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The Oxen python interface makes it easy to integrate Oxen datasets directly into machine learning dataloaders or other data pipelines.
There are two types of repositories one can interact with, a Repo
and a RemoteRepo
.
To fully clone all the data to your local machine, you can use the Repo
class.
import oxen
repo = oxen.Repo("path/to/repository")
repo.clone("https://hub.oxen.ai/ox/CatDogBBox")
If there is a specific version of your data you want to access, you can specify the branch
when cloning.
repo.clone("https://hub.oxen.ai/ox/CatDogBBox", branch="my-pets")
Once you have a repository locally, you can perform the same operations you might via the command line, through the python api.
For example, you can checkout a branch, add a file, commit, and push the data to the same remote you cloned it from.
import oxen
repo = oxen.Repo("path/to/repository")
repo.clone("https://hub.oxen.ai/ox/CatDogBBox")
repo.checkout()
If you don't want to download the data locally, you can use the RemoteRepo
class to interact with a remote repository on OxenHub.
import oxen
repo = RemoteRepo("https://hub.oxen.ai/ox/CatDogBBox")
To stage and commit files to a specific version of the data, you can checkout
an existing branch or create a new one.
repo.create_branch("dev")
repo.checkout("dev")
You can then stage files to the remote repository by specifying the file path and destination directory.
repo.add("new-cat.png", "images") # Stage to images/new-cat.png on remote
repo.commit("Adding another training image")
Note that no "push" command is required here, since the above code creates a commit directly on the remote branch.
FAQs
Data version control for machine learning
We found that oxenai demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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