I plan to run a very large recurrent network (eg 2048x5), is it possible to define one layer at one GPU in tensorflow? How should I implement the model to achieve the best efficiency. I understand there is overhead for inter-GPU or GPU-CPU-GPU communication.
Splitting a large model across multiple GPUs is certainly possible in TensorFlow, but doing it optimally is a hard research problem. In general, you will need to do the following:
Wrap large contiguous regions of your code in a with tf.device(...):
block, naming the different GPUs:
with tf.device("/gpu:0"): # Define first layer. with tf.device("/gpu:1"): # Define second layer. # Define other layers, etc.
When building your optimizer, pass the optional argument colocate_gradients_with_ops=True
to the optimizer.minimize()
method:
loss = ... optimizer = tf.train.AdaGradOptimizer(0.01) train_op = optimizer.minimize(loss, colocate_gradients_with_ops=True)
(Optionally.) You may need to enable "soft placement" in the tf.ConfigProto
when you create your tf.Session
, if any of the operations in your model cannot run on GPU:
config = tf.ConfigProto(allow_soft_placement=True) sess = tf.Session(config=config)
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