blob: 743ebb68ee5bba5635899267cc4839828f7e4e2f (
plain)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
|
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
```sh
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 demostrated 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.
| Benchmark name | examples/second |
| ------------------------------------ | --------------- |
| eager_cudnn_train_large_gpu_batch_20 | 938 |
| graph_cudnn_train_large_gpu_batch_20 | 971 |
| eager_cudnn_train_small_gpu_batch_20 | 2433 |
| graph_cudnn_train_small_gpu_batch_20 | 2585 |
|
