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https://github.com/CrayLabs/SmartRedis

SmartSim Infrastructure Library Clients.
https://github.com/CrayLabs/SmartRedis

hpc machine-learning redis redis-client redisai

Last synced: 3 months ago
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SmartSim Infrastructure Library Clients.

Host: GitHub
URL: https://github.com/CrayLabs/SmartRedis
Owner: CrayLabs
License: bsd-2-clause
Created: 2020-11-09T06:39:52.000Z (about 4 years ago)
Default Branch: develop
Last Pushed: 2024-07-18T16:28:44.000Z (4 months ago)
Last Synced: 2024-07-18T21:25:12.615Z (4 months ago)
Topics: hpc, machine-learning, redis, redis-client, redisai
Language: C++
Homepage:
Size: 7.72 MB
Stars: 53
Watchers: 8
Forks: 24
Open Issues: 39
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE.md

Awesome Lists containing this project

Awesome-HPE-Open-Source - SmartRedis - SmartRedis is a collection of Redis clients that support RedisAI capabilities and include additional features for high performance computing (HPC) applications. (Open Source Projects at HPE)

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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](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},

        }