Qualia2.0 Save

Qualia is a deep learning framework deeply integrated with automatic differentiation and dynamic graphing with CUDA acceleration. Thanks to the define-by-run API, the code written with Qualia enjoys high modularity.

Introduction

David J. Chalmers, an Australian philosopher and cognitive scientist, once argued that if a system reproduces the functional organization of the brain, it will also reproduce the qualia associated with the brain in the paper "Absent Qualia, Fading Qualia, Dancing Qualia." This library "Qualia" named after the series of arguments in philosophy of mind associated with the qualia, hoping for the creation of a system with subjective consciousness.

Overview

The main components of Qualia is listed below:

Docs

Online document is available here.

Requirements

• NVIDIA CUDA GPU: Compute Capability of the GPU must be at least 3.0.
• CUDA Toolkit: Supported Versions: 8.0, 9.0, 9.1, 9.2, 10.0, and 10.1.
• Python 3.6 +

Note: Qualia is also available for CPU use

Installation

For detailed instructions on installing Qualia, see the installation guide.

Depending on the CUDA version you have installed on your host, choose the best option from following.

(For CUDA 8.0)
$ python setup.py install --cuda 80
(For CUDA 9.0)
$ python setup.py install --cuda 90
(For CUDA 9.1)
$ python setup.py install --cuda 91
(For CUDA 9.2)
$ python setup.py install --cuda 92
(For CUDA 10.0)
$ python setup.py install --cuda 100
(For CUDA 10.1)
% python setup.py install --cuda 101
(For without CUDA)
$ python setup.py install

Demo

More examples can be found here.

Supervised learning

Unsupervised learning

Reinforcement learning

Citation

Please cite Qualia if you use the contents in this repository for your research or in a scientific publication.

Y. Kashu, Qualia - Automatic Differentiation and Dynamic Graphing with CUDA for Deep Learning Application, (2019), GitHub repository, https://github.com/Kashu7100/Qualia2.0

BibTex

@misc{qualia,
  author = {Kashu Yamazaki},
  title = {{Q}ualia - Automatic Differentiation and Dynamic Graphing with CUDA for Deep Learning Application},
  year = {2019},
  publisher = {GitHub},
  journal = {GitHub repository},
  keywords = {Python, Automatic Differentiation, Dynamic Graphing, CUDA, Deep Learning}
  howpublished  = {\url{https://github.com/Kashu7100/Qualia2.0}},
}

License

Source codes in the repository follows MIT license.

References

References are listed in wiki