Recurrent Neural Network model.

Implements a language modeling network described in https://www.tensorflow.org/tutorials/recurrent that is compatible with (and idiomatic for) eager execution.

To run:

• Download and extract the Penn Treebank dataset from http://www.fit.vutbr.cz/~imikolov/rnnlm/simple-examples.tgz

  tar xvzf simple-examples.tgz -C /tmp
  

• Run: python rnn_ptb.py --data-dir=/tmp/simple-examples/data

Benchmarks (using synthetic data):

# Using eager execution
python rnn_ptb_test.py --benchmarks=.

# Using graph execution
python rnn_ptb_graph_test.py --benchmarks=.

The above uses the model definition included with the TensorFlow pip package. To build (and run benchmarks) from source:

# Using eager execution
bazel run -c opt --config=cuda :rnn_ptb_test -- --benchmarks=.

# Using graph execution
bazel run -c opt --config=cuda :rnn_ptb_graph_test -- --benchmarks=.

(Or remove the --config=cuda flag for running on CPU instead of GPU).

On October 31, 2017, the benchmarks demonstrated slightly better performance (3-6%) for graph execution over eager execution for this particular model when using a single NVIDIA Titan X (Pascal) GPU on a host with an Intel Xeon E5-1650 CPU @ 3.50GHz and a batch size of 32.