{"data":{"slug":"google-model-search","name":"model_search","tagline":"model_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":3241,"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":89,"ai_summary":null,"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":"2026-07-11T23:36:05.713858+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":"python","name":"python"}],"trust":{"provenance":{"is_fork":false,"github_id":331071860,"owner_type":"Organization","methodology":"github_public_v1","parent_repo":null,"near_duplicate_slugs":[]},"computed_at":"2026-07-11T23:35:59.937Z","maintenance":{"label":"Archived","score":8,"methodology":"github_public_v1","releases_90d":0,"days_since_push":711,"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":"2026-07-11T23:35:59.691Z"},"languages":{"value":["python"],"source":"github.language","observed_at":"2026-07-11T23:35:59.691Z"},"license_spdx":{"value":"Apache-2.0","source":"github.license","observed_at":"2026-07-11T23:35:59.691Z"}}}}