Use W&B Sweeps to automate hyperparameter search and visualize rich, interactive experiment tracking. Pick from popular search methods such as Bayesian, grid search, and random to search the hyperparameter space. Scale and parallelize sweep across one or more machines.Documentation Index
Fetch the complete documentation index at: https://docs.wandb.ai/llms.txt
Use this file to discover all available pages before exploring further.
How it works
Create a sweep with two W&B CLI commands:- Initialize a sweep.
- Start the sweep agent.
The preceding code snippet, and the colab linked on this page, show how to initialize and create a sweep with the W&B CLI. See the Sweeps walkthrough to use the Python SDK to configure, initialize, and run a sweep.
How to get started
Depending on your use case, explore the following resources to get started with W&B Sweeps:- Read through the sweeps walkthrough for a step-by-step outline of the W&B Python SDK commands to use to define a sweep configuration, initialize a sweep, and start a sweep.
- Explore this chapter to learn how to:
- Explore a curated list of Sweep experiments that explore hyperparameter optimization with W&B Sweeps. Results are stored in W&B Reports.