我认为 tf.contrib.estimator.replicate_model_fn 文档，

I think tf.contrib.estimator.replicate_model_fn is a cleaner solution. The following is from tf.contrib.estimator.replicate_model_fn documentation,

...
def model_fn(...):  # See `model_fn` in `Estimator`.
  loss = ...
  optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.001)
  optimizer = tf.contrib.estimator.TowerOptimizer(optimizer)
  if mode == tf.estimator.ModeKeys.TRAIN:
    #  See the section below on `EstimatorSpec.train_op`.
    return EstimatorSpec(mode=mode, loss=loss,
                         train_op=optimizer.minimize(loss))

  #  No change for `ModeKeys.EVAL` or `ModeKeys.PREDICT`.
  return EstimatorSpec(...)
...
classifier = tf.estimator.Estimator(
  model_fn=tf.contrib.estimator.replicate_model_fn(model_fn))

您需要做的是用tf.contrib.estimator.TowerOptimize和model_fn()用tf.contrib.estimator.replicate_model_fn()包装优化器. 我按照说明进行操作，并使TPU squeezenet模型在具有4个GPU的计算机上工作.我的修改此处.

What you need to do is to wrap optimizer with tf.contrib.estimator.TowerOptimize and model_fn() with tf.contrib.estimator.replicate_model_fn(). I followed the description and make an TPU squeezenet model work on a machine with 4 GPUs. My modifications here.