optuna-dashboard
Real-time dashboard for Optuna
Decision gist · record as of 2026-08-14
Yes, if you use Optuna for hyperparameter tuning and want a user-friendly way to inspect results. Installation is straightforward, maintenance is active, and there are no known security issues. The Alpha status reflects ongoing development but not instability—the package is widely used and suitable for production study visualization.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires an existing Optuna study persisted to a database backend (SQLite, MySQL, or PostgreSQL); the dashboard is a viewer, not a study runner.
- Low friction installation with a pure Python wheel.
- Actively maintained with recent commits and no known vulnerabilities.
License · maintenance · safety
MIT License (permissive) — MIT License permits unrestricted use, modification, and distribution with minimal restrictions, making it suitable for both open-source and commercial projects.
last release 2025-11-10 (277 days) · last repo commit 2026-08-14 · 796 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 231,639 downloads/mo, #9,082 on PyPI
Alternatives
Verify before relying
pip install optuna-dashboard
# After running an Optuna study with RDB storage:
optuna-dashboard sqlite:///db.sqlite3
# Then open http://localhost:8080/ in your browser- Whether the dashboard supports real-time updates as new trials complete, or requires manual refresh
- Performance characteristics when visualizing studies with thousands of trials
- Whether custom metrics or user-defined visualizations can be added beyond the built-in graphs
What it is and what it does
optuna-dashboard is a web-based visualization tool for Optuna hyperparameter optimization studies. It connects to an existing Optuna study stored in a database backend (SQLite, MySQL, or PostgreSQL) and presents the optimization history, trial results, and parameter importances through an interactive browser interface. The package runs as a lightweight web server that listens on localhost:8080 by default.
The dashboard is designed as a companion to Optuna itself—you run your optimization study separately using Optuna's API with a persistent RDB backend, then launch the dashboard to inspect results. It depends on bottle for the web framework, optuna for study access, and tomli for configuration parsing. The package is in Alpha status but actively maintained, with support for Python 3.8 through 3.13.
Use it for
- Monitor hyperparameter optimization progress in real-time while an Optuna study is running on a remote machine
- Inspect trial history, best parameters, and convergence plots after completing a hyperparameter search
- Share optimization results with team members via a web interface without requiring Python or Optuna knowledge
- Analyze parameter importance and trial distributions to understand which hyperparameters most affect model performance
- Embed optimization dashboards in Jupyter notebooks or VS Code for integrated development workflows
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you use Optuna for hyperparameter tuning and want a user-friendly way to inspect results.
Installation is straightforward, maintenance is active, and there are no known security issues. The Alpha status reflects ongoing development but not instability—the package is widely used and suitable for production study visualization.
Install
optuna-dashboard on PyPI
Before you install
Low friction installation with a pure Python wheel. Actively maintained with recent commits and no known vulnerabilities. Supports current Python versions from 3.8 onwards.
Requires an existing Optuna study persisted to a database backend (SQLite, MySQL, or PostgreSQL); the dashboard is a viewer, not a study runner.
License in practice
MIT License permits unrestricted use, modification, and distribution with minimal restrictions, making it suitable for both open-source and commercial projects.
Quickstart
pip install optuna-dashboard
# After running an Optuna study with RDB storage:
optuna-dashboard sqlite:///db.sqlite3
# Then open http://localhost:8080/ in your browserVerify before relying
- Whether the dashboard supports real-time updates as new trials complete, or requires manual refresh
- Performance characteristics when visualizing studies with thousands of trials
- Whether custom metrics or user-defined visualizations can be added beyond the built-in graphs
Package facts
| License | MIT License permissive |
| Python support | Supports the current Python release >=3.8 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 3 packagesbottleoptunatomli |
| Maintenance | Actively maintained 277 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 231,639 / month, #9,082 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 3 - AlphaIntended Audience :: DevelopersIntended Audience :: Science/ResearchLicense :: OSI Approved :: MIT LicenseProgramming Language :: PythonProgramming Language :: Python :: 3Programming Language :: Python :: 3 :: OnlyProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.8Programming Language :: Python :: 3.9 |
Evidence: optuna_dashboard-0.20.0-py3-none-any.whl
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See also optuna · hydra-optuna-sweeper · optuna-integration · clawmetry · pyannote-pipeline · esphome-device-builder · esphome-dashboard · postgres-mcp · sweeps · rq-dashboard