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Data Version Control

Core Module

In this module, we are going to return to version control. However, this time we are going to focus on version control of data. The reason we need to separate between standard version control and data version control comes down to one problem: size.

Classic version control was developed to keep track of code files, which all are simple text files. Even a codebase that contains 1000+ files with millions of lines of code can probably be stored in less than a single gigabyte (GB). On the other hand, the size of data can be drastically bigger. As most machine learning algorithms only get better with the more data that you feed them, we are seeing models today that are being trained on petabytes of data (1.000.000 GB).

Because this is an important concept there exist a couple of frameworks that have specialized in versioning data such as DVC, DAGsHub, Hub, Modelstore and ModelDB. Regardless of what framework, they all implement somewhat the same concept: instead of storing the actual data files or in general storing any large artifacts files we instead store a pointer to these large flies. We then version control the point instead of the artifact.


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We are in this course going to use DVC provided by as they also provide tools for automatizing machine learning, which we are going to focus on later.

DVC: What is it?

DVC (Data Version Control) is simply an extension of git to not only take versioning data but also models and experiments in general. But how does it deal with these large data files? Essentially, DVC will just keep track of a small metafile that will then point to some remote location where your original data is stored. Metafiles essentially work as placeholders for your data files. Your large data files are then stored in some remote location such as Google Drive or an S3 bucket from Amazon.


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As the figure shows, we now have two remote locations: one for code and one for data. We use git pull/push for the code and dvc pull/push for the data. The key concept is the connection between the data file model.pkl which is fairly large and its respective metafile model.pkl.dvc which is very small. The large file is stored in the data remote and the metafile is stored in the code remote.

❔ Exercises

If in doubt about some of the exercises, we recommend checking out the documentation for DVC as it contains excellent tutorials.

  1. For these exercises, we are going to use Google drive as a remote storage solution for our data. If you do not already have a Google account, please create one (we are going to use it again in later exercises). Please make sure that you at least have 1GB of free space.

  2. Next, install DVC and the Google Drive extension

    pip install dvc
    pip install dvc-gdrive

    If you installed DVC via pip and plan to use cloud services as remote storage, you might need to install these optional dependencies: [s3], [azure], [gdrive], [gs], [oss], [ssh]. Alternatively, use [all] to include them all. If you encounter that the installation fails, we recommend that you start by updating pip and then trying to update dvc:

    pip install -U pip
    pip install -U dvc-gdrive

    If this does not work for you, it is most likely due to a problem with pygit2 and in that case we recommend that you follow the instructions here.

  3. In your MNIST repository run the following command from the terminal

    dvc init

    this will setup dvc for this repository (similar to how git init will initialize a git repository). These files should be committed using standard git to your repository.

  4. Go to your Google Drive and create a new folder called dtu_mlops_data. Then copy the unique identifier belonging to that folder as shown in the figure below


    Using this identifier, add it as a remote storage

    dvc remote add -d storage gdrive://<your_identifier>
  5. Check the content of the file .dvc/config. Does it contain a pointer to your remote storage? Afterwards, make sure to add this file to the next commit we are going to make:

    git add .dvc/config
  6. Call the dvc add command on your data files exactly like you would add a file with git (you do not need to add every file by itself as you can directly add the data/ folder). Doing this should create a human-readable file with the extension .dvc. This is the metafile as explained earlier that will serve as a placeholder for your data. If you are on Windows and this step fails you may need to install pywin32. At the same time, the data folder should have been added to the .gitignore file that marks which files should not be tracked by git. Confirm that this is correct.

  7. Now we are going to add, commit and tag the metafiles so we can restore to this stage later on. Commit and tag the files, which should look something like this:

    git add data.dvc .gitignore
    git commit -m "First datasets, containing 25000 images"
    git tag -a "v1.0" -m "data v1.0"
  8. Finally, push your data to the remote storage using dvc push. You will be asked to authenticate, which involves copy-pasting the code in the link prompted. Check out your Google Drive folder. You will see that the data is not in a recognizable format anymore due to the way that dvc packs and tracks the data. The boring detail is that dvc converts the data into content-addressable storage which makes data much faster to get. Finally, make sure that your data is not stored in your Github repository.

    After authenticating the first time, DVC should be setup without having to authenticate again. If you for some reason encounter that dvc fails to authenticate, you can try to reset the authentication. Locate the file $CACHE_HOME/pydrive2fs/{gdrive_client_id}/default.json where $CACHE_HOME depends on your operating system:


    This is the typical location, but it may vary depending on what distro you are running


    Delete the complete {gdrive_client_id} folder and retry authenticating with dvc push.

  9. After completing the above steps, it is very easy for others (or yourself) to get setup with both code and data by simply running

    git clone <my_repository>
    cd <my_repository>
    dvc pull

    (assuming that you give them access right to the folder in your drive). Try doing this (in some other location than your standard code) to make sure that the two commands indeed download both your code and data.

  10. Lets look about the process of updating our data. Remember the important aspect of version control is that we do not need to store explicit files called, etc. but just have a single that where we can always checkout earlier versions. Initially start by copying the data data/corruptmnist_v2 folder from this repository to your MNIST code. This contains 3 extra datafiles with 15000 additional observations. Rerun your data pipeline so these gets incorporated into the files in your processed folder.

  11. Redo the above steps, adding the new data using dvc, committing and tagging the metafiles e.g. the following commands should be executed (with appropriate input):

    dvc add -> git add -> git commit -> git tag -> dvc push -> git push.

  12. Lets say that you wanted to go back to the state of your data in v1.0. If the above steps have been done correctly, you should be able to do this using:

    git checkout v1.0
    dvc checkout

    confirm that you have reverted to the original data.

  13. (Optional) Finally, it is important to note that dvc is not only intended to be used to store data files but also any other large files such as trained model weights (with billions of parameters these can be quite large). For example, if we always store our best-performing model in a file called best_model.ckpt then we can use dvc to version control it, store it online and make it easy for others to download. Feel free to experiment with this using your model checkpoints.

In general dvc is a great framework for version-controlling data and models. However, it is important to note that it does have some performance issue when dealing with datasets that consist of many files. Therefore, if you are ever working with a dataset that consists of many small files, it can be a good idea to:

  • zip files into a single archive and then version control the archive. The zip archive should be placed in a data/raw folder and then unzipped in the data/processed folder.

  • If possible turn your data into 1D arrays, then it can be stored in a single file such as .parquet or .csv. This is especially useful for tabular data. Then you can version control the single file instead of the many files.

🧠 Knowledge check

  1. How do you know that a repository is using dvc?


    Similar to a git repository having a .git directory, a repository using dvc needs to have a .dvc folder. Alternatively you can you the dvc status command.

  2. Assume you just added a folder called data/ that you want to track with dvc. What is the sequence of 5 commands to successful version control the folder? (assuming you already setup a remote)

    dvc add data/
    git add .
    git commit -m "added raw data"
    git push
    dvc push

That's all for today. With the combined power of git and dvc we should be able to version control everything in our development pipeline such that no changes are lost (assuming we commit regularly). It should be noted that dvc offers more than just data version control, so if you want to deep dive into dvc we recommend their pipeline feature and how this can be used to setup version controlled experiments. Note that we are going to revisit dvc later for a more permanent (and large-scale) storage solution.