Creating a basic repository for your artifacts

In this tutorial, we will demonstrate how you can create a repository and log artifacts.

A repository is an organized structure that stores and versions your artifacts. It makes it easy to retrieve older versions of artifacts, log new ones or time travel.

import tempfile
from pathlib import Path

from sklearn.datasets import make_classification
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC

from lazyscribe import Repository

First, we’re going to train version 0 of our model that we want to deploy

X, y = make_classification(n_samples=1000, n_features=10)
model = SVC(kernel="linear")
model.fit(X, y)

SVC(kernel='linear')

In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.

Next, we initialize our repository. Here, we’re using a temporary path, but you can use a local path or an external filesystem URI as well.

tmpdir = Path(tempfile.mkdtemp())
repository = Repository(tmpdir / "repository.json", mode="w")

Let’s log version 0 of our model to our repository. Remember to save! Nothing is actually written to file until you save.

repository.log_artifact("model", model, handler="joblib")
repository.save()

If we want to retrieve our artifact, we need to initialize a read-only instance of Repository.

repository_read = Repository(tmpdir / "repository.json", mode="r")
repository_read.load_artifact("model")

SVC(kernel='linear')

In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.

Say we have trained a newer, better version of our model. We can log it as version 1 of our model artifact. Remember to save!

modelv1 = LogisticRegression()
modelv1.fit(X, y)
repository.log_artifact("model", modelv1, handler="joblib")
repository.save()

Now, if we load our model artifact, it defaults to the most recent.

repository_read = Repository(tmpdir / "repository.json", mode="r")
repository_read.load_artifact("model")

LogisticRegression()

In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.

We can still time travel back to version 0 of our model artifact by specifying either the integer version, the datetime it was created, or a string version of the datetime.

repository_read.load_artifact("model", version=0)

SVC(kernel='linear')

In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.