Look here every month for great discussions and technical Q&A's from our users and core development team.
Welcome to the Februrary roundup of useful, intriguing, and good-to-know discussions going on with DVC users and developers. Let’s dive right in with some questions from our Discord channel.
dvc checkout is written for a
Git-like experience, meaning
that it will sync your local workspace with all the model files, dependencies,
and outputs specified by a project’s
.dvc files. If you only want to access
one artifact from the project, you can do this with
dvc checkout <path to file>. This will deliver the specified file to your
If you’re interested in sharing specific artifacts (like data files or model
binaries) with other users, you might also consider
dvc get and
These functions are ideal for downloading a single file (or a few files) to the
local workspace, instead of the whole project.
Most of this functionality is supported by DVC already. We recommend
dvc import as a method for giving users access to data in a repostiory (and
also check out our
tutorial on data registries).
For pre-processing data,
DVC pipelines can automate a
procedure for transforming and cleaning inputs (i.e., you can use bash scripts
dvc run the pipeline whenever a user selects a dataset). Saving the
workspace after experimentation, including model files, metrics, and outputs, is
a core function of DVC (see
dvc add and
dvc push functions). We also have a
so users can load artifacts like datasets and model files into their local
Python session. When they’re done experimenting, they can
dvc add and
dvc push their progress. Users can later “pull” a saved workspace and all
associated files using
As for how to organize hundreds of separate experiments, we’re still evolving our strategy and best-practice recommendations. It’s conceivable that each experiment could be carried out and saved on a separate branch of a project repository. Our thoughts about structuring version control around architecture search and hyperparameter tuning could fill up a whole blog (and probably will in the not-so-distant future); check out one of our recent conversation threads if you’d like to see where we’re currently at. And please let us know how your use case goes—at this stage, we’d love to hear what works for you.
config.localfiles? Is it safe to do git commit without including my config file?
There are indeed two kinds of config files you might come across in your project
.dvc folder and
.gitignore file. The key difference is that
config is intended to be committed to Git, while
config.local is not. You’d
config.local to store sensitive information (like personal credentials for
SSH or another kind of authenticated storage) or settings specific to your local
environment—things you wouldn’t want to push to a GitHub repo. DVC only modifies
config.local when you explicitly use the
--local flag in the
dvc config or
dvc remote * commands, so outside of these cases you shouldn’t have to worry
As for using
git commit without the
config file, it is safe. But you
should check if there are any settings in
config.local that you actually want
to save to
config. This would be rare, since as we mentioned, you’d only have
config.local if you expressly called for them with the
http://link. But the tutorial on DVC shows Azure storage accessed with the
azure://protocol. Which is right?
What you’re describing is exactly as it should be.
azure:// is an internal URL
protocol that tells DVC which API to use to connect to your remote storage, not
the exact address of your Blob. You can use the format
azure://<container-name>/<optional-path>. For more details, you can refer to
our documentation about
supported storage types.
gdrive_client_secret, or maybe give them permission to access my Google Drive folder?
For Google Drive,
gdrive_client_secret aren’t used to
access a specific user’s Google Drive disk; they’re predominantly used by
Google’s API to
track usage and set appropriate rate limits.
So the risk in sharing them is not that your personal files will be vulnerable,
but that your API usage limits could be negatively affected if others are using
it with your credentials. Whether this risk is acceptable is up to you. It’s not
unusual for teams and organizations to share a set of credentials, so a
reasonable level of security may mean ensuring that the
config file for your
project (which typically contains Google Drive credentials) is only visible to
Please check out our docs about Google Drive, too, for more about how DVC uses the Google Drive API.
homebrewand got a “SHA256 mismatch” error. What’s going on?
What most likely happened is that you first installed DVC via
brew install iterative/homebrew-dvc/dvc, which is no longer supported—because
DVC is now a core Homebrew formula! Please uninstall and reinstall using
brew install dvc for uninterrupted upgrades in the future.
This question is from a Reddit discussion.
Versioning the meta-data associated with your dataset is certainly a workable
strategy. You can use prefixes and suffixes to distinguish models trained on
different versions of data, and keep your data files in one
directory. That may be enough for some projects. In our experience, though,
we’ve found this comes with a host of complications that don’t scale well:
We designed DVC to optimize data management from the user’s perspective: users can change the dataset version without changing their code, so organizations don’t have to adhere to explicit filenaming conventions and hardcoded links that are prone to human error. Furthermore, versioning data similar to how Git versions code provides a largely immutable record of every change that has occurred. We think this is important as teams and projects grow in complexity. And from a systems-level perspective, DVC does more than track data: it dedpulicates files behind the scenes, provides simple interfaces for sharing datasets (and models!) with collaborators and users, and connects specific model files with the dataset versions they were trained on.
To summarize, DVC is not the only way to version your data. But we think it’s one way to reduce the overhead of managing data infrastructure when your project involves experimentation or collaboration.