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DVC dependency management – a guide

26.8.2019 | 10 minutes of reading time

This post is a follow-up to A walkthrough of DVC that deals with managing dependencies between DVC projects. In particular, this follow-up is about importing specific versions of an artifact (e.g. a trained model or a dataset) from one DVC project into another.

We discuss two recently added DVC features, namely dvc get and dvc import.

  • Intuitively, dvc get downloads an artifact from a DVC project (where DVC takes care of resolving the project’s DVC cache and its remotes). For example, dvc get is a very helpful tool in delivering your model to production in a CI/CD pipeline.
  • Think of dvc import as dvc get plus managing version information for the downloaded artifact. That is, using dvc import you can manage artifacts from another DVC project like you would manage a software library dependency in a software engineering project. For example, dvc import helps you when organizing dataset registries.

You might want to browse through the walkthrough first, so that you can get the most out of this post. For those of you in a hurry, we provide a quick recap of the original walkthrough . In the first part of this article we create a playground project which we use as a DVC dependency hands-on in the second part. If you’re in a hurry, still, note that part one can be skipped. A publicly accessible playground project is provided, so that you can step right into the hands-on fun in part two.

A real-world application of dvc get and dvc import will be discussed in an upcoming post. So stay tuned for more 😀

Recap of the original walkthrough

In A walkthrough of DVC , we trained a classifier for hand-written numbers. The walkthrough showed how implementing a DVC-pipeline makes all of data loading, preprocessing, training, performance evaluation, etc. fully reproducible. The gist is that DVC versions training data, (hyper-)parameters, code and trained models together. In particular, a DVC project resides in a Git repository, which implements all necessary versioning (see the following figure). Binary data, such as e.g. training data and trained models, are located in DVC’s so-called cache. In particular, for each version of the pipeline, the cache contains different versions of all binary data. However, cache data is not stored in the Git repository itself, but in a separate so-called remote (e.g. an Amazon S3 bucket). When checking out a specific version of the pipeline from the Git repository, DVC takes care of fetching cache data that matches the current pipeline version from the remote.

DVC dependency management: Creating the playground

As for the original walkthrough, the GitHub repository for the post you are reading now provides a readily usable working environment. In this environment, you can interactively create the playground number classifier project (or let a script perform all actions for you). Compared to the original walkthrough, the following extensions were implemented. First, the playground project is pushed to GitHub so that it can easily be referenced as a DVC dependency. Secondly, the playground’s DVC cache is now pushed to a remote in an Amazon S3 bucket, so that binary data (e.g. trained models) can also be accessed from the internet.

To prepare the working environment (see the following code block), clone the GitHub repository , change into the cloned directory, and start the working environment using ./ bash. You will be ‘logged in’ to a newly created container. From the prompt in the container, configure variables at the top of the file /home/dvc/scripts/ to match your GitHub repository and S3 bucket, where both must be empty and writable. (Details of the bucket configuration are discussed in section Configuring the S3 remote .) Then, to automatically perform all steps from the extended walkthrough, you might want to execute /home/dvc/scripts/ After the script is finished, the GitHub repository and the S3 bucket will now contain the playground DVC project and its cache data, respectively.

1# $ is the host prompt
2# $$ is the working environment container prompt
4$ git clone
5$ cd dvc-deps-management
6$ vi scripts/ # configure GitHub and S3 at the top
7$ ./ bash # create and 'log in' to working environment
8$$ /home/dvc/scripts/ # creates the playground DVC project and its cache

Configuring the S3 remote

In this section, we take a quick look at the part of the scripts/ script that sets up an S3 bucket as the DVC cache’s remote.

To access an S3 bucket, DVC relies on the boto3 library1 , i.e., boto3 must be given access to the bucket. Therefore, AWS credentials can be provided as environment variables like in the following code block. (For the provided working environment, this configuration has to be done at the top of the scripts/ script.)

3$$ # from here on, DVC can interact with your bucket

Other means of configuring S3 bucket access for DVC/boto3 are documented here .

Adding an S3 bucket as a remote to a DVC project is the same as adding any other type of remote (see the following code block or scripts/ Since the given URI starts with s3://, DVC knows that the remote should reside in a bucket. The -d flag tells DVC that this remote should be used by default. Once the bucket is added, the DVC pipeline’s configuration in .dvc/config should be saved, by committing the changes to Git.

1$$ dvc remote add -d playground_remote s3://<YOUR_BUCKET_NAME>
2$$ git add .dvc/config # save the configuration of the newly added remote

Using a DVC project as a DVC dependency

We discuss how to access data from a DVC project. To be more precise, we want to access some (possibly binary) file processed and/or created by the pipeline defined in the project. E.g., we want to access some “source” data like a set of training images, or the model created by the training stage. Recall that the pipeline and all data is versioned. Consequently, any version of any data can be accessed.

We present two ways to access data, namely dvc get and dvc import. Intuitively, dvc get simply downloads data. dvc import downloads data and additionally considers what version was fetched.


The working environment given in section Creating the playground should also be used to follow along interactively with the commands presented in this section. If you did not create your own playground project when setting up the environment, you can ignore all steps regarding scripts/

All commands discussed from here on are also available in /home/dvc/scripts/ in the working environment.

To enable DVC to access the playground project, recall that DVC needs to know its GitHub repository’s URL. Also, cache data is located in an S3 bucket remote and read access to that bucket must be provided. Configure your environment as follows:


If you did not create your own playground project, you can use this publicly available playground project . Its URL and credentials are readily configured in /home/dvc/scripts/ (Note that the given AWS credentials provide no permissions except read access to the S3 bucket.)

dvc get

When getting data from a DVC project, DVC takes care of ‘downloading’ the desired version from the file. In the following example, we download version 0.3 of the trained model from the playground project.

1$$ # replace with your playground GitHub repo (if any)
4$$ dvc get --rev 0.1 $GIT_REPO model/model.h5
5$$ ls
6model.h5 # no other file was downloaded

In particular, observe that no file was downloaded besides model.h5. Consequently, there is no way for DVC to tell which version of model.h5 is in your file system. Hence, issuing the same dvc get command again, model.h5 will be downloaded again.

Recall that the file model.h5 is not part of the Git repository of the DVC project. To download the file, DVC transparently resolves the cache remote of your project, see the following figure. In our example, DVC inspects the following files in the tag 0.3 of $GIT_REPO:

1# file .dvc/cache
2['remote "playground_remote"']
3url = s3://
5remote = playground_remote
7# file train.dvc
8# [...]
10- md5: 1042d7fd78dd740019699eaaefd1822f
11  path: model/model.h5
12  cache: true
13  metric: false
14  persist: false

From there, DVC deducts that model.h5 is a file with md5 sum 1042d7fd78dd740019699eaaefd1822f in the given remote S3 bucket.

dvc import

dvc import adds version control to dvc get, i.e., dvc import is meant to manage a software project’s dependencies to some DVC project’s data. The project that receives an import must itself be a DVC project (although it does not have to be an ML project). This way, DVC is able to track the desired version of the imported data as the software project evolves over time.

Here is an example of creating a new project that imports data (model.h5) from another DVC project.

1$$ git init
2$$ dvc init
3$$ git add .
4$$ git commit -m 'initialize repository'
5$$ dvc import --rev 0.1 $GIT_REPO model/model.h5
7$$ # besides downloading the data,
8$$ # dvc also stores some versioning information ...
9$$ ls
10model.h5  model.h5.dvc
12$$ # ... which contains the 'source' of the data
13$$ cat model.h5.dvc
16- repo:
17    url:
18    [...]
19    rev: '0.1'
20  path: model/model.h5

Observe that, following the DVC approach, the imported binary file model.h5 will not be committed to the Git repository. Instead, the import is managed by committing the DVC file model.h5.dvc to our project. Following the naming convention that .dvc files represent stages of a pipeline, a .dvc file for an import is called an import stage.

1$$ git status # model.h5 is not listed as untracked ...
2    .gitignore
3    model.h5.dvc
4$$ cat .gitignore # ... since it's ignored
6$$ git add model.h5.dvc .gitignore
7$$ git commit -m 'import model.h5 (version 0.1)'
8$$ git tag v0.0.1

As time goes by and your project evolves, a new version of the import becomes available. Updating to the new version is easy:

1$$ dvc import --rev 0.2 $GIT_REPO model/model.h5 # bump revision to 0.2
2$$ # the file model.h5 now contains the newer version of the model
3$$ git status
4    modified:   model.h5.dvc
5$$ git add model.h5.dvc
6$$ git commit -m 'update model.h5 to version 0.2'
7$$ git tag v0.0.2 # version 0.0.2 of our project uses version 0.2 of model.h5

Cloning a project with DVC dependencies

In the project created in the previous section, the Git repository does not contain the binary file model.h5. So, if one of your team members clones this project, she will not receive model.h5.

1$$ # checkout tag v0.0.1 of the software project
2$$ ls
3model.h5.dvc # the model is not part of the git repository

How does she obtain model.h5 without reproducing the entire pipeline? She can reproduce the import stage using dvc repro. (At the time of writing, the stage file has to be manually unlocked/locked. However, there are plans to implement the use case in dvc pull.)

1$$ dvc unlock model.h5.dvc
2$$ dvc repro model.h5.dvc
3$$ dvc lock model.h5.dvc # fix lock
4$$ ls
5model.h5  model.h5.dvc # dvc downloaded version 0.1 of model.h5

When issuing the same commands again, DVC detects that the version of model.h5 did not change, and therefore does not download the data again.

1$$ dvc unlock model.h5.dvc; dvc repro model.h5.dvc; dvc lock model.h5.dvc
2Stage 'model.h5.dvc' didnt change.
3Data and pipelines are up to date.

However, when updating the import for another version, DVC detects that another binary must be fetched.

1$$ git checkout -f v0.0.2
2$$ dvc unlock model.h5.dvc; dvc repro model.h5.dvc; dvc lock model.h5.dvc
3WARNING: Output 'model.h5' of 'model.h5.dvc' changed because it is 'not in cache'
4WARNING: Stage 'model.h5.dvc' changed.
5Reproducing 'model.h5.dvc'
6Importing 'model/model.h5 (' -> 'model.h5'
7Preparing to download data from 's3://'

After the command has executed, the correct version of model.h5 was downloaded and is available in your file system.


We introduced you to the DVC commands get, import, and update which allow you to manage artifacts from a DVC project as dependencies. While get simply downloads an artifact, import and update enable you to track the version of the artifact in your software project.

In an upcoming post, we will demo these features in a real-world application. We’re happy to meet you there 😀


1 To install boto3 alongside DVC, issue the command pip install 'dvc[s3]', preferably in a virtual environment .

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