Freedom251 › ML Experiment Tracking Software
Sacred

What Sacred is
Sacred is a free, open-source Python tool for configuring, organizing, logging, and reproducing computational experiments. Configuration scopes turn local variables in decorated functions into experiment parameters, which captured functions can receive by name. Each experiment has a command-line interface for changing parameters and running variants. Observers record dependencies, configuration, machine details, and results, with MongoDB available for saving run data. Automatic seeding helps control randomness, and artifact tracking is supported. Sacred is available for Linux and self-hosted use, with API and SDK access. Installation is described through pip or directly from the Git repository. NumPy and PyMongo are optional dependencies for additional features. The project documents a Neptune observer integration and a TensorFlow integration section. Named external frontends include Omniboard, Incense, Sacredboard, and SacredBrowser. Sacred uses the MIT license and offers a free plan, but no free trial. It is designed for Python experiments rather than non-Python work.
Who it suits
Sacred suits Python researchers and developers who need configurable, logged, reproducible experiments. It is useful for self-hosted workflows that can use observers, MongoDB, or external frontends.
What it does well
- Free MIT-licensed open-source project
- Command-line parameter changes and variants
- Observers record configuration and machine details
- Automatic seeding supports reproducibility
- Artifact tracking and API access
What to know first
- Designed for Python experiments
- No free trial is offered
- NumPy and PyMongo features are optional dependencies
Verdict
Sacred provides experiment configuration, run logging, reproducibility controls, and several viewing integrations. Its Python focus and self-hosted setup should guide tool selection.
Facts from Sacred's own pages
- Command line
- Its command-line interface can change parameters to run experiments with modified settings. · sacred.readthedocs.io, 10 Oct 2026
- Config access
- Configuration parameters can be injected by name into functions. · github.com, 4 Oct 2026
- Configuration
- Config scopes turn local variables in decorated functions into experiment configuration entries. · sacred.readthedocs.io, 10 Oct 2026
- Experiment parameters
- Config scopes let local variables in a function define an experiment’s parameters. · github.com, 4 Oct 2026
- External dashboards
- The README lists external frontends including Omniboard, Incense, and Sacredboard for viewing Sacred experiment data. · github.com, 10 Oct 2026
- Installation
- The README says Sacred can be installed from PyPI using pip or installed from its Git repository. · github.com, 10 Oct 2026
- Integration
- The documentation includes a dedicated integration section for TensorFlow. · sacred.readthedocs.io, 10 Oct 2026
- Integrations
- The README describes a Neptune integration that logs Sacred experiments through an observer. · github.com, 4 Oct 2026
- License
- The project is released under the MIT license. · github.com, 10 Oct 2026
- Limit
- Sacred is designed for Python experiments; the README contrasts it with Sumatra for non-Python experiments. · github.com, 4 Oct 2026
- Optional dependencies
- NumPy and PyMongo are optional dependencies that offer additional features. · github.com, 10 Oct 2026
- Parameter access
- Captured functions can receive configuration entries through dependency injection. · sacred.readthedocs.io, 10 Oct 2026
- Purpose
- Sacred helps configure, organize, log, and reproduce computational experiments. · github.com, 10 Oct 2026
- Reproducibility
- Automatic seeding helps control randomness so experiment results remain reproducible. · sacred.readthedocs.io, 10 Oct 2026
- Run logging
- Observers log experiment information and configuration and can save it to a database. · sacred.readthedocs.io, 10 Oct 2026
- Support
- The README directs usage questions to Stack Overflow under the python-sacred tag and invites bug reports and feature requests through GitHub issues. · github.com, 10 Oct 2026
Where Sacred stands on Freedom251
- ML Experiment Tracking Software#24 of 39