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python wrapper for DeepCL deep convolutional neural network library for OpenCL

Project description

DeepCL Python wrappers

Python wrapper for DeepCL

To install from pip

pip install DeepCL

How to use

See test_deepcl.py for an example of:

  • creating a network, with several layers

  • loading mnist data

  • training the network using a higher-level interface (NetLearner)

For examples of using lower-level entrypoints, see test_lowlevel.py:

  • creating layers directly

  • running epochs and forward/backprop directly

For example of using q-learning, see test_qlearning.py.

To build from source

Pre-requisites:

To build:

cd python
python setup.py build_ext -i

Then, you can run from this directory, by making sure to add it to the path, eg:

PYTHONPATH=. python test_lowlevel.py /my/mnist/data/dir

To install:

cd python
python setup.py install

Notes on how the wrapper works

  • cDeepCL.pxd contains the definitions of the underlying DeepCL c++ libraries classes

  • PyDeepCL.pyx contains Cython wrapper classes around the underlying c++ classes

  • setup.py is a setup file for compiling the PyDeepCL.pyx Cython file

to run unit-tests

From the python directory:

nosetests -sv

Development builds

  • If you want to modify the sourcecode, you’ll need to re-run cython, so you’ll need cython:

    pip install cython
  • If you want to update this readme, you might want to re-generate the README.rst, so you’ll need pypandoc:

    pip install pypandoc
  • (note that pypandoc depends on pandoc)

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