jmoudrik / DeepCL

OpenCL library to train deep convolutional neural networks

DeepCL

• Python API
• Lua API
• Command line API
• C++ API
• Q-learning
• To build
• Development
• Changes

DeepCL

OpenCL library to train deep convolutional networks

• C++
• OpenCL
• Deep convolutional
• Python wrappers
• Lua wrappers
• Q-learning

APIs:

• Python
• Lua
• c++
• command-line

Layer types:

• convolutional
• max-pooling
• normalization
• activation
• dropout
• random translations
• random patches
• loss

Loss layer types:

• softmax
• cross-entropy (synonymous with multinomial logistic, etc)
• square loss

Trainers:

• SGD (including momentum)
• Anneal (New!)
• Nesterov (New!)
• Adagrad (New!)
• Rmsprop (New!)
• Adadelta (New!)

Activations:

• tanh
• scaled tanh (1.7519 * tanh(2/3x) )
• linear
• sigmoid
• relu

Loader formats:

• jpegs
• mnist
• kgsv2
• norb

Weight initializers:

• original
• uniform
• more possible...

Multicolumn net also possible, as in McDnn

Example usages

• obtained 37.2% test accuracy, on next move prediction task, using 33.6 million training examples from kgsgo v2 dataset
  • commandline used ./deepclrun dataset=kgsgoall netdef=12*(32c5z-relu)-500n-tanh-361n numepochs=15 learningrate=0.0001
  • 2 epochs, 2 days per epoch, on an Amazon GPU instance, comprising half an NVidia GRID K520 GPU (about half as powerful as a GTX780)
• obtained 99.5% test accuracy on MNIST, using netdef=rt2-8c5z-relu-mp2-16c5z-relu-mp3-150n-tanh-10n numepochs=20 multinet=6 learningrate=0.002
  • epoch time 99.8 seconds, using an Amazon GPU instance, ie half an NVidia GRID K520 GPU (since we are learning 6 nets in parallel, so 16.6seconds per epoch per net)

To install

Python

• For python, please use Python API, or use pip

Lua

• For Lua, please see Lua API, or use luarocks

Commandline tools, and c++ libraries

Windows

Pre-built binaries are available for Windows. In order to use them you need:

• An OpenCL driver for your GPU
• A recent release with Windows binaries is v5.5.0

linux

Pre-build binaries are available for linux. In order to use them you need:

• An OpenCL driver for your GPU
• A recent release with linux binaries is v5.5.0

If the binaries dont work on your distribution, please build from source

What if it doesn't run?

• Check if you have an OpenCL-enabled device on your system
  • ideally a GPU, or accelerator, since there is no attempt to optimize DeepCL for CPUs (at least, not currently, could change, feel free to submit a pull request :-) )
• Try running gpuinfo (from EasyCL, but built as part of this project too, for ease of use )
  • it should output at least one OpenCL-enabled device
  • if it doesn't, then you need to make sure you have an OpenCL-enabled device, and that appropriate drivers are installed, and that the ICD is configured appropriately (registry in Windows, and /etc/OpenCL/vendors in linux)

What if I need a new feature?

Please raise an issue, let me know you're interested.

• If it's on my list of things I was going to do sooner or later anyway (see below), I might do it sooner rather than later.
• If it's to do with usability, I will try to make that a priority

What if I want to contribute myself?

• please feel free to fork this repository, tweak things, send a pull request. Or get in contact. Or both :-)

Third-party libraries

• EasyCL
• clew
• libpng++

Related projects

• kgsgo-dataset-preprocessor Dataset based on kgsgo games; 33 million data points

Credits

• Tambet Matilsen has provided excellent suggestions and feedback on which functionalities to prioritize, and on how to make the website somewhat presentable

License

Mozilla Public License 2.0

To get in contact

There is a mailing list at http://lists.hughperkins.com/listinfo.cgi/deepcl-hughperkins.com for discussions, ideas, or just to say 'hi'. You can also just create issues, in github, in the top right of this page.