{"id":13653316,"url":"https://github.com/aistairc/aiaccel","last_synced_at":"2026-03-01T02:22:31.663Z","repository":{"id":37619005,"uuid":"496933254","full_name":"aistairc/aiaccel","owner":"aistairc","description":"A hyperparameter optimization library for the ABCI.","archived":false,"fork":false,"pushed_at":"2025-04-17T16:10:20.000Z","size":13538,"stargazers_count":26,"open_issues_count":1,"forks_count":4,"subscribers_count":5,"default_branch":"main","last_synced_at":"2025-04-17T21:14:44.243Z","etag":null,"topics":["abci","hyperparameter-optimization","nelder-mead-algorithm"],"latest_commit_sha":null,"homepage":"https://aistairc.github.io/aiaccel/","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/aistairc.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,"zenodo":null}},"created_at":"2022-05-27T09:16:17.000Z","updated_at":"2025-04-15T04:33:16.000Z","dependencies_parsed_at":"2023-12-04T05:23:32.819Z","dependency_job_id":"62111f31-ac52-41b8-89fb-43e8a1ff7593","html_url":"https://github.com/aistairc/aiaccel","commit_stats":null,"previous_names":[],"tags_count":1,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/aistairc%2Faiaccel","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/aistairc%2Faiaccel/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/aistairc%2Faiaccel/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/aistairc%2Faiaccel/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/aistairc","download_url":"https://codeload.github.com/aistairc/aiaccel/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":250385015,"owners_count":21421831,"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":["abci","hyperparameter-optimization","nelder-mead-algorithm"],"created_at":"2024-08-02T02:01:08.605Z","updated_at":"2026-03-01T02:22:26.592Z","avatar_url":"https://github.com/aistairc.png","language":"Python","funding_links":[],"categories":["Python"],"sub_categories":[],"readme":"\u003cdiv align=\"center\"\u003e\u003cimg src=\"https://raw.githubusercontent.com/aistairc/aiaccel/master/docs/image/logo_aiaccel.png\" width=\"400\"/\u003e\u003c/div\u003e\n\n# aiaccel: an HPO library for ABCI\n[![GitHub license](https://img.shields.io/github/license/aistairc/aiaccel.svg)](https://github.com/aistairc/aiaccel)\n[![Supported Python version](https://img.shields.io/badge/Python-3.8-blue)](https://github.com/aistairc/aiaccel)\n[![Publish on GitHub Pages](https://github.com/aistairc/aiaccel/actions/workflows/publish_pages.yaml/badge.svg)](https://github.com/aistairc/aiaccel/actions/workflows/publish_pages.yaml)\n![CI status](https://github.com/aistairc/aiaccel/actions/workflows/actions.yaml/badge.svg)\n\n[**日本語  (Japanese)**](https://github.com/aistairc/aiaccel/blob/main/README_JP.md)\n\n\nA hyperparameter optimization library for [AI Bridging Cloud Infrastructure (ABCI)](https://abci.ai/).\nThis software solves hyperparameter optimizations related to AI technologies including deep learning and multi-agent simulation.\nThe software currently supports five optimization algorithms: random search, grid search, sobol sequence, nelder-mead method, and TPE.\n\n# Installation\nThe software can be installed using `pip`.\n~~~bash\n\u003e pip install git+https://github.com/aistairc/aiaccel.git\n~~~\n\n# Getting started\n\n## Running on a local computer\nAn example for optimizing a simple function (i.e., sphere function) on a local computer.\n\n\n0. (Optional) Install [Virtualenv](https://virtualenv.pypa.io/en/latest/) and create a virtual environment. \n    ~~~bash\n    \u003e python3 -m venv optenv\n    \u003e source optenv/bin/activate\n    ~~~\n\n1. Install `aiaccel`\n    ~~~bash\n    \u003e pip install git+https://github.com/aistairc/aiaccel.git \n    ~~~\n\n2. Create a workspace and copy the sphere example on the repository.\n    ~~~bash\n    \u003e mkdir your_workspace_directory\n    \u003e cd your_workspace_directory\n    \u003e git clone https://github.com/aistairc/aiaccel.git \n    \u003e cp -R ./aiaccel/examples .\n    \u003e cd examples\n    \u003e ls\n    sphere\n\n    \u003e cd sphere\n    \u003e ls\n    config.yaml         user.py\n    ~~~\n\n3. Run the parameter optimization\n    ~~~bash\n    \u003e aiaccel-start --config config.yaml\n    ~~~\n\n    or\n\n    ~~~bash\n    \u003e python -m aiaccel.cli.start --config config.yaml\n    ~~~\n\n    Tips: You can start after cleaning the workspace directory using `--clean`.\n    ~~~bash\n    \u003e aiaccel-start --config config.yaml --clean\n    ~~~\n\n4. Wait for the program to finish and check the optimization results.\n    ~~~bash\n    \u003e ls ./work\n    abci_output         alive               hp                  lock\n    log                 result              runner              state\n\n    \u003e cat ./work/result/final_result.result\n    ~~~\n\n5. If you want to change configurations, edit `config.yaml`.\n    ~~~bash\n    \u003e vi config.yaml\n    ~~~\n\n## Running on ABCI\nThis tutorial describes how to run examples/sphere on ABCI.\n\n1. First, setup python environment following [the ABCI Users Guide](https://docs.abci.ai/en/python/):\n    ~~~bash\n    \u003e module load python/3.11/3.11.2\n    \u003e python3 -m venv optenv\n    \u003e source optenv/bin/activate\n    ~~~\n\n2. Prepare the workspace by following Steps 1 and 2 in [Running on a local computer](https://github.com/aistairc/aiaccel#Running-on-a-local-computer).\n\n3. Please confirm the configuration file before running master.\n    ```yaml\n    resource:\n        type: \"abci\"\n        num_workers: 4\n    ```\n\n4. Run on an (interactive) job\n    ~~~bash\n    \u003e aiaccel-start --config config.yaml\n    ~~~\n\n5. If you want to check the running jobs, please refer the [ABCI User Guide](https://docs.abci.ai/en/job-execution/#show-the-status-of-batch-jobs).\n\n\n## Others\n- Check the progress\n    ~~~bash\n    \u003e aiaccel-view --config config.yaml\n    ~~~\n\n- Display simple graphs\n    ~~~bash\n    \u003e aiaccel-plot --config config.yaml\n    ~~~\n\n- Output results to workspace/results.csv.\n    ~~~bash\n    \u003e aiaccel-report --config config.yaml\n    ~~~\n\n\n# Acknowledgement\n* Part of this software was developed in a project commissioned by the New Energy and Industrial Technology Development Organization (NEDO).\n* aiaccel is built with the help of Optuna.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Faistairc%2Faiaccel","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Faistairc%2Faiaccel","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Faistairc%2Faiaccel/lists"}