Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/farrajota/benchmark-rnn-torch7
Benchmark rnn libraries on torch7 for the word-language models task.
https://github.com/farrajota/benchmark-rnn-torch7
benchmark cudnn rnn torch7 word-language-model
Last synced: about 12 hours ago
JSON representation
Benchmark rnn libraries on torch7 for the word-language models task.
- Host: GitHub
- URL: https://github.com/farrajota/benchmark-rnn-torch7
- Owner: farrajota
- License: mit
- Created: 2017-06-12T13:48:32.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2017-10-18T09:55:01.000Z (about 7 years ago)
- Last Synced: 2023-10-20T12:34:42.424Z (about 1 year ago)
- Topics: benchmark, cudnn, rnn, torch7, word-language-model
- Language: Lua
- Homepage:
- Size: 1.27 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# RNN modules/libraries benchmark on Torch7
This repo contains benchmark results for popular rnn modules/architectures on three different libraries available for Torch7 on a simple task for word language model:
- [cudnn](https://github.com/soumith/cudnn.torch)
- [rnn](https://github.com/Element-Research/rnn)
- [rnnlib](https://github.com/facebookresearch/torch-rnnlib)
The evaluated rnn architectures are the following:
- RNN
- LSTM
- GRU
## Requirements
To use this repository you must have [Torch7](http://torch.ch/) installed on your system.
Also, you'll need a NVIDIA GPU with compute capability 3.5+ (2GB+ ram) and `CUDNN R5+` installed in order to run this code.
Furthermore, you'll need to install the following dependencies for torch7:
```bash
luarocks install cudnn
luarocks install optnet
luarocks install rnn
luarocks install rnnlib
luarocks install torchnet
```
> Note: Please make sure you have an up-to-date version of torch7 before running the benchmark script. To do this, go to your `torch/` folder and run the `./update.sh` file.
## Getting started
### Data setup
Download and setup the necessary data by running the following script:
```
th download_setup_data.lua
```
This script will download and extract the following datasets to disk:
- [shakespear](http://cs.stanford.edu/people/karpathy/char-rnn/shakespeare_input.txt)
- [linux kernel](http://cs.stanford.edu/people/karpathy/char-rnn/linux_input.txt)
- [wikipedia](http://prize.hutter1.net/)
### Benchmarking the networks/libraries
To evaluate the different rnn modules/libraries tested here, run the main script:
```
th scripts/run_benchmark.lua
```
This will train and test several networks on three different datasets and plot all results into sveral graphs for each dataset.
### Results
When running the benchmark script, you should get the same results presented in these next graphs.
All models have a sequence length of 50, a batch size of 64, 2 layers, and were averaged over 10 epochs and over all 3 datasets.
> Warning: Results are currently being processed. Once they are finished (it takes several days!) I'll upload them.
#### Speed (batch/ms)
#### Forward/backward Speed (batch/ms)
#### GPU Memory Usage (in MB)
#### Test Loss Results
Below are the results of the loss of all rnn
models tested on the Shakespear,
Linux kernel and Wikipedia datasets.
| Shakespear | Linux kernel | Wikipedia |
| --- | --- | --- |
|  |  |  |
|  |  |  |
|  |  |  |
|  |  |  |
|  |  |  |
## License
[MIT License](LICENSE)