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!

Building an LLM app? Track, debug, evaluate, and monitor LLM apps with Weave, our new suite of tools for GenAI.

 

Documentation

See the W&B Developer Guide and API Reference Guide for a full technical description of the W&B platform.

 

Quickstart

Install W&B to track, visualize, and manage machine learning experiments of any size.

Install the wandb library

pip install wandb

Sign up and create an API key

Sign up for a W&B account. Create a new API key at 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&B will prompt you to create an API key the first time you use it.

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.

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.

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 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.

 

Integrations

W&B integrates 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.

 

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: Fully managed platform deployed in W&B’s Google Cloud Platform (GCP) account in GCP’s North America regions.
2. Dedicated Cloud: 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: Deploy W&B Server on your AWS, GCP, or Azure cloud account or within your on-premises infrastructure.

See the Hosting documentation in the W&B Developer Guide for more information.

 

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.

When we discontinue support for a Python version, we will increment the library’s minor version number to reflect this change.

 

Contribution guidelines

Weights & Biases ❤️ open source, and we welcome contributions from the community! See the Contribution Guide and the docs/ directory for more information on the development workflow and the internals of the wandb library. For wandb bugs and feature requests, visit GitHub Issues or contact support@wandb.com.

 

W&B Community

Be a part of the growing W&B Community and interact with the W&B team in our Discord. Stay connected with the latest AI updates and tutorials with W&B Fully Connected.

 

License

MIT License