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.
