Skip to content
Choose a tag to compare


@bvanessen bvanessen released this
· 1261 commits to develop since this release
Choose a tag to compare

============================== Release Notes: v0.99 ==============================

Support for new training algorithms:

  • Improvements to LTFB infrastructure (including transfer of SGD and Adam hyperparameters)

Support for new network structures:

  • Support for Wide ResNets

Support for new layers:

Python front-end:

  • Python front-end for generating neural network architectures (lbann namespace):
    including layers, objective functions, callbacks, metrics, and optimizers.
  • Python interface for launching (SLURM or LSF) jobs on HPC systems
  • Support for running LBANN experiments and capturing experimental output
  • Network templates for AlexNet, LeNet, arbitrary ResNet models, and Wide ResNet models
  • Python scripts for LeNet, AlexNet, and (Wide) ResNets in model zoo.

Performance optimizations:

  • GPU implementation of RMSprop optimizer.
  • cuDNN convolution algorithms are determined by empirically measuring
    performance rather than using heuristics.
  • Avoid setting up unused bias weights.
  • Perform gradient accumulations in-place when possible.

Model portability & usability:

Internal features:

  • Weight gradient allreduces are in-place rather than on a staging buffer.
  • Fully connected and convolution layers only create bias weights when
  • Optimizer exposes gradient buffers so they can be updated in-place.
  • Added callback support to explicitly save model
  • Min-max metric for reporting on multiple LTFB trainers
  • Cleanup of Hydrogen interface to match Hydrogen v1.2.0
  • Added type-erased matrix class for internal refactoring
  • Make CUB always log performance critical events

I/O & data readers:

  • Python data reader that interacts with an embedded Python session.
  • Optimized data store to provide preload option
  • Extended data store to operate with Cosmoflow-numpy data reader

Build system:

  • Added documentation for how users can use Spack to install LBANN
    either directly or via environments.
  • Conduit is a required dependency.
  • Provided Spack environment for installing LBANN as a user
  • Improved documentation on
  • CMake installs a module file in the installation directory that
    sets up PATH and PYTHONPATH variables appropriately

Bug fixes:

  • Models can now be copied or setup multiple times.
  • Fixed incorrect weight initialization with multiple trainers.
  • Updated I/O random number generators to be C++ thread safe (rather than OpenMP)
  • Added an I/O random number generator for preprocessing that is independent
    of the data sequence RNG.
  • Fixed initialization order of RNGs and multiple models / trainers.
  • General fixes for I/O and LTFB interaction.

Retired features:

  • "Zero" layer (hack for early GAN implementation).
  • Removed data reader specific implementations of data store (in favor of Conduit-based
    data store)