The DeepPhysX project provides Python packages allowing users to easily interface their numerical simulations with learning algorithms.
DeepPhysX provides a Core package with no dependency on any simulation or AI framework. Then other packages are compatible with this Core and a specific simulation or AI framework:
DeepPhysX is a full Python3 projects with 3 main features:
The full list of features is detailed in the documentation.
The project was initially developed using SOFA as the simulation package and PyTorch as the AI framework. Thus, DeepPhysX is mainly designed for these frameworks, but obviously other frameworks can also be used. The packages corresponding to these frameworks will therefore be used for the default installation.
The easiest way to install is using
pip, but there are a several way to install and configure a DeepPhysX
environment (refer to the documentation for further instructions).
$ pip install DeepPhysX # Install default package $ pip install DeepPhysX.Sofa # Install simulation package $ pip install DeepPhysX.Torch # Install AI package
DeepPhysX includes a set of detailed tutorials, examples and demos. Following this installation process to directly try the interactive demos:
$ mkdir DeepPhysX $ cd DeepPhysX $ git clone https://github.com/mimesis/deepphysx.git Core # Make shure to clone this repository in 'DeepPhysX/Core' $ cd Core $ python3 config.py # Answer 'yes' to install Torch package to launch examples $ pip install .