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============================== 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)
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# .readthedocs.yml | ||
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image: latest | ||
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python: | ||
version: 3.7 |
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Copyright (c) 2014-2016, Lawrence Livermore National Security, LLC. | ||
Produced at the Lawrence Livermore National Laboratory. | ||
Copyright (c) 2014-2019, Lawrence Livermore National Security, LLC. | ||
Produced at the Lawrence Livermore National Laboratory. | ||
Written by the LBANN Research Team (B. Van Essen, et al.) listed in | ||
the CONTRIBUTORS file. <[email protected]> | ||
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This file is part of LBANN: Livermore Big Artificial Neural Network | ||
Toolkit. For details, see http://software.llnl.gov/LBANN or | ||
https://github.com/LLNL/LBANN. | ||
https://github.com/LLNL/LBANN. | ||
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Licensed under the Apache License, Version 2.0 (the "Licensee"); you | ||
may not use this file except in compliance with the License. You may | ||
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or | ||
implied. See the License for the specific language governing | ||
permissions and limitations under the license. | ||
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