DataTerminology.comNEW
A Python framework to configure, run, and log experiments reproducibly with structured configuration and metadata.
DESCRIPTION
Sacred helps structure machine learning experiments with explicit configuration, logging, and reproducibility guarantees. It separates code from parameters, supports observers for persistent logging, and captures run metadata. Integration with MongoDB and filesystems enables durable experiment storage. Users can run parameter sweeps and track results consistently. Sacred complements trackers like MLflow or WandB by standardizing configuration and run structure. It fosters reproducible research and organized experimentation.
ALTERNATIVE DEFINITIONS
Sacred GitHub
Sacred is a tool to help you configure, organize, log and reproduce experiments.
Focuses on configuration, organization, logging, and reproducibility as core pillars.
Sacred Documentation
Sacred helps you configure, organize, log and reproduce experiments.
Concise statement mirroring the project’s scope and goals.
RELATED TERMS
Experiment Tracking Configuration Management Reproduction Weights & Biases Python Logging Parameter Sweeps MLOps Metadata Management
COMPETING TERMS
Hydra MLflow
KNOWLEDGE AREAS
MLOps AI Data Science Platform & Tooling
TARGET AUDIENCE
Data Scientist MLOps Engineer AI Engineer Software Developer
EXTERNAL SOURCES
- Sacred - Quickstart for running experiments
- Sacred - Observers for persistent logging
- IDSIA - Source code and project overview
TOP QUESTIONS
Why use Sacred for ML experiments?
It enforces structure for configurations and logging, improving reproducibility and comparability across runs with minimal boilerplate.
Category: Relevancy
How does Sacred store experiment results?
Through observers like MongoObserver or FileStorageObserver that persist configurations, metrics, and artifacts.
Category: Application
What’s a common mistake with Sacred?
Hard-coding parameters instead of using configuration scopes reduces reproducibility and clarity.
Category: Mistakes
How does Sacred compare to Hydra?
Sacred focuses on experiments and logging; Hydra excels in hierarchical configuration composition and overrides. They can be used together.
Category: Alternatives
Can Sacred integrate with other trackers?
Yes, many teams pair Sacred’s configuration with MLflow or WandB for model registry and visualization.
Category: Application
How does Sacred aid reproducibility?
It captures configs, source code hashes, and environment info so runs can be recreated reliably.
Category: Requirements
Is Sacred suitable for large-scale sweeps?
Yes, but orchestration is external; use tools like Ray or schedulers to parallelize Sacred runs.
Category: Opinion
Who maintains Sacred?
Originally developed at IDSIA, it’s community-maintained as an open-source project.
Category: Origin
How to start using Sacred quickly?
Follow the quickstart, define an Experiment, create a config, and add an observer for persistence.
Category: Requirements