tensorflow_benchmark/tf_cnn_benchmarks/variable_mgr.py [667:694]:
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    return device_grads[device_num]

  def get_post_init_ops(self):
    # Copy initialized values for variables on GPU 0 to other GPUs.
    global_vars = tf.global_variables()
    var_by_name = dict([(v.name, v) for v in global_vars])
    post_init_ops = []
    for v in global_vars:
      split_name = v.name.split('/')
      # TODO(b/62630508): use more specific prefix than v or v0.
      if split_name[0] == 'v0' or not v.name.startswith('v'):
        continue
      split_name[0] = 'v0'
      copy_from = var_by_name['/'.join(split_name)]
      post_init_ops.append(v.assign(copy_from.read_value()))
    return post_init_ops

  def savable_variables(self):
    """Return the set of variables used for saving/loading the model."""
    params = []
    for v in tf.global_variables():
      split_name = v.name.split('/')
      if split_name[0] == 'v0' or not v.name.startswith('v'):
        params.append(v)
    return params

  def get_devices(self):
    return self.benchmark_cnn.raw_devices
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tensorflow_benchmark/tf_cnn_benchmarks/variable_mgr.py [767:794]:
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    return device_grads[device_num]

  def get_post_init_ops(self):
    """Copy initialized values for variables to other devices."""
    global_vars = tf.global_variables()
    var_by_name = dict([(v.name, v) for v in global_vars])
    post_init_ops = []
    for v in global_vars:
      split_name = v.name.split('/')
      # TODO(b/62630508): use more specific prefix than v or v0.
      if split_name[0] == 'v0' or not v.name.startswith('v'):
        continue
      split_name[0] = 'v0'
      copy_from = var_by_name['/'.join(split_name)]
      post_init_ops.append(v.assign(copy_from.read_value()))
    return post_init_ops

  def savable_variables(self):
    """Return the set of variables used for saving/loading the model."""
    params = []
    for v in tf.global_variables():
      split_name = v.name.split('/')
      if split_name[0] == 'v0' or not v.name.startswith('v'):
        params.append(v)
    return params

  def get_devices(self):
    return self.benchmark_cnn.raw_devices
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