Mastering Model Versioning with DVC and Git: Lessons from the Trenches

When I started working with DVC, I quickly realized there was a gap between theory and what actually happens in practice. This post is about my experience with model versioning using dvc and git. I'll walk you through what I learned, what tripped me up, and the lessons that stuck with me. No fluff — just honest notes from someone who went through it.

Introduction to Model Versioning

As I delved into the world of machine learning operations (MLOps), I quickly realized the importance of model versioning. Keeping track of changes to models, datasets, and training pipelines is crucial for reproducibility and collaboration. In this article, I'll share my experience with using DVC (Data Version Control) and Git for model versioning, highlighting the lessons I learned, the mistakes I made, and the best practices I discovered.

What is DVC and How Does it Work?

DVC is a tool that helps track large files, such as datasets and model artifacts, outside of Git. This is essential because Git is not designed to handle large files, and committing them directly to Git can lead to performance issues and bloated repositories. I learned this the hard way when I committed a 500 MB model file to Git before knowing about DVC. Fortunately, DVC provides a solution by storing large files in remote storage, such as S3, and tracking them in Git using metadata files.

Key DVC Features

One of the most useful features of DVC is dvc repro, which reruns only the changed pipeline stages. This saves a significant amount of time and computational resources, especially when working with complex pipelines. Additionally, DVC's remote storage feature allows any teammate to pull the exact dataset I used, ensuring reproducibility and consistency across the team.

Setting Up DVC and Git

When setting up DVC and Git for model versioning, it's essential to do it right from the start. I learned that setting up DVC on day one, before the dataset gets large, is crucial. This avoids the hassle of dealing with large files in Git and ensures a smooth workflow from the beginning. Another crucial step is to always run dvc push after dvc repro to ensure that the latest changes are pushed to remote storage and tracked in Git.

Example DVC Pipeline

Here's an example dvc.yaml pipeline with a train stage that takes processed data and outputs a model artifact:

stages:
  train:
    cmd: python train.py
    deps:
      - data/processed
    outs:
      - model/artifact

This pipeline defines a single stage, train, which runs the train.py script and depends on the data/processed directory. The output of this stage is the model/artifact directory, which contains the trained model.

Lessons Learned and Mistakes Made

Throughout my experience with DVC and Git, I encountered several challenges and made mistakes. One of the most significant mistakes was forgetting to push DVC artifacts, which resulted in my teammate being unable to reproduce my results. This highlighted the importance of always running dvc push after dvc repro. Another mistake was naming DVC stages inconsistently, which made the pipeline DAG (directed acyclic graph) unreadable. To avoid this, I learned to treat DVC pipeline stages like functions, with one clear input and one clear output.

Best Practices for DVC and Git

Based on my experience, I recommend the following best practices for using DVC and Git:

  • Set up DVC on day one, before your dataset gets large
  • Always run dvc push after dvc repro
  • Treat DVC pipeline stages like functions, with one clear input and one clear output
  • Use consistent naming conventions for DVC stages and directories
  • Use Git tags to track DVC pipeline runs and create a clean model performance history

Wrapping Up

In conclusion, using DVC and Git for model versioning has been a game-changer for my MLOps workflow. By tracking large files outside of Git and using DVC's remote storage feature, I can ensure reproducibility and collaboration across my team. The lessons I learned and the mistakes I made have helped me develop best practices for using DVC and Git, which I hope will be useful for others. By following these best practices and setting up DVC and Git correctly from the start, you can master model versioning and take your MLOps workflow to the next level.

Category: MLOps

DVCModel VersioningGitMLOpsData VersioningMachine LearningVersion Control

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