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============================== Release Notes: v0.100 ==============================
Support for new network structures:
- 3D molecular generation models for Metal Organic Frameworks from the CoRE MOF Database.
- 3D CosmoFlow Model
- DenseNet
- ATOM LSTM model
- RAS state classifier
- node2vec
- Transformer and other attention-based models
- ExaGAN (formerly CosmoGAN)
- MaCC ICF surrogate model
Applications:
- Created a directory of example applications, deprecating the "model zoo" directory
Support for new layers:
- Embedding layer
- Distributed embedding layer
- Channel-wise scale/bias layer
- Entry-wise scale/bias layer
- Gated-Recurrent Units (GRU)
- Entry-wise batchnorm
- Argmax, Argmin, and one-hot layers
- Layer norm
- Deconvolution layer (transposed convolution)
- Layers for channel-wise operations (channel-wise fully-connected, channel-wise softmax, channel-wise scale/bias, instance norm)
- Matrix multiply layer
Python front-end:
- Can now configure contrib launcher with environment variables
- Added NERSC compute center
- Per-layer specification of compute device (CPU or GPU)
- Option to write custom batch scripts with Python front-end
Performance optimizations:
- Parallelized Python data reader with "multiprocessing" module
- Fuse batchnorm stats allreduces in FP/BP.
- Tuned concatenate and slice layer
- Dynamically allocate and free memory for layer error signals (halves LBANN's memory footprint)
Model portability & usability:
- Bamboo tests for individual layers
Internal features:
- Added support for DistConv features (distributed, generalized,
parallel convolution)
- Added support for NVSHMEM 1.0 API (used in distributed embedding
layer and DistConv halo exchange)
- Support for multiple data types per model (per-layer)
- Support for per-layer mixed-precision weight training and inference,
includes per-weight object and objective function mixed-precision.
- Improved how and when the RNGs are initialized
- Callback to dump images to TensorBoard
- Callback to save model weights (useful to export to PyTorch)
- Callback to save top K models (LTFB)
- Improved run-to-run reproducibility by initializing weights in alphabetical order
- Moved models from model_zoo directory to applications directory
- Cleanup and refactoring of callbacks and layer instantiation
- Grouped batchnorm statistics
- Callback to print model description
- Refactored trainer and training-state out of the model class
- Support for transposing data in matrix multiply layers
- Added DiHydrogen tensor and DistConv library
- Added parallel strategy to layer class to support DistConv
- LBANN inference mode supports loading models from multiple directories
- Cleanup of checkpoint and restart logic
I/O & data readers:
- Added in-memory data store that caches samples in CPU memory. It can be loaded
during the first epoch or preloaded
- Added new "transform" data preprocessing ingestion pipeline
- Added sample list format for specifying data sets
- Introduced data coordinator that manages data readers and extracts them from
the input layers
- Data store is able to checkpoint / spill it's contents to local disk
- Data reader for SMILE strings
Build system:
- Hydrogen 1.3.4
- Aluminum 0.3.3
- Improved documentation on read the docs (RTD)
- Robust support for using Spack as a build system around CMake
- Identified compute centers for specifying build and run dependencies
- Added Catch2-based tests
Bug fixes:
- Fixed path resolution for dump weights, save model, and checkpoint callbacks
- Added mutexes for preloading the data store
- Fixed the LTFB exchange to include all ADAM optimizer state
- Fixed the mapping of I/O RNGs to I/O processing threads to ensure
consistent and correct multi-threaded performance
Retired features:
- moving MNIST data reader is replaced by python data reader
- ASCII data reader is deprecated
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