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DVC

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91
+ 1
2
SVN (Subversion)

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+ 1
43
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DVC vs SVN (Subversion): What are the differences?

  1. Version Control System Type: DVC (Data Version Control) is specifically designed for machine learning models and data, while SVN (Subversion) is a general-purpose centralized version control system.
  2. Tracking Data Files: DVC tracks data files separately from code, allowing for efficient management of large datasets, while SVN treats data files as part of the repository, which can lead to repository bloating.
  3. Handling Large Files: DVC handles large files efficiently by using data file pointers, enabling versioning without storing multiple copies of the same file, whereas SVN struggles with large files due to its centralized nature.
  4. Workflow Optimization: DVC optimizes workflows by providing commands for data and model versioning, making it easier to reproduce experiments and share results, while SVN lacks specific tools for optimizing workflow in a data-centric environment.
  5. Collaborative Features: DVC offers collaborative features such as data pipelines to streamline collaboration on large datasets, enabling teams to work more effectively together, whereas SVN primarily focuses on code collaboration and lacks specialized features for data collaboration.
  6. Data Lineage and Reproducibility: DVC tracks the lineage of data and models, ensuring reproducibility of experiments, while SVN does not offer built-in features for tracking data lineage and ensuring reproducibility.

In Summary, DVC and SVN differ significantly in their focus on handling data, workflow optimization, collaboration features, and ensuring reproducibility in a machine learning-specific environment.

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Pros of DVC
Pros of SVN (Subversion)
  • 2
    Full reproducibility
  • 20
    Easy to use
  • 13
    Simple code versioning
  • 5
    User/Access Management
  • 3
    Complicated code versionioning by Subversion
  • 2
    Free

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Cons of DVC
Cons of SVN (Subversion)
  • 1
    Coupling between orchestration and version control
  • 1
    Requires working locally with the data
  • 1
    Doesn't scale for big data
  • 7
    Branching and tagging use tons of disk space

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What is DVC?

It is an open-source Version Control System for data science and machine learning projects. It is designed to handle large files, data sets, machine learning models, and metrics as well as code.

What is SVN (Subversion)?

Subversion exists to be universally recognized and adopted as an open-source, centralized version control system characterized by its reliability as a safe haven for valuable data; the simplicity of its model and usage; and its ability to support the needs of a wide variety of users and projects, from individuals to large-scale enterprise operations.

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What companies use DVC?
What companies use SVN (Subversion)?
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What tools integrate with DVC?
What tools integrate with SVN (Subversion)?

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What are some alternatives to DVC and SVN (Subversion)?
Pachyderm
Pachyderm is an open source MapReduce engine that uses Docker containers for distributed computations.
MLflow
MLflow is an open source platform for managing the end-to-end machine learning lifecycle.
Git
Git is a free and open source distributed version control system designed to handle everything from small to very large projects with speed and efficiency.
GitHub
GitHub is the best place to share code with friends, co-workers, classmates, and complete strangers. Over three million people use GitHub to build amazing things together.
Visual Studio Code
Build and debug modern web and cloud applications. Code is free and available on your favorite platform - Linux, Mac OSX, and Windows.
See all alternatives