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June '22 Community Gems

A roundup of technical Q&A's from the DVC and CML communities. This month: working with the DVC cache, DVC data and remotes, using DVC programmatically, and more.

  • Milecia McGregor
  • June 29, 20228 min read
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Is there a shorthand command to commit changes to all modified files in DVC without manually adding them all individually?

Thanks for the question @Ramnath T!

If you already have data tracked by DVC, the dvc commit command adds all the changes to those files or directories without having to name each target. You'll still need to remember to commit any other changes you've made to Git as well.

If you don't have data tracked by DVC, run dvc add <file name or folder name> and the data will be added to your local cache and no commit is needed. This is how we make DVC aware of any new data we want versioned.

When you run dvc add, a file hash will be calculated, the file content will be moved to the cache, and a .dvc file will be created to start tracking the added data. If you're working with remotes using the --to-remote option, you can skip the local cache entirely and move the file contents directly to your remote storage.

How can I connect Iterative Studio to a remote repo on a private network, like a GitLab server?

Good question about Iterative Studio from @LilDataScientist!

This is something that our users asked quite a bit, so we wrote up a whole guide about custom GitLab server connections. It's a quick walkthrough of how to set up the permissions you'll need and connecting your team to Studio.

You can find lots of great guides and explanations about everything Studio in the User Guide section of the docs!

How does dvc get-url interact with the cache compared to dvc import-url?

This is an awesome question from @Gema Parreno!

When you run dvc get-url, it downloads the file/directory to your local file system. It's not tracking the downloaded data with a .dvc file. It's just pulling that data from some source to your file system. If you want to download a file or directory without needing a DVC project, you can use the dvc get-url command.

On the other hand, when you run dvc import-url, the local cache folder inside of .dvc will be updated. This is similar to running dvc get-url and dvc add together except that dvc import-url also saves a link to the original file/directory location so that if it changes, you can download the updated data.

There is one more option to bypass the local cache and transfer data directly to your remote storage using dvc import-url <url> --to-remote. This doesn't download anything to your local cache so it's another way to transfer data between remotes.

If an image is present in different directories in different projects, will the shared cache store them both as one hash or will their different paths mean the same image appears twice in the cache?

Great question about the cache from @paulwrightkcl!

DVC will index the whole directory, but there will only be one hash per file. So the same image will only appear once in the cache. What will be duplicated in the cache is the .dir hash that DVC uses internally as the directory tree representation.

In summary, the image file is only stored in the shared cache once unless it's modified in one of the directories.

Is it possible to limit which columns show for experiments in the metrics table?

Nice question from @DylanTF!

You can use dvc exp show --drop (or --keep) to decide what to hide (or show). For example, if you have a table like this:

 ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
  neutral:**Experiment**   neutral:**Created**        metric:**avg_prec**   metric:**roc_auc**   param:**train.seed**   param:**train.n_est**   param:**train.min_split**   dep:**./clf**   dep:**./data**    dep:**./data/train.pkl**   dep:**./src/train.py**   dep:**src/evaluate.py**
 ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
  workspace    -                     -         -   20210428     300           75                -       a9bb63e   aded63c            bdc3fe9          b0ef2a1
  mlem-serve   Jun 16, 2022    0.76681   0.38867   20210428     300           75                -       a9bb63e   aded63c            bdc3fe9          b0ef2a1
 ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────

You could clean it up with a command like this:

$ dvc exp show --drop 'Created|train.seed|./clf|./data/*|./src/train.py|src/evaluate.py'

Then get a table like this:

 ─────────────────────────────────────────────────────────────────
  neutral:**Experiment**   metric:**avg_prec**   metric:**roc_auc**   param:**train.n_est**   param:**train.min_split**
 ─────────────────────────────────────────────────────────────────
  workspace           -         -   300           75
  mlem-serve    0.76681   0.38867   300           75
 ─────────────────────────────────────────────────────────────────

Alternatively, you can run the following command to only show the columns that have changed in the experiment run:

$ dvc exp show --only-changed

This will produce a table similar to this one:

 ─────────────────────────────────────────────────────────────────────────────
  neutral:**Experiment**   neutral:**Created**        metric:**avg_prec**   metric:**roc_auc**  param:**train.n_est**   dep:**src/train.py**
 ─────────────────────────────────────────────────────────────────────────────
  workspace    -                     -         -   325           94279e0
  mlem-serve   Jun 16, 2022    0.76681   0.38867   300           bdc3fe9
 ─────────────────────────────────────────────────────────────────────────────

You can also look at/edit these tables with the DVC VS Code extension! If you're interested in more advanced visualizations, you should try out Iterative Studio.

Is it possible to create, commit, and push updates to datasets using DVC with Python instead of the command line?

Fantastic question from @wlu07!

Yes, we do have an internal Repo class to do DVC operations using Python. You can refer to the GitHub repo for the DVC CLI commands to see how the CLI arguments are translated into the Repo function arguments and you can see how to use some of the Repo methods in our docs.

Here's an example of how you might run DVC commands using Python:

from dvc.repo import Repo

repo = Repo(".")

repo.add("test_dataset.csv")

repo.push()

Keep in mind that dvc.repo.Repo is not an official public API, so there is no guarantee it will always be in stable state.

How can I write generated artifacts back to a GitHub repo after a GitHub workflow is finished?

Wonderful CML question from @Fourtin!

If you want to add the artifact to your repo just like you would a file, then you should check out the cml pr <file> command. You can use this to merge pull requests to the same branch the workflow was triggered from.

For example, if you run a command like:

$ cml pr --squash train.py

It will run git add train.py, commit the change, create a new branch, open a pull request, and squash and merge it.

Is there a way to programmatically update the content of params.py?

Thanks for asking this @petek!

If you have a params.py file like this:

class TrainTestSplit:
    FOLDER = "data/train_test_split"
    SPLIT_METHOD = "proportional"

In DVC, you can update the params and run dvc exp run --set-param <param>. Here's an example of what that might look like:

$ dvc exp run --set-param params.py:TrainTestSplit.SPLIT_METHOD="proportional"

Note: It may not be able to update Python parameters correctly. Because of this, we recommend you use params.yaml files.

If you need a pure Python solution, you could try something like this:

from dvc.utils.serialize import modify_py

with modify_py("params.py") as d:
    d["key"] = "value"

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