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!
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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. 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.
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:
- Multi-tenant Cloud: Fully managed platform deployed in W&B’s Google Cloud Platform (GCP) account in GCP’s North America regions.
- 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.
- 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 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 ML updates and tutorials with W&B Fully Connected.
License
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