Dragon: A Computation Graph Virtual Machine Based Deep Learning Framework

(Installation | Documentation)

Dragon is a C(Computation)G(Graph)V(Virtual)M(Machine) based distributed deep learning framework.

Our goal is to reduce the unnecessary structures or interfaces. Therefore, in addition to feed or fetch, the last thing is designing a objective function through all available operators.

Besides, we demonstrate that a cross-frameworks frontend(Deep Learning VirtualBox) is feasible, and further more, will get benefit from all participating crucial interfaces especially when one is not reasonable.

WHY NOT Original DL Frameworks?

I was always confused in my childhood of studying DeepLearning:  

import theano
import caffe
import tensorflow
import torch

Too stupied, ISN'T?

One day, I saw a JOKE:

# FXCK TF
# KEEP CALM AND USE PYTORCH
import tensorflow as torch

So, I made it:

import dragon.vm.theano as theano
import dragon.vm.caffe as caffe
import dragon.vm.tensorflow as tensorflow
import dragon.vm.torch as torch

WOW, I could use ALL above DL Frameworks all together!

News

Dragon 0.2.2 Released - Cleaner, Faster, Stronger and now we have DYNAMIC GRAPH >>> (VM.PyTorch :-) <<<

License and Citation

Dragon is released under the BSD 2-Clause license.

Please cite Dragon in your publications if it helps your research:

@article{pan2017dragon,
  Author = {Pan, Ting},
  Journal = {arXiv preprint arXiv:1707.08265},
  Title = {Dragon: A Computation Graph Virtual Machine Based Deep Learning Framework},
  Year = {2017}
}