The Livermore Big Artificial Neural Network toolkit (LBANN) is an open-source, HPC-centric, deep learning training framework that is optimized to compose multiple levels of parallelism.

LBANN provides model-parallel acceleration through domain decomposition to optimize for strong scaling of network training. It also allows for composition of model-parallelism with both data parallelism and ensemble training methods for training large neural networks with massive amounts of data. LBANN is able to advantage of tightly-coupled accelerators, low-latency high-bandwidth networking, and high-bandwidth parallel file systems.

LBANN supports state-of-the-art training algorithms such as unsupervised, self-supervised, and adversarial (GAN) training methods in addition to traditional supervised learning. It also supports recurrent neural networks via back propagation through time (BPTT) training, transfer learning, and multi-model and ensemble training methods.

Building LBANN

The preferred method for LBANN users to install LBANN is to use Spack. After some system configuration, this should be as straightforward as

spack install lbann

More detailed instructions for building and installing LBANN are available at the main LBANN documentation.

Running LBANN

The basic template for running LBANN is

<mpi-launcher> <mpi-options> \
    lbann <lbann-options> \
    --model=model.prototext \
    --optimizer=opt.prototext \
    --reader=data_reader.prototext

When using GPGPU accelerators, users should be aware that LBANN is optimized for the case in which one assigns one GPU per MPI rank. This should be borne in mind when choosing the parameters for the MPI launcher.

More details about running LBANN are documented here.

Publications

A list of publications, presentations and posters are shown here.

Reporting issues

Issues, questions, and bugs can be raised on the Github issue tracker.