zincware/ZnTrack
Create, visualize, run & benchmark DVC pipelines in Python & Jupyter notebooks.
This tool helps scientists, engineers, and researchers make their computational experiments and simulations consistently reproducible. It allows you to organize your Python code into a clear series of steps, where you define what data goes into each step and what results come out. The primary users are researchers working with data processing, simulations, or machine learning models who need to ensure their scientific results can be reliably recreated and shared.
Used by 1 other package. Available on PyPI.
Use this if you need to reliably reproduce your Python-based data processing, simulations, or machine learning experiments, and want to easily track inputs, outputs, and parameters.
Not ideal if your workflow is simple, does not involve complex data dependencies, or you don't need to track or version control your experiment's data and parameters.
Stars
56
Forks
4
Language
Python
License
Apache-2.0
Category
Last pushed
Mar 09, 2026
Commits (30d)
0
Dependencies
8
Reverse dependents
1
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