ML Models

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 Tensorflow, PyTorch, or Scikit-Learn models.

Here's a very simple example of how to push a model to dstack:

from sklearn.svm import SVC
from sklearn.multiclass import OneVsRestClassifier
from sklearn.preprocessing import LabelBinarizer
import dstack as ds
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X = [[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)
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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 ds
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X = [[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 dstack application:

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