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
related pypi page: https://pypi.python.org/pypi/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:
on Windows:
Python 2.7 or Python 3.4
A compiler:
Python 2.7 build: need Visual Studio 2008 for Python 2.7 from Microsoft
Python 3.4 build: need Visual Studio 2010, eg Visual C++ 2010 Express
on linux:
Python 2.7 or Python 3.4
g++, supporting c++0x, eg 4.4 or higher
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)
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distributions
Built Distribution
Hashes for DeepCL-v5.8.4-py2.7-linux-i686.egg
Algorithm | Hash digest | |
---|---|---|
SHA256 | cce5b363959eb67db3cea6e570ddda3152d8a650f7cc24a7ef4c97eb915c2b9b |
|
MD5 | 9f1e5da8293533e378bc75a4a04c4718 |
|
BLAKE2b-256 | 48eb75e4538451f12d61ef5b9b977dfcb1e084dc278041fcbc7d4d7e03064651 |