[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-9656":3},{"id":4,"name":5,"fullName":6,"owner":5,"repo":5,"description":7,"homepage":8,"htmlUrl":9,"language":10,"languages":9,"totalLinesOfCode":9,"stars":11,"forks":12,"watchers":13,"openIssues":14,"contributorsCount":15,"subscribersCount":15,"size":15,"stars1d":15,"stars7d":16,"stars30d":17,"stars90d":15,"forks30d":15,"starsTrendScore":18,"compositeScore":19,"rankGlobal":9,"rankLanguage":9,"license":20,"archived":21,"fork":21,"defaultBranch":22,"hasWiki":23,"hasPages":23,"topics":24,"createdAt":9,"pushedAt":9,"updatedAt":44,"readmeContent":45,"aiSummary":46,"trendingCount":15,"starSnapshotCount":15,"syncStatus":47,"lastSyncTime":48,"discoverSource":49},9656,"wandb","wandb\u002Fwandb","The AI developer platform. Use Weights & Biases to train and fine-tune models, and manage models from experimentation to production.","https:\u002F\u002Fwandb.ai",null,"Python",11116,881,62,712,0,12,56,5,43.84,"MIT License",false,"main",true,[25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43],"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","2026-06-12 02:02:10","\u003Cp align=\"center\">\n  \u003Cimg src=\".\u002Fassets\u002Flogo.svg\" width=\"600\" alt=\"Weights & Biases\" \u002F>\n\u003C\u002Fp>\n\n\u003Cp align=\"center\">\n\u003Ca href=\"https:\u002F\u002Fpypi.python.org\u002Fpypi\u002Fwandb\">\u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fpypi\u002Fv\u002Fwandb\" \u002F>\u003C\u002Fa>\n\u003Ca href=\"https:\u002F\u002Fanaconda.org\u002Fconda-forge\u002Fwandb\">\u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fconda\u002Fvn\u002Fconda-forge\u002Fwandb\" \u002F>\u003C\u002Fa>\n\u003Ca href=\"https:\u002F\u002Fpypi.python.org\u002Fpypi\u002Fwandb\">\u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fpypi\u002Fpyversions\u002Fwandb\" \u002F>\u003C\u002Fa>\n\u003Ca href=\"https:\u002F\u002Fcircleci.com\u002Fgh\u002Fwandb\u002Fwandb\">\u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fcircleci\u002Fbuild\u002Fgithub\u002Fwandb\u002Fwandb\u002Fmain\" \u002F>\u003C\u002Fa>\n\u003Ca href=\"https:\u002F\u002Fcodecov.io\u002Fgh\u002Fwandb\u002Fwandb\">\u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fcodecov\u002Fc\u002Fgh\u002Fwandb\u002Fwandb\" \u002F>\u003C\u002Fa>\n\u003C\u002Fp>\n\u003Cp align='center'>\n\u003Ca href=\"https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Fwandb\u002Fexamples\u002Fblob\u002Fmaster\u002Fcolabs\u002Fintro\u002FIntro_to_Weights_%26_Biases.ipynb\">\u003Cimg src=\"https:\u002F\u002Fcolab.research.google.com\u002Fassets\u002Fcolab-badge.svg\" \u002F>\u003C\u002Fa>\n\u003C\u002Fp>\n\nUse W&B 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&B today, [sign up for a W&B account](https:\u002F\u002Fwandb.com?utm_source=github&utm_medium=code&utm_campaign=wandb&utm_content=readme)!\n\n\u003Cbr>\n\nBuilding an LLM app? Track, debug, evaluate, and monitor LLM apps with [Weave](https:\u002F\u002Fwandb.github.io\u002Fweave?utm_source=github&utm_medium=code&utm_campaign=wandb&utm_content=readme), our new suite of tools for GenAI.\n\n \n\n# Documentation\n\nSee the [W&B Developer Guide](https:\u002F\u002Fdocs.wandb.ai?utm_source=github&utm_medium=code&utm_campaign=wandb&utm_content=documentation) and [API Reference Guide](https:\u002F\u002Fdocs.wandb.ai\u002Ftraining\u002Fapi-reference#api-overview?utm_source=github&utm_medium=code&utm_campaign=wandb&utm_content=documentation) for a full technical description of the W&B platform.\n\n \n\n# Quickstart\n\nInstall W&B 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&B account](https:\u002F\u002Fwandb.ai\u002Flogin?utm_source=github&utm_medium=code&utm_campaign=wandb&utm_content=quickstart). Create a new API key at [wandb.ai\u002Fsettings](https:\u002F\u002Fwandb.ai\u002Fsettings) and store it securely. Optionally, use the `wandb login` CLI to configure your API key on your machine. You can skip this step -- W&B 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&B run with `wandb.init()`.\nSpecify hyperparameters and log metrics and other information to W&B.\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&B with run.log()\n    run.log({\"accuracy\": 0.9, \"loss\": 0.1})\n```\n\nVisit [wandb.ai\u002Fhome](https:\u002F\u002Fwandb.ai\u002Fhome) 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 \n\n# Integrations\n\nW&B [integrates](https:\u002F\u002Fdocs.wandb.ai\u002Fmodels\u002Fintegrations) 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&B to a new framework, follow the [W&B Developer Guide](https:\u002F\u002Fdocs.wandb.ai\u002Fmodels\u002Fintegrations\u002Fadd-wandb-to-any-library).\n\n \n\n# W&B Hosting Options\n\nWeights & Biases is available in the cloud or installed on your private infrastructure. Set up a W&B Server in a production environment in one of three ways:\n\n1. [Multi-tenant Cloud](https:\u002F\u002Fdocs.wandb.ai\u002Fplatform\u002Fhosting\u002Fhosting-options\u002Fmulti_tenant_cloud?utm_source=github&utm_medium=code&utm_campaign=wandb&utm_content=hosting): Fully managed platform deployed in W&B’s Google Cloud Platform (GCP) account in GCP’s North America regions.\n2. [Dedicated Cloud](https:\u002F\u002Fdocs.wandb.ai\u002Fplatform\u002Fhosting\u002Fhosting-options\u002Fdedicated_cloud?utm_source=github&utm_medium=code&utm_campaign=wandb&utm_content=hosting): Single-tenant, fully managed platform deployed in W&B’s AWS, GCP, or Azure cloud accounts. Each Dedicated Cloud instance has its own isolated network, compute and storage from other W&B Dedicated Cloud instances.\n3. [Self-Managed](https:\u002F\u002Fdocs.wandb.ai\u002Fplatform\u002Fhosting\u002Fhosting-options\u002Fself-managed?utm_source=github&utm_medium=code&utm_campaign=wandb&utm_content=hosting): Deploy W&B Server on your AWS, GCP, or Azure cloud account or within your on-premises infrastructure.\n\nSee the [Hosting documentation](https:\u002F\u002Fdocs.wandb.ai\u002Fplatform\u002Fhosting?utm_source=github&utm_medium=code&utm_campaign=wandb&utm_content=hosting) in the W&B Developer Guide for more information.\n\n \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:\u002F\u002Fdevguide.python.org\u002Fversions\u002F).\n\nWhen we discontinue support for a Python version, we will increment the library’s minor version number to reflect this change.\n\n \n\n# Contribution guidelines\n\nWeights & Biases ❤️ open source, and we welcome contributions from the community! See the [Contribution guide](https:\u002F\u002Fgithub.com\u002Fwandb\u002Fwandb\u002Fblob\u002Fmain\u002FCONTRIBUTING.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:\u002F\u002Fgithub.com\u002Fwandb\u002Fwandb\u002Fissues) or contact support@wandb.com.\n\n \n\n# W&B Community\n\nBe a part of the growing W&B Community and interact with the W&B team in our [Discord](https:\u002F\u002Fwandb.me\u002Fdiscord). Stay connected with the latest AI updates and tutorials with [W&B Fully Connected](https:\u002F\u002Fwandb.ai\u002Ffully-connected).\n\n \n\n# License\n\n[MIT License](https:\u002F\u002Fgithub.com\u002Fwandb\u002Fwandb\u002Fblob\u002Fmain\u002FLICENSE)\n","Weights & Biases (W&B) 是一个面向AI开发者的平台，用于训练、微调模型，并从实验到生产全程管理模型。其核心功能包括实验跟踪、超参数优化、模型版本控制等，支持多种深度学习框架如PyTorch、TensorFlow和Keras。W&B特别适用于需要高效协作与可复现性的机器学习项目中，无论是小型研究还是大规模生产环境都能受益于其强大的可视化和管理工具。通过Python库实现集成，开发者可以轻松记录实验细节、监控模型性能并分享成果。",2,"2026-06-11 03:24:00","top_topic"]