hippynn 0.1.3

Modular set of pytorch layers for atomistic operations

• Atomistic operations can be tricky to write in native pytorch. Most operations provided here support linear-scaling models.

• Model energy, force charge & charge moments, bond orders, and more!

• nn.Modules are written with minimal reference to the rest of the library; if you want to use them in your scripts without using the rest of the features provided here – no problem!

Graph level API for simple and flexible construction of models from pytorch components.

• Build models based on the abstract physics/mathematics of the problem, without having to think about implementation details.

• Graph nodes support native python syntax, for example different forms of loss can be directly added.

• Link predicted values in the model with a database entry to compare predicted and true values

• IndexType logic records metadata about tensor structure, and provides automatic conversion to compatible structures when possible.

• Graph API is independent of module implementation.

Plot level API for tracking your training.

• Using the graph API, define quantities to evaluate before, during, or after training as figures using matplotlib.

Training & Experiment API

• Integrated with graph level API

• Pretty-printing loss metrics, generating plots periodically

• Callbacks and checkpointing

Custom Kernels for fast execution

• Certain operations are not efficiently written in pure pytorch, we provide alternative implementations with numba and cupy

• These are directly linked in with pytorch Autograd – use them like native pytorch functions.

• These provide advantages in memory footprint and speed

• Includes CPU and GPU execution for custom kernels

Interfaces to other codes

• ASE: Define ASE calculators based on the graph-level API.

• PYSEQM: Use PYSEQM calculations as nodes in a graph.

• LAMMPS: Use models from hippynn in LAMMPS via the MLIAP Unified interface.

Install hippynn from source:

For detailed instructions, see the documentation section on installation.

Clone the hippynn repository and navigate into it, e.g.:

$ git clone https://github.com/lanl/hippynn.git
$ cd hippynn
$ pip install -e .

The -e specifies an editable install, that is, python will import hippynn from the current directory, which will allow you to tinker with hippynn if you so choose.

If numpy and pytorch are not currently installed, this command will install them using pip.

Once hippynn is installed, you can proceed to add optional packages as needed for various extended functionality.