https://github.com/xiaohanzhangcmu/axiom

scalable C++ and python binding framework for Scientific Computing and Machine Learning
https://github.com/xiaohanzhangcmu/axiom

deepq-learning finite-element-methods molecular-dynamics stanford tensorflow

Last synced: 8 days ago
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scalable C++ and python binding framework for Scientific Computing and Machine Learning

• Host: GitHub
• URL: https://github.com/xiaohanzhangcmu/axiom
• Owner: XiaohanZhangCMU
• Created: 2018-06-13T16:34:15.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2020-07-06T04:18:33.000Z (over 4 years ago)
• Last Synced: 2024-11-04T08:58:09.335Z (about 2 months ago)
• Topics: deepq-learning, finite-element-methods, molecular-dynamics, stanford, tensorflow
• Language: HTML
• Homepage:
• Size: 3.67 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Axiom Drives Scientific Computing Codes with Python

Authors: **aXioM**=**Xiao**Han+**Mo**Han :)

This framework helps bind (GPU/MPI-accelerated) C++ codes with Python using pybind11. For example, packages/mdsw implements Molecular Dynamics in C++ and it interfaces with OpenAI/gym environment through Python. New package can be added by following the existing examples. Axiom allows C++ and Python to communicate efficiently within memory through Numpy. Below shows an calculation done with mdsw -- the greedy search part is written in Python.

 Minimum Atomistic Defect (Frank-Partial Dislocation) Nucleation Energy Barrier Found by Greedy Algorithm 



## Installation requirement:

1) OS X > Yosemite, Linux: CentOS >= 7.5.1804 or ubuntu >=16.04.4 LTS).

2) python: 2.7 or 3.6. 

## Installation of packages:

1) git clone [email protected]:XiaohanZhangCMU/axiom.git; cd axiom;   

2) Add your (GPU/MPI)C++ project "ABC" to axiom/packages/ABC. Organize the codes into src and include.

3) Write CMakeLists.txt in axiom/packages/ABC and write _axiom.cpp in a similar way as other packages to export the variables and methods to python.

4) sh ./install_package "your package name " to compile. (You can also supply multiple package names and build them simultaneously. If no argument is given, all packages in axiom/packages will be built.)

5) In python script, add sys.path.append('your_path_to/axiom/lib/') to the top.

6) Enjoy!

## Optional packages for visualization:

1) pip install PyOpenGL PyOpenGL_accelerate (On Ubuntu or Linux need: sudo apt-get install python-opengl).

2) python3 -m pip install Pillow   

## Existing packages:

1) zoo: A playground for simple pybind11 binding. 

2) tensor: GPU and boost::share_ptr for automatic gpu memeory allocation and syncronization. 

3) mdsw: MD++ (http://micro.stanford.edu/MDpp), a molecular dynamics simulator. Working with reinforcement learning. See  Python/frankMEP/SearchAlgorihtm.py.

4) mdfem: Solve nonlinear neo-hookean FEM by minimizing potential energy.

5) fem: A one dimensional elasticity FEM solver (to be generalized).  

## Some Features:

1) Implement greedy search algorithm for Frank Partial Dislocation energy barrier calculation in silicon thin film.

2) mdsw interfaces with openAI/gym. On going: Deep Q network search algorithm for Frank Partial dislocation nucleation mechanism.

3) tensor package enables automatic memory synchronization between GPU and CPU for tensors (same as Caffe).

4) python/tests/View.py uses PyOpenG for visualization.

5) Arrays can be exported as numpy arrays and modified in place (no deep memory copy is needed).

Bug reports or questions:

1) Note: python script should not have the same name as the module because "module load" refers to the currently used python script, not the dynamic libraries.

[email protected]