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<div display="inline-block">
<a href="https://github.com/CrayLabs/SmartRedis"><b>Home</b></a>
<a href="https://www.craylabs.org/docs/installation.html#smartredis"><b>Install</b></a>
<a href="https://www.craylabs.org/docs/smartredis.html"><b>Documentation</b></a>
<a href="https://join.slack.com/t/craylabs/shared_invite/zt-nw3ag5z5-5PS4tIXBfufu1bIvvr71UA"><b>Slack</b></a>
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[](https://github.com/CrayLabs/SmartRedis/blob/master/LICENSE.md)
[](https://github.com/psf/black)
[](https://codecov.io/gh/CrayLabs/SmartRedis)
----------
# 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 | 3.8, 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](https://github.com/CrayLabs/SmartSim).
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](https://www.craylabs.org/docs/installation_instructions/basic.html#smartredis)
Additionally, detailed [API documents](https://www.craylabs.org/docs/api/smartredis_api.html) are also available as
part of the SmartSim documentation.
## Dependencies
SmartRedis utilizes the following libraries:
- [NumPy](https://github.com/numpy/numpy)
- [Hiredis](https://github.com/redis/hiredis) 1.1.0
- [Redis-plus-plus](https://github.com/sewenew/redis-plus-plus) 1.3.5
## Publications
The following are public presentations or publications using SmartRedis
- [Collaboration with NCAR - CGD Seminar](https://www.youtube.com/watch?v=2e-5j427AS0)
- [Using Machine Learning in HPC Simulations - paper](https://www.sciencedirect.com/science/article/pii/S1877750322001065)
- [Relexi — A scalable open source reinforcement learning framework for high-performance computing - paper](https://www.sciencedirect.com/science/article/pii/S2665963822001063)
## 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},
}
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