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
Clients alternatives to Sacred
Streamline experiment tracking, visualise data insights, and collaborate seamlessly with comprehensive version control tools.
See more details See less details
This software offers a robust platform for tracking and managing machine learning experiments efficiently. It allows users to visualise data insights in real-time and ensures that all team members can collaborate effortlessly through built-in sharing features. With comprehensive version control tools, the software fosters an organised environment, making it easier to iterate on projects while keeping track of changes and findings across various experiments.
Read our analysis about Comet.mlTo Comet.ml product page
Offers comprehensive monitoring tools for tracking experiments, visualising performance metrics, and facilitating collaboration among data scientists.
See more details See less details
Neptune.ai is a powerful platform designed for efficient monitoring of experiments in data science. It provides tools for tracking and visualising various performance metrics, ensuring that users can easily interpret results. The software fosters collaboration by allowing multiple data scientists to work together seamlessly, sharing insights and findings. Its intuitive interface and robust features make it an essential tool for teams aiming to enhance productivity and maintain oversight over complex projects.
Read our analysis about Neptune.aiTo Neptune.ai product page
This software offers comprehensive tools for tracking and managing machine learning experiments, ensuring reproducibility and efficient collaboration.
See more details See less details
ClearML provides an extensive array of features designed to streamline the monitoring of machine learning experiments. It allows users to track metrics, visualise results, and manage resource allocation effectively. Furthermore, it facilitates collaboration among teams by providing a shared workspace for experiment management, ensuring that all relevant data is easily accessible. With its emphasis on reproducibility, ClearML helps mitigate common pitfalls in experimentation, making it an essential tool for data scientists and researchers.
Read our analysis about ClearMLTo ClearML product page
Appvizer Community Reviews (0) The reviews left on Appvizer are verified by our team to ensure the authenticity of their submitters.
Write a review No reviews, be the first to submit yours.