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Get Started with DVC

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How cool would it be to track large datasets and machine learning models alongside your code, sidestepping all the limitations of storing it in Git? Imagine cloning a repository and immediately seeing your datasets, checkpoints and models staged in your workspace. Imagine switching to a different version of a 100Gb file in less than a second with a git checkout.

๐Ÿ’ซ DVC is your "Git for data"!

Initializing a project

Before we begin, settle on a directory for this guide. Everything we will do will be self contained there.

Imagine we want to build an ML project from scratch. Let's start by creating a Git repository:

$ mkdir example-get-started
$ cd example-get-started
$ git init

This directory name is used in our example-get-started repo.

Inside your chosen directory, we will use our current working directory as a DVC project. Let's initialize it by running dvc init inside a Git project:

$ dvc init

A few internal files are created that should be added to Git:

$ git status
Changes to be committed:
        new file:   .dvc/.gitignore
        new file:   .dvc/config
$ git commit -m "Initialize DVC"

Now you're ready to DVC!

Tracking data

Working inside an initialized project directory, let's pick a piece of data to work with. We'll use an example data.xml file, though any text or binary file (or directory) will do. Start by running:

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

We used dvc get above to show how DVC can turn any Git repo into a "data registry". dvc get can download any file or directory tracked in a DVC repository.

Use dvc add to start tracking the dataset file:

$ dvc add data/data.xml

DVC stores information about the added file in a special .dvc file named data/data.xml.dvc. This small, human-readable metadata file acts as a placeholder for the original data for the purpose of Git tracking.

Next, run the following commands to track changes in Git:

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

Now the metadata about your data is versioned alongside your source code, while the original data file was added to .gitignore.

dvc add moved the data to the project's cache, and linked it back to the workspace. The .dvc/cache will look like this:

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    โ””โ”€โ”€ a1a2931c8370d3aeedd7183606fd7f

The hash value of the data.xml file we just added (22a1a29...) determines the cache path shown above. And if you check data/data.xml.dvc, you will find it there too:

  - md5: 22a1a2931c8370d3aeedd7183606fd7f
    path: data.xml

Storing and sharing

You can upload DVC-tracked data to a variety of storage systems (remote or local) referred to as "remotes". For simplicity, we will use a "local remote" for this guide, which is just a directory in the local file system.

Configuring a remote

Before pushing data to a remote we need to set it up using the dvc remote add command:

$ mkdir /tmp/dvcstore
$ dvc remote add -d myremote /tmp/dvcstore
$ mkdir %TEMP%/dvcstore
$ dvc remote add -d myremote %TEMP%\dvcstore

DVC supports many remote storage types, including Amazon S3, NFS, SSH, Google Drive, Azure Blob Storage, and HDFS.

An example for a common use case is configuring an Amazon S3 remote:

$ dvc remote add -d storage s3://mybucket/dvcstore

For this to work, you'll need an AWS account and credentials set up to allow access.

To learn more about storage remotes, see the Remote Storage Guide.

Uploading data

Now that a storage remote was configured, run dvc push to upload data:

$ dvc push

dvc push copied the data cached locally to the remote storage we set up earlier. The remote storage directory should look like this:

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    โ””โ”€โ”€ a1a2931c8370d3aeedd7183606fd7f

Usually, we would also want to Git track any code changes that led to the data change ( git add, git commit and git push ).

Retrieving data

Once DVC-tracked data and models are stored remotely, they can be downloaded with dvc pull when needed (e.g. in other copies of this project). Usually, we run it after git pull or git clone.

Let's try this now:

$ dvc pull

After running dvc push above, the dvc pull command afterwards was short-circuited by DVC for efficiency. The project's data/data.xml file, our cache and the remote storage were all already in sync. We need to empty the cache and delete data/data.xml from our project if we want to have DVC actually moving data around. Let's do that now:

$ rm -rf .dvc/cache
$ rm -f data/data.xml
$ rmdir .dvc\cache
$ del data\data.xml

Now we can run dvc pull to retrieve the data from the remote:

$ dvc pull

Making local changes

Next, let's say we obtained more data from some external source. We will simulate this by doubling the dataset contents:

$ cp data/data.xml /tmp/data.xml
$ cat /tmp/data.xml >> data/data.xml
$ copy data\data.xml %TEMP%\data.xml
$ type %TEMP%\data.xml >> data\data.xml

After modifying the data, run dvc add again to track the latest version:

$ dvc add data/data.xml

Now we can run dvc push to upload the changes to the remote storage, followed by a git commit to track them:

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

Switching between versions

A commonly used workflow is to use git checkout to switch to a branch or checkout a specific .dvc file revision, followed by a dvc checkout to sync data into your workspace:

$ git checkout <...>
$ dvc checkout

Return to a previous version of the dataset

Let's go back to the original version of the data:

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

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

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

As you can see, DVC is technically not a version control system by itself! It manipulates .dvc files, whose contents define the data file versions. Git is already used to version your code, and now it can also version your data alongside it.

Following This Guide

To help you understand and use DVC better, consider the following three use-cases: data pipelines, experiment tracking and model management. You may pick any to start learning about how DVC helps you "solve" that scenario!

Choose a trail to jump into its first chapter:

  • Data Pipelines - Use DVC as a build system for reproducible, data driven pipelines.

  • Experiment Management - Easily track your experiments and their progress by only instrumenting your code, and collaborate on ML experiments like software engineers do for code.

  • Model Registry - Use the DVC model registry to manage the lifecycle of your models in an auditable way. Easily access your models and integrate your model registry actions into CICD pipelines to follow GitOps best practices.