<div align="center">
<a href="https://github.com/CrayLabs/SmartSim"><img src="https://raw.githubusercontent.com/CrayLabs/SmartSim/master/doc/images/SmartSim_Large.png" width="90%"><img></a>
<br />
<br />
<div display="inline-block">
<a href="https://github.com/CrayLabs/SmartRedis"><b>Home</b></a>
<a href="https://www.craylabs.org/docs/installation_instructions/basic.html#"><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>
<a href="https://github.com/CrayLabs"><b>Cray Labs</b></a>
</div>
<br />
<br />
</div>
[](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 | 3.9, 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#)
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)
- [Redis-plus-plus](https://github.com/sewenew/redis-plus-plus)
## 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},
}
Raw data
{
"_id": null,
"home_page": "https://github.com/CrayLabs/SmartRedis",
"name": "smartredis",
"maintainer": null,
"docs_url": null,
"requires_python": "<3.12,>=3.9",
"maintainer_email": null,
"keywords": "redis, clients, hpc, ai, deep learning",
"author": "CrayLabs, a Hewlett Packard Enterprise OSS Organization",
"author_email": "craylabs@hpe.com",
"download_url": "https://files.pythonhosted.org/packages/c6/fe/c0288d25f03a9b839086a7222594af84e4cee8a5a60a4183f69f298aeb25/smartredis-0.6.1.tar.gz",
"platform": null,
"description": "\n\n<div align=\"center\">\n <a href=\"https://github.com/CrayLabs/SmartSim\"><img src=\"https://raw.githubusercontent.com/CrayLabs/SmartSim/master/doc/images/SmartSim_Large.png\" width=\"90%\"><img></a>\n <br />\n <br />\n <div display=\"inline-block\">\n <a href=\"https://github.com/CrayLabs/SmartRedis\"><b>Home</b></a> \n <a href=\"https://www.craylabs.org/docs/installation_instructions/basic.html#\"><b>Install</b></a> \n <a href=\"https://www.craylabs.org/docs/smartredis.html\"><b>Documentation</b></a> \n <a href=\"https://join.slack.com/t/craylabs/shared_invite/zt-nw3ag5z5-5PS4tIXBfufu1bIvvr71UA\"><b>Slack</b></a> \n <a href=\"https://github.com/CrayLabs\"><b>Cray Labs</b></a> \n </div>\n <br />\n <br />\n</div>\n\n\n[](https://github.com/CrayLabs/SmartRedis/blob/master/LICENSE.md)\n\n\n\n\n\n[](https://github.com/psf/black)\n[](https://codecov.io/gh/CrayLabs/SmartRedis)\n----------\n# SmartRedis\n\nSmartRedis is a collection of Redis clients that support\nRedisAI capabilities and include additional\nfeatures for high performance computing (HPC) applications.\nSmartRedis provides clients in the following languages:\n\n| Language | Version/Standard |\n|------------|:----------------------------------------------:|\n| Python | 3.9, 3.10, 3.11 |\n| C++ | C++17 |\n| C | C99 |\n| Fortran | Fortran 2018 (GNU/Intel), 2003 (PGI/Nvidia) |\n\nSmartRedis is used in the [SmartSim library](https://github.com/CrayLabs/SmartSim).\nSmartSim makes it easier to use common Machine Learning (ML) libraries like\nPyTorch and TensorFlow in numerical simulations at scale. SmartRedis connects\nthese simulations to a Redis database or Redis database cluster for\ndata storage, script execution, and model evaluation. While SmartRedis\ncontains features for simulation workflows on supercomputers, SmartRedis\nis fully functional as a RedisAI client library and can be used without\nSmartSim in any Python, C++, C, or Fortran project.\n\n## Using SmartRedis\n\nSmartRedis installation instructions are currently hosted as part of the\n[SmartSim library installation instructions](https://www.craylabs.org/docs/installation_instructions/basic.html#)\nAdditionally, detailed [API documents](https://www.craylabs.org/docs/api/smartredis_api.html) are also available as\npart of the SmartSim documentation.\n\n## Dependencies\n\nSmartRedis utilizes the following libraries:\n\n - [NumPy](https://github.com/numpy/numpy)\n - [Hiredis](https://github.com/redis/hiredis)\n - [Redis-plus-plus](https://github.com/sewenew/redis-plus-plus)\n\n## Publications\n\nThe following are public presentations or publications using SmartRedis\n\n - [Collaboration with NCAR - CGD Seminar](https://www.youtube.com/watch?v=2e-5j427AS0)\n - [Using Machine Learning in HPC Simulations - paper](https://www.sciencedirect.com/science/article/pii/S1877750322001065)\n - [Relexi \u2014 A scalable open source reinforcement learning framework for high-performance computing - paper](https://www.sciencedirect.com/science/article/pii/S2665963822001063)\n\n## Cite\n\nPlease use the following citation when referencing SmartSim, SmartRedis, or any SmartSim related work:\n\n Partee et al., \"Using Machine Learning at scale in numerical simulations with SmartSim:\n An application to ocean climate modeling\",\n Journal of Computational Science, Volume 62, 2022, 101707, ISSN 1877-7503.\n Open Access: https://doi.org/10.1016/j.jocs.2022.101707.\n\n### bibtex\n\n @article{PARTEE2022101707,\n title = {Using Machine Learning at scale in numerical simulations with SmartSim:\n An application to ocean climate modeling},\n journal = {Journal of Computational Science},\n volume = {62},\n pages = {101707},\n year = {2022},\n issn = {1877-7503},\n doi = {https://doi.org/10.1016/j.jocs.2022.101707},\n url = {https://www.sciencedirect.com/science/article/pii/S1877750322001065},\n author = {Sam Partee and Matthew Ellis and Alessandro Rigazzi and Andrew E. Shao\n and Scott Bachman and Gustavo Marques and Benjamin Robbins},\n keywords = {Deep learning, Numerical simulation, Climate modeling, High performance computing, SmartSim},\n }\n",
"bugtrack_url": null,
"license": "BSD 2-Clause License",
"summary": "RedisAI clients for SmartSim",
"version": "0.6.1",
"project_urls": {
"Documentation": "https://www.craylabs.org",
"Homepage": "https://github.com/CrayLabs/SmartRedis",
"Source": "https://github.com/CrayLabs/SmartRedis"
},
"split_keywords": [
"redis",
" clients",
" hpc",
" ai",
" deep learning"
],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "bae7d92f3d97cfe07b62b9c4e441683b81e3496039a2946d8e6d1d578306c0ec",
"md5": "4c1665c788d86b59fc00847a3905f106",
"sha256": "bbe27c244d4e0393d92a2c80f6f73f3cdf835bab3f310eb6a0965649b4998e54"
},
"downloads": -1,
"filename": "smartredis-0.6.1-cp310-cp310-macosx_10_9_x86_64.whl",
"has_sig": false,
"md5_digest": "4c1665c788d86b59fc00847a3905f106",
"packagetype": "bdist_wheel",
"python_version": "cp310",
"requires_python": "<3.12,>=3.9",
"size": 828583,
"upload_time": "2024-09-27T20:15:36",
"upload_time_iso_8601": "2024-09-27T20:15:36.089193Z",
"url": "https://files.pythonhosted.org/packages/ba/e7/d92f3d97cfe07b62b9c4e441683b81e3496039a2946d8e6d1d578306c0ec/smartredis-0.6.1-cp310-cp310-macosx_10_9_x86_64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "1d69b8c0156905d0904d99990d8a914fb641bd989028fd579555de9626de78ac",
"md5": "7b30aeffc020fe4d6b365524c600891f",
"sha256": "04a1837bdd3154c3b90b2fb05ee35d8237ec57421cdd8fa794bdbb258dba1151"
},
"downloads": -1,
"filename": "smartredis-0.6.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl",
"has_sig": false,
"md5_digest": "7b30aeffc020fe4d6b365524c600891f",
"packagetype": "bdist_wheel",
"python_version": "cp310",
"requires_python": "<3.12,>=3.9",
"size": 876342,
"upload_time": "2024-09-27T20:15:37",
"upload_time_iso_8601": "2024-09-27T20:15:37.504073Z",
"url": "https://files.pythonhosted.org/packages/1d/69/b8c0156905d0904d99990d8a914fb641bd989028fd579555de9626de78ac/smartredis-0.6.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "bb19160b853f145238b6630c2ea97471436c0c07b81aa8198ac874e9587fc260",
"md5": "2309db01134b36cf9d45e3aaf66ab9a5",
"sha256": "8c062de15955a5ceb8132f9028a216584d906e7cecbfcaecdd9ad894c139e69a"
},
"downloads": -1,
"filename": "smartredis-0.6.1-cp310-cp310-musllinux_1_2_x86_64.whl",
"has_sig": false,
"md5_digest": "2309db01134b36cf9d45e3aaf66ab9a5",
"packagetype": "bdist_wheel",
"python_version": "cp310",
"requires_python": "<3.12,>=3.9",
"size": 1889156,
"upload_time": "2024-09-27T20:15:39",
"upload_time_iso_8601": "2024-09-27T20:15:39.190451Z",
"url": "https://files.pythonhosted.org/packages/bb/19/160b853f145238b6630c2ea97471436c0c07b81aa8198ac874e9587fc260/smartredis-0.6.1-cp310-cp310-musllinux_1_2_x86_64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "23a4e6c98d4ddb0269c808ed1029485b88a97dc253460f35e658735323237d61",
"md5": "f62f8db019dd3f4f287c3eec3c5e761a",
"sha256": "3a2f5e90a2f6e4a40cd2874e03209c083c6cdea8c4dfa9407c8318ef0f0c20ee"
},
"downloads": -1,
"filename": "smartredis-0.6.1-cp311-cp311-macosx_10_9_x86_64.whl",
"has_sig": false,
"md5_digest": "f62f8db019dd3f4f287c3eec3c5e761a",
"packagetype": "bdist_wheel",
"python_version": "cp311",
"requires_python": "<3.12,>=3.9",
"size": 829636,
"upload_time": "2024-09-27T20:15:40",
"upload_time_iso_8601": "2024-09-27T20:15:40.454368Z",
"url": "https://files.pythonhosted.org/packages/23/a4/e6c98d4ddb0269c808ed1029485b88a97dc253460f35e658735323237d61/smartredis-0.6.1-cp311-cp311-macosx_10_9_x86_64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "bd03e2877e383d8023bcba897e477a4e0d65efd5271e93133cd5c2161cfc4fa1",
"md5": "4802c9d64b94b4b19ebf196fc2401734",
"sha256": "4e77b53be6c05c30189dde40fb30cc21a11e96015d6add54e6b2ecc2d679259f"
},
"downloads": -1,
"filename": "smartredis-0.6.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl",
"has_sig": false,
"md5_digest": "4802c9d64b94b4b19ebf196fc2401734",
"packagetype": "bdist_wheel",
"python_version": "cp311",
"requires_python": "<3.12,>=3.9",
"size": 877165,
"upload_time": "2024-09-27T20:15:41",
"upload_time_iso_8601": "2024-09-27T20:15:41.499052Z",
"url": "https://files.pythonhosted.org/packages/bd/03/e2877e383d8023bcba897e477a4e0d65efd5271e93133cd5c2161cfc4fa1/smartredis-0.6.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "149316d52ec9ce925dafd06c91fa67d3dbc7026a91f9aac89bdb72d30b24bfab",
"md5": "0beccc4116b2ded60908b530bc44378b",
"sha256": "882860a0047a6b0cb79a74f0d6ba89378c2934148af378eb7788548aec72985a"
},
"downloads": -1,
"filename": "smartredis-0.6.1-cp311-cp311-musllinux_1_2_x86_64.whl",
"has_sig": false,
"md5_digest": "0beccc4116b2ded60908b530bc44378b",
"packagetype": "bdist_wheel",
"python_version": "cp311",
"requires_python": "<3.12,>=3.9",
"size": 1889742,
"upload_time": "2024-09-27T20:15:43",
"upload_time_iso_8601": "2024-09-27T20:15:43.346285Z",
"url": "https://files.pythonhosted.org/packages/14/93/16d52ec9ce925dafd06c91fa67d3dbc7026a91f9aac89bdb72d30b24bfab/smartredis-0.6.1-cp311-cp311-musllinux_1_2_x86_64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "4b9a3beabcd2dafd6e5a492a9d6ca69c6250d93ee9f0027c554a6e880f7ecf1b",
"md5": "5ec19285f42e4484447a2b2766e888f8",
"sha256": "5b93b9cf2a498505501f590b3dcd68d37ea983ea7eb700032d94b91161fda8e1"
},
"downloads": -1,
"filename": "smartredis-0.6.1-cp39-cp39-macosx_10_9_x86_64.whl",
"has_sig": false,
"md5_digest": "5ec19285f42e4484447a2b2766e888f8",
"packagetype": "bdist_wheel",
"python_version": "cp39",
"requires_python": "<3.12,>=3.9",
"size": 828795,
"upload_time": "2024-09-27T20:15:45",
"upload_time_iso_8601": "2024-09-27T20:15:45.240186Z",
"url": "https://files.pythonhosted.org/packages/4b/9a/3beabcd2dafd6e5a492a9d6ca69c6250d93ee9f0027c554a6e880f7ecf1b/smartredis-0.6.1-cp39-cp39-macosx_10_9_x86_64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "84ff0677344f31f0f621a501db7abf7d67df7e62019b15953d7a6a48f8b3be0d",
"md5": "4e807ec3ff17c1a94078e44c6daf9f17",
"sha256": "3deed1c38c0c51dfc9132d04fecc1dc5d20ee1545ed542e1bbfe675cb9088fba"
},
"downloads": -1,
"filename": "smartredis-0.6.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl",
"has_sig": false,
"md5_digest": "4e807ec3ff17c1a94078e44c6daf9f17",
"packagetype": "bdist_wheel",
"python_version": "cp39",
"requires_python": "<3.12,>=3.9",
"size": 876540,
"upload_time": "2024-09-27T20:15:46",
"upload_time_iso_8601": "2024-09-27T20:15:46.385658Z",
"url": "https://files.pythonhosted.org/packages/84/ff/0677344f31f0f621a501db7abf7d67df7e62019b15953d7a6a48f8b3be0d/smartredis-0.6.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "27f7b64c9dad75d70c5f4fca14798e772492ffded80193b27ed701ccdff43c4a",
"md5": "fe6b72e0b66e07642093fe21279b5881",
"sha256": "fbcea605d7a1bd5b475de2ae6136a932585d82c3fcfee5a05ccc0372acb31f30"
},
"downloads": -1,
"filename": "smartredis-0.6.1-cp39-cp39-musllinux_1_2_x86_64.whl",
"has_sig": false,
"md5_digest": "fe6b72e0b66e07642093fe21279b5881",
"packagetype": "bdist_wheel",
"python_version": "cp39",
"requires_python": "<3.12,>=3.9",
"size": 1889367,
"upload_time": "2024-09-27T20:15:48",
"upload_time_iso_8601": "2024-09-27T20:15:48.255531Z",
"url": "https://files.pythonhosted.org/packages/27/f7/b64c9dad75d70c5f4fca14798e772492ffded80193b27ed701ccdff43c4a/smartredis-0.6.1-cp39-cp39-musllinux_1_2_x86_64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "c6fec0288d25f03a9b839086a7222594af84e4cee8a5a60a4183f69f298aeb25",
"md5": "1a95ec9b66c31054459ec5f7a9401fd9",
"sha256": "88bd5425abf03830382143c9b9acc2ee362e8251eafb46de48c515e9d6a373f5"
},
"downloads": -1,
"filename": "smartredis-0.6.1.tar.gz",
"has_sig": false,
"md5_digest": "1a95ec9b66c31054459ec5f7a9401fd9",
"packagetype": "sdist",
"python_version": "source",
"requires_python": "<3.12,>=3.9",
"size": 215934,
"upload_time": "2024-09-27T20:15:49",
"upload_time_iso_8601": "2024-09-27T20:15:49.423304Z",
"url": "https://files.pythonhosted.org/packages/c6/fe/c0288d25f03a9b839086a7222594af84e4cee8a5a60a4183f69f298aeb25/smartredis-0.6.1.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2024-09-27 20:15:49",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "CrayLabs",
"github_project": "SmartRedis",
"travis_ci": false,
"coveralls": false,
"github_actions": true,
"lcname": "smartredis"
}
JSON
