add software

content/en/docs/software/_index.md

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+---
+title: "Software"
+linkTitle: "Software"
+weight: 45
+menu:
+  main:
+    weight: 45
+---
+
+A list of software we use to make things easiers

content/en/docs/software/cloudmesh/index.md

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+---
+title: "cloudmesh"
+linkTitle: "cloudmesh"
+weight: 50
+description: >
+  cloudmesh is a flexible framework to develop cloud and HPC programs using python. It is based on a number of plugins.
+resources:
+  - src: "**.{png,jpg}"
+    title: "Image #:counter"
+---
+
+## Overview
+
+Cloudmesh allows the creation of an extensible commandline and commandshell tool based internally on a number of python APIs that can be loaded conveniently through plugins.
+
+Plugins useful for this effort include
+
+* cloudmesh-vpn  -- a convenient way to configure VPN
+* cloudmesh-common -- useful common libraries including a StopWatch for benchmarking 
+* cloudmesh-cmd5 -- a plugin manager that allows plugins to be integrated as commandline tool or command shell
+* cloudmesh-ee -- A pluging to create AI grid searchs using  LSF and SLURM jobs
+* cloudmesh-cc -- A plugin to allow benchmarks to be run in coordination on heterogeneous compute resources and multiple clusters
+
+Cloudmesh has over 100 plugins coordinated at http://github.com/cloudmesh
+
+[^1]
+
+## References
+
+[^1]: Gregor von Laszewski, J. P. Fleischer, and Geoffrey
+C. Fox. 2022. Hybrid Reusable Computational Analytics Workflow
+Management with Cloudmesh. https://doi.org/10.48550/ARXIV.2210.16941
+

content/en/docs/software/sabath/index.md

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+---
+title: "sabath"
+linkTitle: "sabath"
+weight: 50
+description: >
+  SABATH provides benchmarking infrastructure for evaluating scientific ML/AI models. It contains support for scientific machine learning surrogates from external repositories such as SciML-Bench.
+resources:
+  - src: "**.{png,jpg}"
+    title: "Image #:counter"
+---
+
+## Introduction
+
+SABATH provides benchmarking infrastructure for evaluating scientific ML/AI models. It contains support for scientific machine learning surrogates from external repositories such as SciML-Bench.
+
+The software dependences are explicitly exposed in the surrogate model definition, which allows the use of advanced optimization, communication, and hardware features. For example, distributed, multi-GPU training may be enabled with Horovod. Surrogate models may be implemented using TensorFlow, PyTorch, or MXNET frameworks.
+
+## Models
+
+Models are collected so far at
+
+* <https://github.com/icl-utk-edu/sabath/tree/main/var/sabath/assets/sabath/models>
+
+  * [autophasenn.json](https://github.com/icl-utk-edu/sabath/blob/main/var/sabath/assets/sabath/models/a/autophasenn.json)
+  * [cosmoflow.json](https://github.com/icl-utk-edu/sabath/blob/main/var/sabath/assets/sabath/models/c/cosmoflow.json)
+  * [miniWeatherML.json](https://github.com/icl-utk-edu/sabath/blob/main/var/sabath/assets/sabath/models/m/miniWeatherML.json)
+  * [nanoconfinement.json](https://github.com/icl-utk-edu/sabath/blob/main/var/sabath/assets/sabath/models/n/nanoconfinement.json)
+  * [ptychonn.json](https://github.com/icl-utk-edu/sabath/blob/main/var/sabath/assets/sabath/models/p/ptychonn.json)
+
+[^1]
+
+## References
+
+[^1]: <https://github.com/icl-utk-edu/sabath>