configurations better than hand-tuned methods.
  Autotuning with DeepSpeed requires no code change from DeepSpeed users. Please refer to the README for usage details.
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code: def num_tuning_micro_batch_sizes(self):
      return self.autotuning_config.num_tuning_micro_batch_sizes
  
imports: import shutil
  import subprocess
  import torch
  import time
  import datetime
  import math
  import hjson
  from ..runtime.config_utils import dict_raise_error_on_duplicate_keys
  from ..runtime.constants import *
  from ..runtime.zero.config import ZERO_OPTIMIZATION, ZeroStageEnum
  from ..utils import logger
  from .config import DeepSpeedAutotuningConfig
  from .constants import *
  from .scheduler import ResourceManager
  from .tuner import GridSearchTuner, RandomTuner, ModelBasedTuner
  from .utils import *
label_desc: Use image node_id 12 for calling the Autotuner obj's underlying member method code with example usage: obj.num_tuning_micro_batch_sizes() and returns: self