Get Started: Data Versioning

How cool would it be to make Git handle arbitrary large files and directories with the same performance as with small code files? Imagine doing a git clone and seeing data files and machine learning models in the workspace. Or switching to a different version of a 100Gb file in less than a second with a git checkout.

The foundation of DVC consists of a few commands that you can run along with git to track large files, directories, or ML models. Think "Git for data". Read on or watch our video to learn about versioning data with DVC!

To start tracking a file or directory, use dvc add:

⚙️ Expand to get an example dataset.

Having initialized a project in the previous section, get the data file we will be using later like this:

$ dvc get https://github.com/iterative/dataset-registry \
          get-started/data.xml -o data/data.xml

We use the fancy dvc get command to jump ahead a bit and show how Git repo becomes a source for datasets or models - what we call "data registry" or "model registry". dvc get can download any file or directory tracked in a DVC repository. It's like wget, but for DVC or Git repos. In this case we download the latest version of the data.xml file from the dataset registry repo as the data source.

$ dvc add data/data.xml

DVC stores information about the added file (or a directory) in a special .dvc file named data/data.xml.dvc, a small text file with a human-readable format. This file can be easily versioned like source code with Git, as a placeholder for the original data (which gets listed in .gitignore):

$ git add data/data.xml.dvc data/.gitignore
$ git commit -m "Add raw data"

💡 Expand to see what happens under the hood.

dvc add moved the data to the project's cache, and linked* it back to the workspace.

$ tree .dvc/cache
../.dvc/cache
└── a3
    └── 04afb96060aad90176268345e10355

The hash value of the data.xml file we just added (a304afb...) determines the cache path shown above. And if you check data/data.xml.dvc, you will find it there too:

outs:
  - md5: a304afb96060aad90176268345e10355
    path: data.xml

* See Large Dataset Optimization and dvc config cache for more info. on file linking.

You can upload DVC-tracked data or models with dvc push, so they're safely stored remotely. This also means they can be retrieved on other environments later with dvc pull. First, we need to setup a storage:

$ dvc remote add -d storage s3://mybucket/dvcstore
$ git add .dvc/config
$ git commit -m "Configure remote storage"

DVC supports the following remote storage types: Google Drive, Amazon S3, Azure Blob Storage, Google Cloud Storage, Aliyun OSS, SSH, HDFS, and HTTP. Please refer to dvc remote add for more details and examples.

⚙️ Set up a remote storage

DVC remotes let you store a copy of the data tracked by DVC outside of the local cache, usually a cloud storage service. For simplicity, let's set up a local remote:

$ mkdir -p /tmp/dvcstore
$ dvc remote add -d myremote /tmp/dvcstore
$ git commit .dvc/config -m "Configure local remote"

While the term "local remote" may seem contradictory, it doesn't have to be. The "local" part refers to the type of location: another directory in the file system. "Remote" is how we call storage for DVC projects. It's essentially a local data backup.

$ dvc push

Usually, we also want to git commit and git push the corresponding .dvc files.

💡 Expand to see what happens under the hood.

dvc push copied the data cached locally to the remote storage we set up earlier. You can check that the data has been stored in the DVC remote with:

$ ls -R /tmp/dvcstore
/tmp/dvcstore/:
a3

/tmp/dvcstore/a3:
04afb96060aad90176268345e10355

Retrieving

Having DVC-tracked data stored remotely, it can be downloaded when needed in other copies of this project with dvc pull. Usually, we run it after git clone and git pull.

⚙️ Expand to explode the project 💣

If you've run dvc push, you can delete the cache (.dvc/cache) and data/data.xml to experiment with dvc pull:

$ rm -rf .dvc/cache
$ rm -f data/data.xml

$ dvc pull

📖 See also Sharing Data and Model Files for more on basic collaboration workflows.

Making changes

When you make a change to a file or directory, run dvc add again to track the latest version:

⚙️ Expand to make some changes.

For the sake of simplicity let's just double the dataset artificially (and pretend that we got more data from some external source):

$ cp data/data.xml /tmp/data.xml
$ cat /tmp/data.xml >> data/data.xml

$ dvc add data/data.xml

Usually you would also run git commit and dvc push to save the changes:

$ git commit data/data.xml.dvc -m "Dataset updates"
$ dvc push

Switching between versions

The regular workflow is to use git checkout first to switch a branch, checkout a commit, or a revision of a .dvc file, and then run dvc checkout to sync data:

$ git checkout <...>
$ dvc checkout

⚙️ Expand to get the previous version of the dataset.

Let's cleanup the previous artificial changes we made and get the previous :

$ git checkout HEAD^1 data/data.xml.dvc
$ dvc checkout

Let's commit it (no need to do dvc push this time since the previous version of this dataset was saved before):

$ git commit data/data.xml.dvc -m "Revert dataset updates"

Yes, DVC is technically not even a version control system! .dvc files content defines data file versions. Git itself provides the version control. DVC in turn creates these .dvc files, updates them, and synchronizes DVC-tracked data in the workspace efficiently to match them.

Large datasets versioning

In cases where you process very large datasets, you need an efficient mechanism (in terms of space and performance) to share a lot of data, including different versions of itself. Do you use a network attached storage? Or a large external volume?

While these cases are not covered in the Get Started, we recommend reading the following sections next to learn more about advanced workflows:

A shared external cache can be set up to store, version and access a lot of data on a large shared volume efficiently.
A quite advanced scenario is to track and version data directly on the remote storage (e.g. S3). Check out Managing External Data to learn more.

Get Started: Data Versioning

Storing and sharing

Retrieving

Making changes

Switching between versions

Large datasets versioning

Content