Every month we share news, findings, interesting reads, community takeaways, and everything else along the way. Some of those are related to our brainchild DVC and its journey. The others are a collection of exciting stories and ideas centered around ML best practices and workflow.
There’s a lot of action in our Discord channel these days. Ruslan, DVC’s core maintainer, said it best with a gif.
It’s a lot to keep up with, so here are some highlights. We think these are useful, good-to-know, and interesting conversations between DVC developers and users.
For an in-depth answer, check out this
Stack Overflow discussion.
But in brief, with DVC you don’t need a special server, and you can use nearly
any kind of storage (S3, Google Cloud Storage, Azure Blobs, your own server,
etc.) without a fuss. There are also no limits on the size of the data that you
can store, unlike with GitHub. With Git LFS, there are some general LFS server
limits, too. DVC has additional features for sharing your data (e.g.,
dvc import) and has pipeline support, so it does much more than LFS. Plus, we
have flexible and quick checkouts, as we utilize different link types (reflinks,
symlinks, and hardlinks). We think there are lots of advantages; of course, the
usefulness will depend on your particular needs.
DVC is built to work with the SSH protocol to access remote storage (we provide some examples in our official documentation). When SSH requires a key file, try this:
$ dvc remote modify myremote keyfile <path to *.pem>
You can specify a directory as a dependency/output in your DVC pipeline, and store checkpointed models in that directory. It might look like this:
$ dvc run \ -f train.dvc \ -d data \ -d train.py \ -o models python code/train.py
models is a directory created for checkpoint files. If you would like to
preserve your models in the data directory, though, then you would need to
specify them one by one. You can do this with bash:
$ dvc run $(for file in data/*.gz; do echo -n -d $file; done)
Be careful, though: if you declare checkpoint files to be an output of the DVC pipeline, you won’t be able to re-run the pipeline using those checkpoint files to initialize weights for model training. This would introduce circularity, as your output would become your input.
Also keep in mind that whenever you re-run a pipeline with
dvc repro, outputs
are deleted and then regenerated. If you don’t wish to automatically delete
outputs, there is a
--persist flag (see discussion
here), although we don’t
currently provide technical support for it.
Finally, remember that setting something as a dependency (
-d) doesn’t mean it
is automatically tracked by DVC. So remember to
dvc add data files in the
This is absolutely possible, and you don’t need any external software to safely
use multiple DVC repos in parallel. With DVC, cache operations are atomic. The
only exception is cleaning the cache with
dvc gc, which you should only run
when no one else is working on a shared project that is referenced in your cache
(and also, be sure to use the
as described in our docs). For more
about using multiple DVC repos in parallel, check out some discussions
Using DVC for version control is entirely compatible with using remote computing resources, like high performance computing (HPC), in your model training pipeline. We think a great example of using DVC with parallel computing is provided by Peter Fogh Take a look at his repo for a detailed use case. Please keep us posted about how HPC works in your pipeline, as we’ll be eager to pass on any insights to the community.
Absolutely, DVC supports pulling data from different DVC-files. An example would
be having two project subdirectories in your Git repo,
detection. You could use
dvc pull -R classification to only pull files in
that project to your workspace.
If you prefer to be even more granular, you can
dvc add files individually.
Then you can use
dvc pull <filename>.dvc to retrieve the outputs specified
only by that file.
dvc pullto fetch the dataset.
Yes, and we love the idea of publicly hosting a dataset. There are a few ways to
do it with DVC. We use one method in our own DVC project repository on Github.
If you run
git clone https://github.com/iterative/dvc and then
you’ll see that DVC is downloading data from an HTTP repository, which is
actually just an S3 repository that we’ve granted public HTTP read-access to.
So you would need to configure two remotes in your config file, each pointing to the same S3 bucket through different protocols. Like this:
$ dvc remote add -d --local myremote s3://bucket/path $ dvc remote add -d mypublicemote http://s3-external-1.amazonaws.com/bucket/path
Here’s why this works: the
-d flag sets the default remote, and the
flag creates a set of configuration preferences that will override the global
settings when DVC commands are run locally and won’t be shared through Git (you
can read more about this
in our docs).
This means that even though you and users from the public are accessing the
stored dataset by different protocols (S3 and HTTPS), you’ll all run the same