{"id":18762637,"url":"https://github.com/deephyper/dmobo-sc24","last_synced_at":"2025-06-30T10:36:08.376Z","repository":{"id":232937250,"uuid":"784594561","full_name":"deephyper/dmobo-sc24","owner":"deephyper","description":"Experimental repository for the paper on Parallel Multi-Objective Bayesian Hyperparameter Optimization with Normalized and Bounded Objectives","archived":false,"fork":false,"pushed_at":"2024-04-12T09:58:26.000Z","size":40,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":2,"default_branch":"main","last_synced_at":"2025-05-20T18:58:07.791Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"bsd-3-clause","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/deephyper.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2024-04-10T06:50:12.000Z","updated_at":"2024-04-15T02:56:14.000Z","dependencies_parsed_at":null,"dependency_job_id":"bf0eb035-9556-491c-b730-23cec952e492","html_url":"https://github.com/deephyper/dmobo-sc24","commit_stats":null,"previous_names":["deephyper/dmobo-sc24"],"tags_count":3,"template":false,"template_full_name":null,"purl":"pkg:github/deephyper/dmobo-sc24","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/deephyper%2Fdmobo-sc24","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/deephyper%2Fdmobo-sc24/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/deephyper%2Fdmobo-sc24/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/deephyper%2Fdmobo-sc24/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/deephyper","download_url":"https://codeload.github.com/deephyper/dmobo-sc24/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/deephyper%2Fdmobo-sc24/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":262756841,"owners_count":23359590,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-11-07T18:22:33.155Z","updated_at":"2025-06-30T10:36:08.352Z","avatar_url":"https://github.com/deephyper.png","language":"Python","readme":"# Parallel Multi-Objective Bayesian Hyperparameter Optimization with Normalized and Bounded Objectives\n\nExperimental repository for the paper on \"Parallel Multi-Objective Bayesian Hyperparameter Optimization with Normalized and Bounded Objectives\".\n\n\n## Installation\n\nThe installation requires Miniconda or Anaconda. See [Miniconda Documentation](https://docs.anaconda.com/free/miniconda/miniconda-install/).\n\n### Local\n\nFrom the root of this repository:\n\n```console\nmkdir build \u0026\u0026 cd build/\n./install/local.sh\n```\n\n### Polaris (ALCF)\n\nFrom the root of this repository:\n\n```console\nmkdir build \u0026\u0026 cd build/\n../install/local.sh\n```\n\n## Easy local reproducible examples\n\nExperiments from Figures 3 and 4 are easy to reproduce locally by running the following commands after following the [Local Installation](#local).\n\n```console\ncd experiments/local/jobs/\n./run-all.sh\n```\n\nThe produced outputs are placed in the `experiments/local/output/` folder.\nThen the plotting of the figures can be done with:\n\n```console\ncd ..\npython plot.py\n```\n\nThe produced figures are placed in the `experiments/local/figures/` folder.\n\n## Large-scale experiments on Polaris\n\nThis section explains how to reproduce experiments run on Polaris on the Combo benchmark. An example installation script is provided for Polaris at the Argonne Leadership Computing Facility. This script can be used as an example for other HPC systems.\n\nFrom a login node of Polaris and the root of this repository, the following commands can be run to install:\n\n```console\nmkdir build \u0026\u0026 cd build/\n../install/polaris.sh\n```\n\nOnce the installation is complete go to Polaris experimental directory:\n\n```console\ncd experiments/polaris/jobs/\n```\n\nFrom this directory, each job can be submitted with the command `qsub some-job-script.sh`. For example, if we want to submit D-MoBO with 10 nodes we run `qsub dmobo-10.sh`. Similarly, if we want to submit NSGAII with enforced constraints/penalty with 40 nodes we run `qsub nsgaii-P-40.sh`.\n\nOnce the experiments are completed the graphs can be created from `experiments/polaris` by running:\n\n```console\nsource ../../activate-dhenv.sh\npython plot.py\n```\n\nThe figures will then appear in the `experiments/polaris/figures` folder.","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdeephyper%2Fdmobo-sc24","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdeephyper%2Fdmobo-sc24","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdeephyper%2Fdmobo-sc24/lists"}