How to run Tensorflow Estimator on multiple GPUs with data parallelism

Question

I have a standard tensorflow Estimator with some model and want to run it on multiple GPUs instead of just one. How can this be done using data parallelism?

I searched

Answer 1

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))

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.