Recent Releases of hydragnn
hydragnn - HydraGNN v4.0
The new version of HydraGNN v4.0 provides additional core capabilities, such as:
- Inclusion of multi-body atomistic cluster expansion MACE, polarizable atom interaction neural network PAINN, and equivariant principal neighborhood aggregation (PNAEq) among the message passing layers supported -Inclusion of graph transformers to directly model long-range interactions between nodes that are distant in the graph topology
- Integration of graph transformers with message passing layers by combining the graph embedding generated by the two mechanisms, which allows for an improved expressivity of the HydraGNN architecture
- Improved re-implementation of multi-task learning (MTL) to allow its use for stabilized training across imbalanced, multi-source, multi-fidelity data
- Introduction of multi-task parallelism, a newly proposed type of model parallelism specifically for MTL architectures, which allows to dispatch different output decoding heads to different GPU devices
- Integration of multi-task parallelism with pre-existing distributed data parallelism to enable a 2D parallelization for distributed training
- Improved portability of the distributed training across Intel GPUs, which has been testes on ALCF exascale supercomputer Aurora
- Inclusion of 2-level fine-grained energy profilers portable across NVIDIA, AMD, and Intel GPUs to monitor the power and energy consumption associated with different functions executed by the HydraGNN code during data pre-load and training
- Restructuring of previous examples and inclusion of new sets of examples to illustrate the download, preprocess, and training of HydraGNN models on new large-scale open-source datasets for atomistic materials modeling (e.g., Alexandria, Transition1x, OMat24, OMol25)
- Python
Published by allaffa 7 months ago
hydragnn - HydraGNN v2.0.0 Release
Summary
New or improved capabilities included in v2.0.0 release are as follows: * Enhancement in message passing layers through class inheritance * Adding transformation to ensure translation and rotation invariance * Supporting various optimizers * Atomic descriptors * Integration with continuous CI test * Distributed printouts and timers * Profiling * Support of ADIOS2 for scalable data loading * Large-scale system support, including Summit (ORNL) and Perlmutter (NERSC)
Capabilities provided in v1.0.0 release (Oct 2021)
Major capabilities included in the previous release v1.0.0 are as follows: * Multi-task graph neural network training with enhanced message passing layers * Distributed Data Parallelism (DDP) support
- Python
Published by allaffa about 3 years ago