ZnTrack 0.3.4

A Python package for parameter and data version control with DVC

Parameter Tracking for Python

ZnTrack [zɪŋk træk] is an easy-to-use package for tracking parameters in your Python projects. What is a parameter? Anything set by a user in your code, for example, the number of layers in a neural network layer or the window size of a moving average. ZnTrack works by storing the values of parameters in Python classes and functions and monitoring how they change for several different runs. These changes can then be compared graphically to see what effect they had on your workflow. Beyond the standard tracking of parameters in a project, ZnTrack can be used to deploy jobs with a set of different parameter values, avoid the re-running of components of code where parameters have not changed, and to identify computational bottlenecks in your code.

Example

With ZnTrack a DVC Node on the computational graph can be written as a Python class. DVC Options, such as parameters, input dependencies and output files are class attributes.

from zntrack import Node, zn
from random import randrange


class HelloWorld(Node):
    """Define a ZnTrack Node"""
    # parameter to be tracked
    max_number = zn.params()
    # parameter to store as output
    random_number = zn.outs()

    def __init__(self, max_number=None, **kwargs):
        """Pass tracked arguments"""
        super().__init__(**kwargs)
        self.max_number = max_number

    def run(self):
        """Command to be run by DVC"""
        self.random_number = randrange(self.max_number)

This Node can then be saved as a DVC stage

HelloWorld(max_number=512).write_graph()

which builds the DVC stage and can be used e.g., through dvc repro. The results can then be accessed easily via HelloWorld.load().random_number.

More detailed examples and further information can be found in the ZnTrack Documentation.

Technical Details

ZnTrack as an Object-Relational Mapping for DVC

On a fundamental level the ZnTrack package provides an easy-to-use interface for DVC directly from Python. It handles all the computational overhead of reading config files, defining outputs in the dvc.yaml as well as in the script and much more.

For more information on DVC visit their homepage.

Installation

Simply run:

pip install zntrack

Or you can install from source with:

git clone https://github.com/zincware/ZnTrack.git
cd ZnTrack
pip install .

Similar Tools

The following (incomplete) list of other projects that either work together with ZnTrack or can achieve similar results with slightly different goals or programming languages.

• DVC - Main dependency of ZnTrack for Data Version Control.
• dvthis - Introduce DVC to R.
• DAGsHub Client - Logging parameters from within .Python
• MLFlow - A Machine Learning Lifecycle Platform.
• Metaflow - A framework for real-life data science.