{"id":13585063,"url":"https://github.com/wandb/wandb","last_synced_at":"2026-02-05T02:02:32.156Z","repository":{"id":37251369,"uuid":"86031674","full_name":"wandb/wandb","owner":"wandb","description":"The AI developer platform. Use Weights \u0026 Biases to train and fine-tune models, and manage models from experimentation to production.","archived":false,"fork":false,"pushed_at":"2025-05-12T05:07:30.000Z","size":180231,"stargazers_count":9852,"open_issues_count":774,"forks_count":739,"subscribers_count":62,"default_branch":"main","last_synced_at":"2025-05-12T16:22:40.962Z","etag":null,"topics":["ai","collaboration","data-science","data-versioning","deep-learning","experiment-track","hyperparameter-optimization","hyperparameter-search","hyperparameter-tuning","jax","keras","machine-learning","ml-platform","mlops","model-versioning","pytorch","reinforcement-learning","reproducibility","tensorflow"],"latest_commit_sha":null,"homepage":"https://wandb.ai","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/wandb.png","metadata":{"files":{"readme":"README.md","changelog":"CHANGELOG.md","contributing":"CONTRIBUTING.md","funding":null,"license":"LICENSE","code_of_conduct":"CODE_OF_CONDUCT.md","threat_model":null,"audit":null,"citation":null,"codeowners":".github/CODEOWNERS","security":"SECURITY.md","support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2017-03-24T05:46:23.000Z","updated_at":"2025-05-12T14:25:47.000Z","dependencies_parsed_at":"2023-10-05T03:32:54.807Z","dependency_job_id":"5ec84b6d-7b4c-4e2d-b3f6-8bac374966dc","html_url":"https://github.com/wandb/wandb","commit_stats":{"total_commits":7115,"total_committers":203,"mean_commits":35.04926108374384,"dds":0.8312016865776528,"last_synced_commit":"fa543bcebb6ba4cfc538cd7ca65dbe8fef0179a1"},"previous_names":["wandb/client"],"tags_count":176,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/wandb%2Fwandb","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/wandb%2Fwandb/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/wandb%2Fwandb/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/wandb%2Fwandb/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/wandb","download_url":"https://codeload.github.com/wandb/wandb/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":253774660,"owners_count":21962210,"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":["ai","collaboration","data-science","data-versioning","deep-learning","experiment-track","hyperparameter-optimization","hyperparameter-search","hyperparameter-tuning","jax","keras","machine-learning","ml-platform","mlops","model-versioning","pytorch","reinforcement-learning","reproducibility","tensorflow"],"created_at":"2024-08-01T15:04:43.218Z","updated_at":"2026-01-30T01:48:35.990Z","avatar_url":"https://github.com/wandb.png","language":"Python","readme":"\u003cp align=\"center\"\u003e\n  \u003cimg src=\"./assets/logo.svg\" width=\"600\" alt=\"Weights \u0026 Biases\" /\u003e\n\u003c/p\u003e\n\n\u003cp align=\"center\"\u003e\n\u003ca href=\"https://pypi.python.org/pypi/wandb\"\u003e\u003cimg src=\"https://img.shields.io/pypi/v/wandb\" /\u003e\u003c/a\u003e\n\u003ca href=\"https://anaconda.org/conda-forge/wandb\"\u003e\u003cimg src=\"https://img.shields.io/conda/vn/conda-forge/wandb\" /\u003e\u003c/a\u003e\n\u003ca href=\"https://pypi.python.org/pypi/wandb\"\u003e\u003cimg src=\"https://img.shields.io/pypi/pyversions/wandb\" /\u003e\u003c/a\u003e\n\u003ca href=\"https://circleci.com/gh/wandb/wandb\"\u003e\u003cimg src=\"https://img.shields.io/circleci/build/github/wandb/wandb/main\" /\u003e\u003c/a\u003e\n\u003ca href=\"https://codecov.io/gh/wandb/wandb\"\u003e\u003cimg src=\"https://img.shields.io/codecov/c/gh/wandb/wandb\" /\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003cp align='center'\u003e\n\u003ca href=\"https://colab.research.google.com/github/wandb/examples/blob/master/colabs/intro/Intro_to_Weights_%26_Biases.ipynb\"\u003e\u003cimg src=\"https://colab.research.google.com/assets/colab-badge.svg\" /\u003e\u003c/a\u003e\n\u003c/p\u003e\n\nUse W\u0026B to build better models faster. Track and visualize all the pieces of your machine learning pipeline, from datasets to production machine learning models. Get started with W\u0026B today, [sign up for a W\u0026B account](https://wandb.com?utm_source=github\u0026utm_medium=code\u0026utm_campaign=wandb\u0026utm_content=readme)!\n\n\u003cbr\u003e\n\nBuilding an LLM app? Track, debug, evaluate, and monitor LLM apps with [Weave](https://wandb.github.io/weave?utm_source=github\u0026utm_medium=code\u0026utm_campaign=wandb\u0026utm_content=readme), our new suite of tools for GenAI.\n\n\u0026nbsp;\n\n# Documentation\n\nSee the [W\u0026B Developer Guide](https://docs.wandb.ai/?utm_source=github\u0026utm_medium=code\u0026utm_campaign=wandb\u0026utm_content=documentation) and [API Reference Guide](https://docs.wandb.ai/training/api-reference#api-overview?utm_source=github\u0026utm_medium=code\u0026utm_campaign=wandb\u0026utm_content=documentation) for a full technical description of the W\u0026B platform.\n\n\u0026nbsp;\n\n# Quickstart\n\nInstall W\u0026B to track, visualize, and manage machine learning experiments of any size.\n\n## Install the wandb library\n\n```shell\npip install wandb\n```\n\n## Sign up and create an API key\n\nSign up for a [W\u0026B account](https://wandb.ai/login?utm_source=github\u0026utm_medium=code\u0026utm_campaign=wandb\u0026utm_content=quickstart). Create a new API key at [wandb.ai/settings](https://wandb.ai/settings) and store it securely. Optionally, use the `wandb login` CLI to configure your API key on your machine. You can skip this step -- W\u0026B will prompt you to create an API key the first time you use it.\n\n**Note:** API keys can only be viewed once when created. Store your API key in a secure location like a password manager or environment variable.\n\n## Create a machine learning training experiment\n\nIn your Python script or notebook, initialize a W\u0026B run with `wandb.init()`.\nSpecify hyperparameters and log metrics and other information to W\u0026B.\n\n```python\nimport wandb\n\n# Project that the run is recorded to\nproject = \"my-awesome-project\"\n\n# Dictionary with hyperparameters\nconfig = {\"epochs\": 1337, \"lr\": 3e-4}\n\n# The `with` syntax marks the run as finished upon exiting the `with` block,\n# and it marks the run \"failed\" if there's an exception.\n#\n# In a notebook, it may be more convenient to write `run = wandb.init()`\n# and manually call `run.finish()` instead of using a `with` block.\nwith wandb.init(project=project, config=config) as run:\n    # Training code here\n\n    # Log values to W\u0026B with run.log()\n    run.log({\"accuracy\": 0.9, \"loss\": 0.1})\n```\n\nVisit [wandb.ai/home](https://wandb.ai/home) to view recorded metrics such as accuracy and loss and how they changed during each training step. Each run object appears in the Runs column with generated names.\n\n\u0026nbsp;\n\n# Integrations\n\nW\u0026B [integrates](https://docs.wandb.ai/models/integrations) with popular ML frameworks and libraries making it fast and easy to set up experiment tracking and data versioning inside existing projects.\n\nFor developers adding W\u0026B to a new framework, follow the [W\u0026B Developer Guide](https://docs.wandb.ai/models/integrations/add-wandb-to-any-library).\n\n\u0026nbsp;\n\n# W\u0026B Hosting Options\n\nWeights \u0026 Biases is available in the cloud or installed on your private infrastructure. Set up a W\u0026B Server in a production environment in one of three ways:\n\n1. [Multi-tenant Cloud](https://docs.wandb.ai/platform/hosting/hosting-options/multi_tenant_cloud?utm_source=github\u0026utm_medium=code\u0026utm_campaign=wandb\u0026utm_content=hosting): Fully managed platform deployed in W\u0026B’s Google Cloud Platform (GCP) account in GCP’s North America regions.\n2. [Dedicated Cloud](https://docs.wandb.ai/platform/hosting/hosting-options/dedicated_cloud?utm_source=github\u0026utm_medium=code\u0026utm_campaign=wandb\u0026utm_content=hosting): Single-tenant, fully managed platform deployed in W\u0026B’s AWS, GCP, or Azure cloud accounts. Each Dedicated Cloud instance has its own isolated network, compute and storage from other W\u0026B Dedicated Cloud instances.\n3. [Self-Managed](https://docs.wandb.ai/platform/hosting/hosting-options/self-managed?utm_source=github\u0026utm_medium=code\u0026utm_campaign=wandb\u0026utm_content=hosting): Deploy W\u0026B Server on your AWS, GCP, or Azure cloud account or within your on-premises infrastructure.\n\nSee the [Hosting documentation](https://docs.wandb.ai/guides/hosting?utm_source=github\u0026utm_medium=code\u0026utm_campaign=wandb\u0026utm_content=hosting) in the W\u0026B Developer Guide for more information.\n\n\u0026nbsp;\n\n# Python Version Support\n\nWe are committed to supporting our minimum required Python version for _at least_ six months after its official end-of-life (EOL) date, as defined by the Python Software Foundation. You can find a list of Python EOL dates [here](https://devguide.python.org/versions/).\n\nWhen we discontinue support for a Python version, we will increment the library’s minor version number to reflect this change.\n\n\u0026nbsp;\n\n# Contribution guidelines\n\nWeights \u0026 Biases ❤️ open source, and we welcome contributions from the community! See the [Contribution guide](https://github.com/wandb/wandb/blob/main/CONTRIBUTING.md) for more information on the development workflow and the internals of the wandb library. For wandb bugs and feature requests, visit [GitHub Issues](https://github.com/wandb/wandb/issues) or contact support@wandb.com.\n\n\u0026nbsp;\n\n# W\u0026B Community\n\nBe a part of the growing W\u0026B Community and interact with the W\u0026B team in our [Discord](https://wandb.me/discord). Stay connected with the latest AI updates and tutorials with [W\u0026B Fully Connected](https://wandb.ai/fully-connected).\n\n\u0026nbsp;\n\n# License\n\n[MIT License](https://github.com/wandb/wandb/blob/main/LICENSE)\n","funding_links":[],"categories":["Python","Machine Learning Framework","Visualization \u0026 Interpretability","工作流程和实验跟踪","Frameworks","NLP","Curated List","Application Recommendation","Monitoring and Observability","Repos","\u003ca id=\"tools\"\u003e\u003c/a\u003e🛠️ Tools","Model, Data and Experiment Management","ML Platforms","Large Scale Deployment","The Data Science Toolbox","Runtime","🚀 MLOps","Training \u0026 Fine-Tuning"],"sub_categories":["Experiment Management","3. 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