Learn more about sweeps

Academic papers

Li, Lisha, et al. “Hyperband: A novel bandit-based approach to hyperparameter optimization.” The Journal of Machine Learning Research 18.1 (2017): 6765-6816.

Sweep Experiments

The following W&B Reports demonstrate examples of projects that explore hyperparameter optimization with W&B Sweeps.

• Drought Watch Benchmark Progress
  • Description: Developing the baseline and exploring submissions to the Drought Watch benchmark.
• Tuning Safety Penalties in Reinforcement Learning
  • Description: We examine agents trained with different side effect penalties on three different tasks: pattern creation, pattern removal, and navigation.
• Meaning and Noise in Hyperparameter Search with W&B Stacey Svetlichnaya
  • Description: How do we distinguish signal from pareidolia (imaginary patterns)? This article is showcases what is possible with W&B and aims to inspire further exploration.
• Who is Them? Text Disambiguation with Transformers
  • Description: Using Hugging Face to explore models for natural language understanding
• DeepChem: Molecular Solubility
  • Description: Predict chemical properties from molecular structure with random forests and deep nets.
• Intro to MLOps: Hyperparameter Tuning
  • Description: Explore why hyperparameter optimization matters and look at three algorithms to automate hyperparameter tuning for your machine learning models.

selfm-anaged

The following how-to-guide demonstrates how to solve real-world problems with W&B:

• Sweeps with XGBoost
  • Description: How to use W&B Sweeps for hyperparameter tuning using XGBoost.

Sweep GitHub repository

W&B advocates open source and welcome contributions from the community. Find the GitHub repository at https://github.com/wandb/sweeps. For information on how to contribute to the W&B open source repo, see the W&B GitHub Contribution guidelines.