Sacred : Lightweight experiment tracking for machine learning
Sacred: in summary
Sacred is an open-source Python library designed to facilitate reproducible machine learning experiments by helping researchers and developers organize, configure, log, and track experiments in a lightweight and flexible way. Originally developed by the Swiss AI lab IDSIA, Sacred is used in academic and research contexts where structured experiment management, traceability, and minimal setup overhead are important.
Unlike full-featured platforms, Sacred provides a code-centric, dependency-free approach to experiment monitoring, with optional integrations for storage and visualization (e.g., with MongoDB and Sacredboard).
Key benefits:
Simple, code-based way to log configurations, results, and metadata
Designed for reproducibility and minimal external dependencies
Suitable for researchers and developers working in Python environments
What are the main features of Sacred?
Configuration management and reproducibility
Tracks all configurable parameters of an experiment via decorators
Uses named configurations and ingredients to manage complex setups
Automatically captures source code versions, command-line arguments, and dependencies
Ensures that experiments can be re-executed identically
Logging and result tracking
Logs metrics, status, artifacts, and exceptions during execution
Supports structured result output and custom observers
Records start/end time, host information, and exit codes
Integrates with MongoDB to persist experiment runs and metadata
Observers and extensibility
Uses observer classes to send experiment data to different backends
Built-in observers: MongoDB, file storage, Slack (notifications), SQL, and more
Developers can create custom observers for new storage or notification systems
Modular architecture allows easy extension for specific needs
Minimalistic and framework-agnostic
Does not depend on any specific ML library or data pipeline tool
Can be integrated with any training loop, model, or data source
Lightweight and suitable for academic and scripting-based workflows
Maintains high compatibility with standard Python workflows
Optional visualization with Sacredboard
Sacredboard provides a web interface to browse, search, and compare experiments
Displays configurations, logs, metrics, and outputs
Helps analyze and navigate experiment history from MongoDB storage
Useful for collaborative research and reviewing long-running experiments
Why choose Sacred?
Designed for clarity, simplicity, and reproducibility in ML experiments
Lightweight, open-source, and easy to integrate into existing workflows
Highly flexible thanks to custom observers and code-centric configuration
Ideal for academic research, rapid prototyping, and offline experiment tracking
Enables transparent documentation of all experiment settings and outcomes
Sacred: its rates
Standard
Rate
On demand
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