v0.99

============================== 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
  needed.
• 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 lbann.readthedocs.io
• 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)