Get Started: Data and Model Access
We've learned how to track data and models with DVC, and how to commit their versions to Git. The next questions are: How can we use these artifacts outside of the project? How do we download a model to deploy it? How to download a specific version of a model? Or reuse datasets across different projects?
These questions tend to come up when you browse the files that DVC saves to
remote storage (e.g.
s3://dvc-public/remote/get-started/fb/89904ef053f04d64eafcc3d70db673
๐ฑ
instead of the original file name such as model.pkl
or data.xml
).
Remember those .dvc
files dvc add
generates? Those files (and dvc.lock
,
which we'll cover later) have their history in Git. DVC's remote storage config
is also saved in Git, and contains all the information needed to access and
download any version of datasets, files, and models. It means that a Git
repository with DVC files becomes an entry point, and can be used
instead of accessing files directly.
Find a file or directory
You can use dvc list
to explore a DVC repository hosted on any
Git server. For example, let's see what's in the get-started/
directory of our
dataset-registry repo:
$ dvc list https://github.com/iterative/dataset-registry get-started
.gitignore
data.xml
data.xml.dvc
The benefit of this command over browsing a Git hosting website is that the list
includes files and directories tracked by both Git and DVC (data.xml
is not
visible if you
check GitHub).
Download
One way is to simply download the data with dvc get
. This is useful when
working outside of a DVC project environment, for example in an
automated ML model deployment task:
$ dvc get https://github.com/iterative/dataset-registry \
use-cases/cats-dogs
When working inside another DVC project though, this is not the best strategy because the connection between the projects is lost โ others won't know where the data came from or whether new versions are available.
Import file or directory
dvc import
also downloads any file or directory, while also creating a .dvc
file (which can be saved in the project):
$ dvc import https://github.com/iterative/dataset-registry \
get-started/data.xml -o data/data.xml
This is similar to dvc get
+ dvc add
, but the resulting .dvc
files
includes metadata to track changes in the source repository. This allows you to
bring in changes from the data source later using dvc update
.
Note that the dataset registry repository doesn't actually contain a
get-started/data.xml
file. Likedvc get
,dvc import
downloads from remote storage.
.dvc
files created by dvc import
have special fields, such as the data
source repo
and path
(under deps
):
+deps:
+- path: get-started/data.xml
+ repo:
+ url: https://github.com/iterative/dataset-registry
+ rev_lock: 96fdd8f12c14fa58a1b7354f15c7adb50e4e8542
outs:
- md5: 22a1a2931c8370d3aeedd7183606fd7f
path: data.xml
The url
and rev_lock
subfields under repo
are used to save the origin and
version of the dependency, respectively.
Python API
It's also possible to integrate your data or models directly in source code with DVC's Python API. This lets you access the data contents directly from within an application at runtime. For example:
import dvc.api
with dvc.api.open(
'get-started/data.xml',
repo='https://github.com/iterative/dataset-registry'
) as f:
# f is a file-like object which can be processed normally