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Quickstarts
Scikit

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Intro

By the end of this guide you’ll have an API endpoint that can handle any scale of traffic by running inference on serverless CPU’s/GPU’s.

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Project set up

Before building you need to set up a Cerebrium account. This is as simple as starting a new Project in Cerebrium and copying the API key. This will be used to authenticate all calls for this project.

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Create a project

  1. Go to dashboard.cerebrium.ai
  2. Sign up or Login
  3. Navigate to the API Keys page
  4. You should see an API key with the source “Cerebrium”. Click the eye icon to display it. It will be in the format: c_api_key-xxx

API Key

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Develop model

Now navigate to where your model code is stored. This could be in a notebook or in a plain .py file.

To start you should install the Cerebrium framework running the following command in your notebook or terminal

pip install cerebrium

Copy and paste our code below. This creates a simple random forest classifier on the Iris dataset. This code could be replaced by any Sklearn model. Make sure you have the required libraries installed.

from sklearn.datasets import load_iris
from sklearn.ensemble import RandomForestClassifier
import pickle

iris = load_iris()
X, y = iris.data, iris.target

rf = RandomForestClassifier()
rf.fit(X, y)

# Save to pickle
with open("iris.pkl", "wb") as f:
    pickle.dump(rf, f)

In the last line of code, you will see we pickle the file. This is all you need to deploy your model to Cerebrium! You can then import the deploy() function from the Cerebrium framework.

from cerebrium import deploy, model_type

endpoint = endpoint = deploy((model_type.SKLEARN_CLASSIFIER, "iris.pkl"), "sk-test-model" , "<API_KEY>")

Deployed Model

Your result format will change for the _classifier model types. The sklearn_classifier will return a result object containing the probability distribution for the predicted output classes, rather than the argmax of the distribution. This is to allow you flexibility in how you want to handle the output of your model for classification. For example, you may want to return the top 3 predictions for your model, or you may want to return the top 3 predictions with a minimum probability threshold. This is up to you.

Your model is now deployed and ready for inference all in under 10 seconds! Navigate to the dashboard and on the Models page you will see your model.

You can run inference using curl

curl --location --request POST '<ENDPOINT>' \
--header 'Authorization: <API_KEY>' \
--header 'Content-Type: application/json' \
--data-raw '[[5.1, 3.5, 1.4, 0.2]]'

and your response should be:

Scikit Postman Response

Navigate back to the dashboard and click on the name of the model you just deployed. You will see an API call was made and the inference time. From your dashboard you can monitor your model, roll back to previous versions and see traffic.

Scikit Monitoring

With one line of code, your model was deployed in seconds with automatic versioning, monitoring and the ability to scale based on traffic spikes. Try deploying your own model now or check out our other frameworks.