What is XenonPy project

XenonPy is a Python library that implements a comprehensive set of machine learning tools for materials informatics. Its functionalities partially depend on PyTorch and R. The current release provides some limited modules:

• Interface to public materials database
• Library of materials descriptors (compositional/structural descriptors)
• Pretrained model library XenonPy.MDL (v0.1.0b, 2018/12/25: more than 10,000 modles in 35 properties of small molecules, polymers, and inorganic compounds)
• Machine learning tools.
• Transfer learning using the pretrained models in XenonPy.MDL

XenonPy inspired by matminer: https://hackingmaterials.github.io/matminer/.

XenonPy is a open source project https://github.com/yoshida-lab/XenonPy.

See our documents for details: http://xenonpy.readthedocs.io

XenonPy images

XenonPy images packed a lot of useful package for materials informatics using. The following table list some core packages in XenonPy images.

Package	Version
PyTorch	1.0.1
pymatgen	2019.2.4
matminer	0.5.1
scipy	1.2.0
scikit-learn	0.20.2
pandas	0.24.1
rdkit	2018.09.1
jupyter	1.0.0
seaborn	0.9.0
matplotlib	3.0.2
plotly	3.5.0

Requirements

In order to use this image you must have Docker Engine installed. Instructions for setting up Docker Engine are available on the Docker website.

CUDA requirements

If you have a CUDA-compatible NVIDIA graphics card, you can use a CUDA-enabled version of the PyTorch image to enable hardware acceleration. This only can be used in Ubuntu Linux.

Firstly, ensure that you install the appropriate NVIDIA drivers and libraries. If you are running Ubuntu, you can install proprietary NVIDIA drivers from the PPA and CUDA from the NVIDIA website.

You will also need to install nvidia-docker2 to enable GPU device access within Docker containers. This can be found at NVIDIA/nvidia-docker.

Usage

Pre-built xenonpy images are available on Docker Hub under the name yoshidalab/xenonpy. For example, you can pull the CUDA 10.0 version with:

docker pull yoshidalab/xenonpy:cuda10

The table below lists software versions for each of the currently supported Docker image tags .

Image tag	CUDA	PyTorch
latest	10.0	1.0.0
cpu	None	1.0.0
cuda10	10.0	1.0.0
cuda9	9.0	1.0.0

Running XenonPy

It is possible to run XenonPy inside a container. Using xenonpy with jupyter is very easy, you could run it with the following command:

docker run --rm -it \
  --runtime=nvidia \
  --ipc=host \
  --publish="8888:8888"
  --volume=$Home/.xenonpy:/home/user/.xenonpy \
  --volume=<path/to/your/workspace>:/workspace \
  -e NVIDIA_VISIBLE_DEVICES=0 \
  yoshidalab/xenonpy

Here's a description of the Docker command-line options shown above:

• --runtime=nvidia: Required if using CUDA, optional otherwise. Passes the graphics card from the host to the container. Optional, based on your usage.
• --ipc=host: Required if using multiprocessing, as explained at https://github.com/pytorch/pytorch#docker-image. Optional
• --publish="8888:8888": Publish container's port 8888 to the host. Needed
• --volume=$Home/.xenonpy:/home/user/.xenonpy: Mounts the XenonPy root directory into the container. Optional, but highly recommended.
• --volume=<path/to/your/workspace>:/workspace: Mounts the your working directory into the container. Optional, but highly recommended.
• -e NVIDIA_VISIBLE_DEVICES=0: Sets an environment variable to restrict which graphics cards are seen by programs running inside the container. Set to all to enable all cards. Optional, defaults to all.

You may wish to consider using Docker Compose to make running containers with many options easier. At the time of writing, only version 2.3 of Docker Compose configuration files supports the runtime option.

Copyright and license

©Copyright 2019 The XenonPy project, all rights reserved. Released under the BSD-3 license.