For example, the RNN is a dynamic 3-layer bidirectional LSTM with the hidden vector size of 200 (tf.nn.bidirectional_dynamic_rnn
) and I have 4 GPUs to train the model. I saw a post using data parallelism
on subsets of samples in a batch but that didn't speed up the training process.
How to speed up the training of an RNN model with multiple GPUs in TensorFlow?
1.3k views Asked by Maosi Chen At
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You can also try model parallelism. One way to do this is to make a cell wrapper like this, which will create cells on a specific device:
Then place each individual layer onto dedicated GPU: