message: ... specify `num_gpus_per_worker` for your strategy object ...
diff: @@ -187,7 +187,7 @@ in the input function gives a solid boost in performance. When using
  For multi-worker training, no code change is required to the `Estimator` code.
  You can run the same model code for all tasks in your cluster including
  parameter servers and the evaluator. But you need to use
  -`tf.estimator.train_and_evaluate`, explicitly specify `num_gpus_per_workers`
  +`tf.estimator.train_and_evaluate`, explicitly specify `num_gpus_per_worker`
  for your strategy object, and set "TF\_CONFIG" environment variables for each
  binary running in your cluster. We'll provide a Kubernetes template in the
  [tensorflow/ecosystem](https://github.com/tensorflow/ecosystem) repo which sets
  