{"id":13473475,"url":"https://github.com/automl/DeepCAVE","last_synced_at":"2025-03-26T19:34:19.231Z","repository":{"id":38356339,"uuid":"364165755","full_name":"automl/DeepCAVE","owner":"automl","description":"An interactive framework to visualize and analyze your AutoML process in real-time.","archived":false,"fork":false,"pushed_at":"2025-03-06T16:29:28.000Z","size":60726,"stargazers_count":86,"open_issues_count":47,"forks_count":12,"subscribers_count":7,"default_branch":"main","last_synced_at":"2025-03-25T20:06:41.198Z","etag":null,"topics":["analysis","automl","hyperparameter-importance","hyperparameters","iml","interactive","real-time","sampling-bias","visualization"],"latest_commit_sha":null,"homepage":"https://automl.github.io/DeepCAVE/main/","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"apache-2.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/automl.png","metadata":{"files":{"readme":"README.md","changelog":"CHANGELOG.md","contributing":null,"funding":null,"license":"LICENSE.txt","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":"2021-05-04T06:51:06.000Z","updated_at":"2025-03-25T07:01:32.000Z","dependencies_parsed_at":"2023-02-18T05:31:12.693Z","dependency_job_id":"c645ae18-1606-477d-a360-f46f45e4e14d","html_url":"https://github.com/automl/DeepCAVE","commit_stats":null,"previous_names":[],"tags_count":15,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/automl%2FDeepCAVE","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/automl%2FDeepCAVE/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/automl%2FDeepCAVE/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/automl%2FDeepCAVE/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/automl","download_url":"https://codeload.github.com/automl/DeepCAVE/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":245535502,"owners_count":20631297,"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":["analysis","automl","hyperparameter-importance","hyperparameters","iml","interactive","real-time","sampling-bias","visualization"],"created_at":"2024-07-31T16:01:03.949Z","updated_at":"2025-03-26T19:34:14.214Z","avatar_url":"https://github.com/automl.png","language":"Python","readme":"\u003cimg src=\"docs/images/DeepCAVE_Logo_wide.png\" alt=\"Logo\"/\u003e \n\n# DeepCAVE\n\nDeepCAVE is a visualization and analysis tool for AutoML, with a particular focus on\nhyperparameter optimization (HPO). Built on the Dash framework, it offers a fully\ninteractive experience. The tool features a variety of plugins that enable efficient insight\ngeneration, aiding in understanding and debugging the application of HPO.\nAdditionally, the powerful run interface and the modularized plugin structure allow extending the \ntool at any time effortlessly.\n\n![Configuration Footprint](docs/images/plugins/configuration_footprint.png)\n\n\n## Installation\n\nFirst, make sure you have [redis-server](https://flaviocopes.com/redis-installation/) installed on\nyour computer.\n\nAfterwards, follow the instructions to install DeepCAVE:\n```bash\nconda create -n DeepCAVE python=3.9\nconda activate DeepCAVE\nconda install -c anaconda swig\npip install DeepCAVE\n```\n\nTo load runs created with Optuna or the BOHB optimizer, you need to install the\nrespective packages by running:\n```bash\npip install deepcave[optuna]\npip install deepcave[bohb]\n```\n\nTo try the examples for recording your results in DeepCAVE format, run this after installing:\n```bash\npip install deepcave[examples]\n```\n\nIf you want to contribute to DeepCAVE, use the following steps instead:\n```bash\ngit clone https://github.com/automl/DeepCAVE.git\ncd DeepCAVE\nconda create -n DeepCAVE python=3.9\nconda activate DeepCAVE\nconda install -c anaconda swig\nmake install-dev\n```\n\nPlease visit the [documentation](https://automl.github.io/DeepCAVE/main/installation.html) to get\nfurther help (e.g. if you cannot install redis server or if you are on MacOS).\n\n\n## Visualizing and Evaluating\n\nThe webserver as well as the queue/workers can be started by simply running:\n```bash\ndeepcave --open\n```\n\nIf you specify `--open` your webbrowser automatically opens at `http://127.0.0.1:8050/`.\nYou can find more arguments and information (like using custom configurations) in the\n[documentation](https://automl.github.io/DeepCAVE/main/getting_started.html).\n\n\n## Example runs\n\nDeepCAVE comes with some pre-evaluated runs to get a feeling for what DeepCAVE can do.\n\nIf you cloned the repository from GitHub via `git clone https://github.com/automl/DeepCAVE.git`,\nyou can try out some examples by exploring the `logs` directory inside the DeepCAVE dashboard.\nFor example, if you navigate to `logs/DeepCAVE`, you can view the run `mnist_pytorch` if you hit\nthe `+` button left to it.\n\n\n## Features\n\n### Interactive Interface\n- **Interactive Dashboard:**  \n  The dashboard runs in a webbrowser and allows you to self-analyze your optimization runs interactively.\n  \n- **Run Selection Interface:**  \n  Easily select runs from your working directory directly within the interface.\n  \n- **Integrated Help and Documentation:**  \n  Use help buttons and integrated documentation within the interface to better understand the plugins.\n\n### Comprehensive Analysis Tools\n- **Extensive Plugin Collection:**  \n  Explore a wide range of plugins for in-depth performance, hyperparameter, and budget analysis.\n\n- **Analysis of Running Processes:**  \n  Analyze and monitor optimization processes as they occur, with automatic detection of run changes.\n  \n- **Group Analysis:**  \n  Choose groups of runs for combined analysis to gain deeper insights.\n\n### Flexible and Modular Architecture\n- **Modular Plugin Architecture:**  \n  Benefit from a modularized plugin structure with access to selected runs and groups, offering you maximum flexibility.\n  \n- **Asynchronous Execution:**  \n  Utilize asynchronous execution of resource-intensive plugins and caching of results to improve performance.\n\n### Broad Optimizer Support\n- **Optimizer Support:**  \n  Work with many frameworks and optimizers using our converters, including converters for SMAC, BOHB, AMLTK, and Optuna.\n  \n- **Native Format Saving:**  \n  Save AutoML runs from various frameworks in DeepCAVE's native format using the built-in recorder.\n  \n- **Flexible Data Loading:**  \n  Alternatively, load AutoML runs from other frameworks by converting them into a Pandas DataFrame.\n\n### Developer and API Features\n- **API Mode:**  \n  Interact with the code directly through API mode, allowing you to bypass the graphical interface if preferred.\n\n\n## Citation\n\nIf you use DeepCAVE in one of your research projects, please cite our [ReALML@ICML'22 workshop paper](https://arxiv.org/abs/2206.03493):\n```\n@misc{sass-realml2022,\n    title = {DeepCAVE: An Interactive Analysis Tool for Automated Machine Learning},\n    author = {Sass, René and Bergman, Eddie and Biedenkapp, André and Hutter, Frank and Lindauer, Marius},\n    doi = {10.48550/ARXIV.2206.03493},\n    url = {https://arxiv.org/abs/2206.03493},\n    publisher = {arXiv},\n    year = {2022},\n    copyright = {arXiv.org perpetual, non-exclusive license}\n}\n```\n\nCopyright (C) 2021-2024 The DeepCAVE 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