Components

The SDK release comprises the components below. All are on conda-forge; all but Seekrflow are also on PyPI.

RHAPSODY

Unified AI+HPC runtime supporting services alongside tasks (extends RADICAL-Pilot). RHAPSODY orchestrates coupled AI-and-simulation ensembles across heterogeneous resources.

• Repository: https://github.com/radical-cybertools/rhapsody

• Documentation: https://rhapsody-py.readthedocs.io/

• PyPI: https://pypi.org/project/rhapsody-py/

• conda-forge: https://anaconda.org/conda-forge/rhapsody-py

AsyncFlow

Asynchronous workflow layer for scalable HPC pipelines.

• Repository: https://github.com/radical-cybertools/radical.asyncflow

• Documentation: https://radicalasyncflow.readthedocs.io/

• PyPI: https://pypi.org/project/radical-asyncflow/

• conda-forge: https://anaconda.org/conda-forge/radical.asyncflow

Seekrflow

Biomolecular rate-constant estimation workflows built on SEEKR.

• Repository: https://github.com/seekrcentral/seekrflow

• Documentation: https://seekrflow.readthedocs.io/en/latest

• SEEKR documentation: https://seekr.readthedocs.io/en/latest/

• conda-forge: https://anaconda.org/conda-forge/seekrflow (conda-only: depends on SEEKR and OpenMM)

QuAcc

Quantum chemistry workflow recipes for high-throughput materials science.

• Repository: https://github.com/Quantum-Accelerators/quacc

• Documentation: https://quantum-accelerators.github.io/quacc/

• PyPI: https://pypi.org/project/quacc/

• conda-forge: https://anaconda.org/conda-forge/quacc

Rootstock

Run pre-deployed machine learned interatomic potentials on HPC clusters without managing conflicting dependencies.

• Repository: https://github.com/Garden-AI/rootstock

• Model catalog: https://garden-ai.github.io/almanac/

• PyPI: https://pypi.org/project/rootstock/

• conda-forge: https://anaconda.org/conda-forge/rootstock

DeepDriveSim

Deep learning-driven Adaptive Simulations.

• Repository: https://github.com/radical-collaboration/DeepDriveSim

• Documentation: https://radical-collaboration.github.io/DeepDriveSim/

• PyPI: https://pypi.org/project/ddsim/

• conda-forge: https://anaconda.org/conda-forge/ddsim