base on The AI developer platform. Use Weights & Biases to train and fine-tune models, and manage models from experimentation to production. <p align="center"> <img src="./assets/logo.svg" width="600" alt="Weights & Biases" /> </p> <p align="center"> <a href="https://pypi.python.org/pypi/wandb"><img src="https://img.shields.io/pypi/v/wandb" /></a> <a href="https://anaconda.org/conda-forge/wandb"><img src="https://img.shields.io/conda/vn/conda-forge/wandb" /></a> <a href="https://pypi.python.org/pypi/wandb"><img src="https://img.shields.io/pypi/pyversions/wandb" /></a> <a href="https://circleci.com/gh/wandb/wandb"><img src="https://img.shields.io/circleci/build/github/wandb/wandb/main" /></a> <a href="https://codecov.io/gh/wandb/wandb"><img src="https://img.shields.io/codecov/c/gh/wandb/wandb" /></a> </p> <p align='center'> <a href="https://colab.research.google.com/github/wandb/examples/blob/master/colabs/intro/Intro_to_Weights_%26_Biases.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" /></a> </p> Use 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://wandb.com?utm_source=github&utm_medium=code&utm_campaign=wandb&utm_content=readme)! <br> Building an LLM app? Track, debug, evaluate, and monitor LLM apps with [Weave](https://wandb.github.io/weave?utm_source=github&utm_medium=code&utm_campaign=wandb&utm_content=readme), our new suite of tools for GenAI. &nbsp; # Documentation See the [W&B Developer Guide](https://docs.wandb.ai/?utm_source=github&utm_medium=code&utm_campaign=wandb&utm_content=documentation) and [API Reference Guide](https://docs.wandb.ai/training/api-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. &nbsp; # Quickstart Install W&B to track, visualize, and manage machine learning experiments of any size. ## Install the wandb library ```shell pip install wandb ``` ## Sign up and create an API key Sign up for a [W&B account](https://wandb.ai/login?utm_source=github&utm_medium=code&utm_campaign=wandb&utm_content=quickstart). Optionally, use the `wandb login` CLI to configure an API key on your machine. You can skip this step -- W&B will prompt you for an API key the first time you use it. ## Create a machine learning training experiment In your Python script or notebook, initialize a W&B run with `wandb.init()`. Specify hyperparameters and log metrics and other information to W&B. ```python import wandb # Project that the run is recorded to project = "my-awesome-project" # Dictionary with hyperparameters config = {"epochs": 1337, "lr": 3e-4} # The `with` syntax marks the run as finished upon exiting the `with` block, # and it marks the run "failed" if there's an exception. # # In a notebook, it may be more convenient to write `run = wandb.init()` # and manually call `run.finish()` instead of using a `with` block. with wandb.init(project=project, config=config) as run: # Training code here # Log values to W&B with run.log() run.log({"accuracy": 0.9, "loss": 0.1}) ``` Visit [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. &nbsp; # Integrations W&B [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. For developers adding W&B to a new framework, follow the [W&B Developer Guide](https://docs.wandb.ai/models/integrations/add-wandb-to-any-library). &nbsp; # W&B Hosting Options Weights & 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: 1. [Multi-tenant Cloud](https://docs.wandb.ai/platform/hosting/hosting-options/multi_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. 2. [Dedicated Cloud](https://docs.wandb.ai/platform/hosting/hosting-options/dedicated_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. 3. [Self-Managed](https://docs.wandb.ai/platform/hosting/hosting-options/self-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. See the [Hosting documentation](https://docs.wandb.ai/guides/hosting?utm_source=github&utm_medium=code&utm_campaign=wandb&utm_content=hosting) in the W&B Developer Guide for more information. &nbsp; # Python Version Support We 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/). When we discontinue support for a Python version, we will increment the library’s minor version number to reflect this change. &nbsp; # Contribution guidelines Weights & 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 [email protected]. &nbsp; # W&B Community Be a part of the growing W&B Community and interact with the W&B team in our [Discord](https://wandb.me/discord). Stay connected with the latest AI updates and tutorials with [W&B Fully Connected](https://wandb.ai/fully-connected). &nbsp; # License [MIT License](https://github.com/wandb/wandb/blob/main/LICENSE) ", Assign "at most 3 tags" to the expected json: {"id":"10505","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"