dstack decouples the development of applications from the development of ML models by offering a built-in ML registry. This way, one can develop ML models, push them to the registry, and then pull these models later from applications.
dstack's ML Registry supports
Here's a very simple example of how to push a model to
from sklearn.svm import SVCfrom sklearn.multiclass import OneVsRestClassifierfrom sklearn.preprocessing import LabelBinarizerimport dstack as dsX = [[1, 2], [2, 4], [4, 5], [3, 2], [3, 1]]y = [0, 0, 1, 1, 2]classif = OneVsRestClassifier(estimator=SVC(random_state=0))classif.fit(X, y)url = ds.push("classif", classif)print(url)
Now, if you click the URL, it will open the following page:
Here, you can see a code snippet of how to pull the model from an application or from anywhere else:
import dstack as dsX = [[1, 2], [2, 4], [4, 5], [3, 2], [3, 1]]classif = ds.pull('/dstack/classif')classif.predict(X)
Also, here you can edit a
README.md file to provide documentation on how to use the model.
Check out the following tutorial that trains a more complex ML model and uses it in a