{"data":{"slug":"google-model-search","name":"model_search","tagline":"Automated machine learning for model architecture search.","github_url":"https://github.com/google/model_search","owner":"google","repo":"model_search","owner_avatar_url":"https://avatars.githubusercontent.com/u/1342004?v=4","primary_language":"Python","stars":3239,"forks":549,"topics":[],"archived":true,"github_pushed_at":"2024-07-30T21:36:15+00:00","maintenance_label":"Archived","url":"https://www.graphcanon.com/tools/google-model-search","markdown_url":"https://www.graphcanon.com/tools/google-model-search.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/google-model-search","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=google-model-search","description":null,"homepage_url":null,"license":"Apache-2.0","open_issues":53,"watchers":90,"ai_summary":"This is an automated machine learning library designed to find the best model architecture given specific data constraints and optimization goals, focusing on simplifying model training processes through predefined configurations and evaluation metrics.","readme_excerpt":"## Getting Started\nLet us start with the simplest case. You have a csv file where the features are numbers\nand you would like to run let AutoML find the best model architecture for you.\n\nBelow is a code snippet for doing so:\n\n```python\nimport model_search\nfrom model_search import constants\nfrom model_search import single_trainer\nfrom model_search.data import csv_data\n\ntrainer = single_trainer.SingleTrainer(\n    data=csv_data.Provider(\n        label_index=0,\n        logits_dimension=2,\n        record_defaults=[0, 0, 0, 0],\n        filename=\"model_search/data/testdata/csv_random_data.csv\"),\n    spec=constants.DEFAULT_DNN)\n\ntrainer.try_models(\n    number_models=200,\n    train_steps=1000,\n    eval_steps=100,\n    root_dir=\"/tmp/run_example\",\n    batch_size=32,\n    experiment_name=\"example\",\n    experiment_owner=\"model_search_user\")\n```\n\nThe above code will try 200 different models - all binary classification models,\nas the `logits_dimension` is 2. The root directory will have a subdirectory of all\nmodels, all of which will be already evaluated.\nYou can open the directory with tensorboard and see all the models with the\nevaluation metrics.\n\nThe search will be performed according to the default specification. That can be found in:\n`model_search/configs/dnn_config.pbtxt`.\n\nFor more details about the fields and if you want to create your own specification, you\ncan look at: `model_search/proto/phoenix_spec.proto`.","github_created_at":"2021-01-19T18:26:34+00:00","created_at":"2026-07-11T23:35:52.167332+00:00","updated_at":"2026-08-04T06:01:25.970938+00:00","categories":[{"slug":"evaluation-observability","name":"Evaluation & Observability","url":"https://www.graphcanon.com/categories/evaluation-observability","markdown_url":"https://www.graphcanon.com/categories/evaluation-observability.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/evaluation-observability"},{"slug":"model-training","name":"Model Training","url":"https://www.graphcanon.com/categories/model-training","markdown_url":"https://www.graphcanon.com/categories/model-training.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/model-training"}],"tags":[{"slug":"automl","name":"automl"},{"slug":"binary-classification","name":"binary classification"},{"slug":"data-driven-architecture-selection","name":"data-driven architecture selection"},{"slug":"machine-learning","name":"machine-learning"},{"slug":"model-search","name":"model search"}],"trust":{"provenance":{"is_fork":false,"github_id":331071860,"owner_type":"Organization","methodology":"github_public_v1","parent_repo":null,"near_duplicate_slugs":[]},"computed_at":"2026-08-04T06:01:25.125Z","maintenance":{"label":"Archived","score":8,"methodology":"github_public_v1","releases_90d":0,"days_since_push":734,"last_release_at":null},"security_summary":{"status":"findings","scanner":"osv@v1","low_count":268,"high_count":0,"last_scan_at":"2026-07-11T23:36:00.500Z","medium_count":0,"scan_profile":"deps","critical_count":0}},"capability_facts":{"scan":{"source":"repo_scan","observed_at":"2026-08-04T06:01:25.628Z"},"languages":{"value":["python"],"source":"github.language","observed_at":"2026-08-04T06:01:25.628Z"},"license_spdx":{"value":"Apache-2.0","source":"github.license","observed_at":"2026-08-04T06:01:25.628Z"}},"decision_facts":{"hosting":null,"pricing":null,"requirements":null,"constraints":null,"when_to_use":["When you want to streamline the selection of optimal model architectures for your specific data without manual tuning.","If you prefer an automated approach that supports binary classification problems and leverages default specifications."],"when_not_to_use":["Avoid if your project requires customization beyond what model_search offers through predefined configurations.","Not ideal for tasks outside of binary classification which strictly uses a logits_dimension of 2."],"source":"enrich:decision_facts","observed_at":"2026-07-17T06:15:30.981Z"},"constraint_facets":null,"decision_summary":[{"label":"Adopt for","value":"model_search simplifies model architecture search by automating the process with predefined configurations focusing on binary classification tasks."}]}}