smartredis 0.6.1

RedisAI clients for SmartSim

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SmartRedis

SmartRedis is a collection of Redis clients that support RedisAI capabilities and include additional features for high performance computing (HPC) applications. SmartRedis provides clients in the following languages:

Language	Version/Standard
Python	3.9, 3.10, 3.11
C++	C++17
C	C99
Fortran	Fortran 2018 (GNU/Intel), 2003 (PGI/Nvidia)

SmartRedis is used in the SmartSim library. SmartSim makes it easier to use common Machine Learning (ML) libraries like PyTorch and TensorFlow in numerical simulations at scale. SmartRedis connects these simulations to a Redis database or Redis database cluster for data storage, script execution, and model evaluation. While SmartRedis contains features for simulation workflows on supercomputers, SmartRedis is fully functional as a RedisAI client library and can be used without SmartSim in any Python, C++, C, or Fortran project.

Using SmartRedis

SmartRedis installation instructions are currently hosted as part of the SmartSim library installation instructions Additionally, detailed API documents are also available as part of the SmartSim documentation.

Dependencies

SmartRedis utilizes the following libraries:

• NumPy
• Hiredis
• Redis-plus-plus

Publications

The following are public presentations or publications using SmartRedis

• Collaboration with NCAR - CGD Seminar
• Using Machine Learning in HPC Simulations - paper
• Relexi — A scalable open source reinforcement learning framework for high-performance computing - paper

Cite

Please use the following citation when referencing SmartSim, SmartRedis, or any SmartSim related work:

Partee et al., "Using Machine Learning at scale in numerical simulations with SmartSim:
An application to ocean climate modeling",
Journal of Computational Science, Volume 62, 2022, 101707, ISSN 1877-7503.
Open Access: https://doi.org/10.1016/j.jocs.2022.101707.

bibtex

@article{PARTEE2022101707,
    title = {Using Machine Learning at scale in numerical simulations with SmartSim:
    An application to ocean climate modeling},
    journal = {Journal of Computational Science},
    volume = {62},
    pages = {101707},
    year = {2022},
    issn = {1877-7503},
    doi = {https://doi.org/10.1016/j.jocs.2022.101707},
    url = {https://www.sciencedirect.com/science/article/pii/S1877750322001065},
    author = {Sam Partee and Matthew Ellis and Alessandro Rigazzi and Andrew E. Shao
    and Scott Bachman and Gustavo Marques and Benjamin Robbins},
    keywords = {Deep learning, Numerical simulation, Climate modeling, High performance computing, SmartSim},
    }