{"id":13640070,"url":"https://github.com/studioml/studio","last_synced_at":"2025-05-16T07:05:33.115Z","repository":{"id":47715698,"uuid":"91284550","full_name":"studioml/studio","owner":"studioml","description":"Studio: Simplify and expedite model building process","archived":false,"fork":false,"pushed_at":"2024-07-06T00:47:45.000Z","size":2627,"stargazers_count":381,"open_issues_count":58,"forks_count":53,"subscribers_count":22,"default_branch":"master","last_synced_at":"2025-05-10T21:47:53.483Z","etag":null,"topics":["hacktoberfest"],"latest_commit_sha":null,"homepage":"https://studio.ml","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/studioml.png","metadata":{"files":{"readme":"docs/README.rst","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":"2017-05-15T01:49:28.000Z","updated_at":"2025-05-03T09:53:02.000Z","dependencies_parsed_at":"2023-02-08T14:01:50.082Z","dependency_job_id":"7ecb2d4e-7f36-4191-bfb0-2231bfcd548c","html_url":"https://github.com/studioml/studio","commit_stats":{"total_commits":1973,"total_committers":25,"mean_commits":78.92,"dds":0.3786112519006589,"last_synced_commit":"e8aedf9c15baa872eb7aee4d6b28ad6208a9fca2"},"previous_names":[],"tags_count":55,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/studioml%2Fstudio","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/studioml%2Fstudio/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/studioml%2Fstudio/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/studioml%2Fstudio/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/studioml","download_url":"https://codeload.github.com/studioml/studio/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":254485060,"owners_count":22078767,"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":["hacktoberfest"],"created_at":"2024-08-02T01:01:07.473Z","updated_at":"2025-05-16T07:05:28.019Z","avatar_url":"https://github.com/studioml.png","language":"Python","funding_links":[],"categories":["Model and Data Versioning","Model, Data and Experiment Tracking","工作流程和实验跟踪","Python"],"sub_categories":[],"readme":".. raw:: html\n   \n   \u003cp align=\"center\"\u003e\n      \u003cimg src=\"logo.png\"\u003e\n   \u003c/p\u003e\n\n|Hex.pm| |Build.pm|\n\nStudio is a model management framework written in Python to help simplify and expedite your model building experience. It was developed to minimize the overhead involved with scheduling, running, monitoring and managing artifacts of your machine learning experiments. No one wants to spend their time configuring different machines, setting up dependencies, or playing archeologist to track down previous model artifacts.\n\nMost of the features are compatible with any Python machine learning\nframework (`Keras \u003chttps://github.com/fchollet/keras\u003e`__,\n`TensorFlow \u003chttps://github.com/tensorflow/tensorflow\u003e`__,\n`PyTorch \u003chttps://github.com/pytorch/pytorch\u003e`__,\n`scikit-learn \u003chttps://github.com/scikit-learn/scikit-learn\u003e`__, etc);\nsome extra features are available for Keras and TensorFlow.\n\n**Use Studio to:** \n\n* Capture experiment information- Python environment, files, dependencies and logs- without modifying the experiment code. \n* Monitor and organize experiments using a web dashboard that integrates with TensorBoard. \n* Run experiments locally, remotely, or in the cloud (Google Cloud or Amazon EC2) \n* Manage artifacts\n* Perform hyperparameter search\n* Create customizable Python environments for remote workers.\n\nNOTE: ``studio`` package is compatible with Python 2 and 3!\n\nExample usage\n-------------\n\nStart visualizer:\n\n::\n\n    studio ui\n\nRun your jobs:\n\n::\n\n    studio run train_mnist_keras.py\n\nYou can see results of your job at http://localhost:5000. Run\n``studio {ui|run} --help`` for a full list of ui / runner options.\nWARNING: because studio tries to create a reproducible environment \nfor your experiment, if you run it in a large folder, it will take\na while to archive and upload the folder. \n\nInstallation\n------------\n\npip install studioml from the master pypi repositry:\n\n::\n\n    pip install studioml\n\nFind more `details \u003cinstallation.rst\u003e`__ on installation methods and the release process. \n\nAuthentication\n--------------\n\nCurrently Studio supports 2 methods of authentication: `email / password \u003cauthentication.rst#email--password-authentication\u003e`__ and using a `Google account. \u003cauthentication.rst#google-account-authentication\u003e`__ To use studio runner and studio ui in guest\nmode, in studio/default\\_config.yaml, uncomment \"guest: true\" under the\ndatabase section.\n\nAlternatively, you can set up your own database and configure Studio to\nuse it. See `setting up database \u003csetup_database.rst\u003e`__. This is a\npreferred option if you want to keep your models and artifacts private.\n\n\nFurther reading and cool features\n---------------------------------\n\n-  `Running experiments remotely \u003chttp://docs.studio.ml/en/latest/remote_worker.html\u003e`__\n   \n   -  `Custom Python environments for remote workers \u003chttp://docs.studio.ml/en/latest/customenv.html\u003e`__\n\n-  `Running experiments in the cloud \u003chttp://docs.studio.ml/en/latest/cloud.html\u003e`__\n\n   -  `Google Cloud setup instructions \u003chttp://docs.studio.ml/en/latest/glcloud_setup.html\u003e`__\n\n   -  `Amazon EC2 setup instructions \u003chttp://docs.studio.ml/en/latest/ec2_setup.html\u003e`__\n\n-  `Artifact management \u003chttp://docs.studio.ml/en/latest/artifacts.html\u003e`__\n-  `Hyperparameter search \u003chttp://docs.studio.ml/en/latest/hyperparams.html\u003e`__\n-  `Pipelines for trained models \u003chttp://docs.studio.ml/en/latest/model_pipelines.html\u003e`__\n-  `Containerized experiments \u003chttp://docs.studio.ml/en/latest/containers.html\u003e`__\n\n.. |Hex.pm| image:: https://img.shields.io/hexpm/l/plug.svg\n   :target: https://github.com/studioml/studio/blob/master/LICENSE\n\n.. |Build.pm| image:: https://travis-ci.org/studioml/studio.svg?branch=master\n   :target: https://travis-ci.org/studioml/studio.svg?branch=master\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fstudioml%2Fstudio","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fstudioml%2Fstudio","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fstudioml%2Fstudio/lists"}