sweeps
Weights and Biases Hyperparameter Sweeps Engine.
Decision gist · record as of 2026-08-14
Yes. The package is actively maintained, has no known vulnerabilities, carries a permissive MIT license, and offers low-friction installation. It solves a concrete problem—suggesting hyperparameters and early-stopping decisions—with established algorithms. Install it if you need programmatic hyperparameter optimization or are integrating with Weights & Biases sweeps locally.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Low friction install with a pure-Python wheel.
- Actively maintained as of August 2026 with no known vulnerabilities.
- Depends on established scientific libraries (numpy, scipy, scikit-learn, pydantic).
License · maintenance · safety
MIT license (permissive) — MIT license permits commercial and private use with minimal restrictions; you may use, modify, and distribute freely provided you include the license notice.
last release 2022-08-15 (1460 days) · last repo commit 2026-08-05 · 42 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 103,140 downloads/mo, #12,828 on PyPI
Alternatives
Verify before relying
pip install sweeps
from sweeps import next_run, SweepRun, RunState
config = {
"metric": {"name": "loss", "goal": "minimize"},
"method": "bayes",
"parameters": {"v1": {"min": 1, "max": 10}},
}
runs = []
suggestion = next_run(config, runs)- Whether the package is actively developed beyond August 2026 or if maintenance has stalled since the last release in August 2022.
- Performance characteristics and scalability limits for large numbers of runs or high-dimensional parameter spaces.
- Availability of documentation beyond the README examples provided.
What it is and what it does
Sweeps is a hyperparameter optimization engine that powers Weights & Biases' sweep functionality. It takes a sweep configuration (defining the optimization method, parameter ranges, and metric to optimize) and a list of completed runs, then suggests the next hyperparameter values to try. It supports multiple search strategies including Bayesian optimization and grid search, and can identify runs to stop early using methods like Hyperband to save compute resources.
The package validates configurations against a JSON schema, routes to the appropriate search algorithm, and returns structured suggestions. It's designed as a backend component but is also available as a standalone library for local hyperparameter tuning workflows. Dependencies include numpy, scipy, scikit-learn for numerical computation, and pydantic for configuration validation.
Use it for
- Suggest next hyperparameters in a machine learning training loop using Bayesian optimization to balance exploration and exploitation.
- Implement early stopping in parallel hyperparameter searches by identifying underperforming runs via Hyperband.
- Validate and parse sweep configurations to ensure they conform to the expected schema before running experiments.
- Integrate hyperparameter optimization into a local training pipeline without relying on external services.
- Grid search over discrete parameter combinations with deterministic parameter assignment.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
The package is actively maintained, has no known vulnerabilities, carries a permissive MIT license, and offers low-friction installation. It solves a concrete problem—suggesting hyperparameters and early-stopping decisions—with established algorithms. Install it if you need programmatic hyperparameter optimization or are integrating with Weights & Biases sweeps locally.
Install
sweeps on PyPI
Before you install
Low friction install with a pure-Python wheel. Actively maintained as of August 2026 with no known vulnerabilities. Depends on established scientific libraries (numpy, scipy, scikit-learn, pydantic).
License in practice
MIT license permits commercial and private use with minimal restrictions; you may use, modify, and distribute freely provided you include the license notice.
Quickstart
pip install sweeps
from sweeps import next_run, SweepRun, RunState
config = {
"metric": {"name": "loss", "goal": "minimize"},
"method": "bayes",
"parameters": {"v1": {"min": 1, "max": 10}},
}
runs = []
suggestion = next_run(config, runs)Verify before relying
- Whether the package is actively developed beyond August 2026 or if maintenance has stalled since the last release in August 2022.
- Performance characteristics and scalability limits for large numbers of runs or high-dimensional parameter spaces.
- Availability of documentation beyond the README examples provided.
Package facts
| License | MIT license permissive |
| Python support | Supports the current Python release >=3.6 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 7 packagesnumpyscipyPyYAMLjsonschemajsonrefpydanticscikit-learn |
| Maintenance | Actively maintained 1,460 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 103,140 / month, #12,828 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableIntended Audience :: DevelopersIntended Audience :: Science/ResearchLicense :: OSI Approved :: MIT LicenseNatural Language :: EnglishProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.6Programming Language :: Python :: 3.7Programming Language :: Python :: 3.8Programming Language :: Python :: 3.9Topic :: Scientific/Engineering :: Artificial IntelligenceTopic :: Software Development :: Libraries :: Python Modules |
Evidence: sweeps-0.2.0-py3-none-any.whl
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