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autocalibration

OpenSource tools from the E3SM Autotuning team

https://github.com/e3sm-project/autocalibration

Science Score: 44.0%

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    Found CITATION.cff file
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    Low similarity (10.8%) to scientific vocabulary
Last synced: 6 months ago · JSON representation ·

Repository

OpenSource tools from the E3SM Autotuning team

Basic Info
  • Host: GitHub
  • Owner: E3SM-Project
  • License: bsd-3-clause
  • Language: Python
  • Default Branch: main
  • Size: 73.1 MB
Statistics
  • Stars: 0
  • Watchers: 5
  • Forks: 0
  • Open Issues: 0
  • Releases: 2
Created over 2 years ago · Last pushed about 2 years ago
Metadata Files
Readme License Citation

README.md

AutoCalibration

OpenSource tools from the E3SM Autotuning team

This is a working repository for the "Autotuning" Epic, started in FY21 under the NGD Software and Algorithms portfolio. The project is working to add some automation to the GCM tuning process using UQ and ML algorithms.

POCs are Benjamin M. Wagman, Drew Yarger, Lyndsay Shand, and Andy Salinger (formerly K. Chowdhary).

  • The file "requirements.txt" sets up the Python packages and environment that we use for autotuning, resulting in the package versions described by "autotuning_environment.yaml". Our code depends on the "tesuract" and "clif" packages built by K. Chowdhary as specified in "requirements.txt".

  • The "dakota_e3sm/" folder contains the code and configuration for running the perturbed parameter ensembles (PPEs) in our work.

  • The "src/" folder contains code for the surrogate construction, optimization, and comparison of autotuning and default parameter sets.

    • "src/data/": code that processes the time-averaged spatial fields from the PPE, observations, and miscellaneous model runs. By default, the resulting files are stored in "data/".
    • "src/models/": in "surrogatelatest.py", code that creates surrogate and optimizes parameters, depending on code in "preprocessing.py", "postprocessing.py", and "optimization.py". The file "configlatest.yaml" contains parameters for the surrogate (for example, which fields to use) so that one can construct a surrogate and optimize parameters using "python surrogatelatest.py configlatest.yaml".
    • "src/mcmc/": code that runs Markov Chain Monte Carlo based on the optimization function set up in "configmcmc.yaml", so that one can construct a surrogate, optimize parameters, and run MCMC using "python mcmc.py configmcmc.yaml".
    • "src/eda/": code for exploratory data analysis of the surrogate, perturbed parameter ensemble, and optimization results. This folder contains scripts to process the results after creating a surrogate, and is mostly based in R instead of Python.
    • "src/visualization/": code that creates visualizations of predicted fields for the surrogate.

  • As is currently set up, the processed data is expected to be placed in "data/", and the results from the autotuning procedure are expected to be placed in the "surrogate_models/" folder.

Owner

  • Name: Energy Exascale Earth System Model Project
  • Login: E3SM-Project
  • Kind: organization

Repositories associated with the E3SM Project

Citation (CITATION.cff) 

cff-version: 1.2.0
title: AutoCalibration
version: 1.0.1
message: '  If you use this software, please cite it using the metadata from this file.'
type: software
authors:
  - given-names: E3SM
    family-names: AutoCalibration
doi: 10.5281/zenodo.8284881  
repository-code: 'https://github.com/E3SM-Project/AutoCalibration'
url: 'https://e3sm.org'
date-released: 2023-08-25

GitHub Events

Total
Last Year

Committers

Last synced: about 2 years ago

All Time
  • Total Commits: 16
  • Total Committers: 2
  • Avg Commits per committer: 8.0
  • Development Distribution Score (DDS): 0.063
Past Year
  • Commits: 16
  • Committers: 2
  • Avg Commits per committer: 8.0
  • Development Distribution Score (DDS): 0.063
Top Committers
Name Email Commits
Benjamin Moore Wagman b****a@s****v 15
Andy Salinger a****n@s****v 1
Committer Domains (Top 20 + Academic)
sandia.gov: 2

Issues and Pull Requests

Last synced: 11 months ago

All Time
  • Total issues: 0
  • Total pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Total issue authors: 0
  • Total pull request authors: 0
  • Average comments per issue: 0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 0
  • Pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 0
  • Pull request authors: 0
  • Average comments per issue: 0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
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Dependencies

requirements.txt pypi
  • condaactivateautotuning *
  • condacreate--nameautotuning--clonee3sm_unified_1.7.1_chrysalis *
  • condaenvexport >autotuning_environment.yml
  • condalist--explicit >autotuning_spec_file.txt
  • exportPYTHONNOUSERSITE =1
  • source *
setup.py pypi