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Training with Python notebooks

Training jobs create and store models in the model registry. You can create new training jobs from a Python notebook using the add_job API.

A simple training job​

Before beginning, make sure your notebook is logged into Modelbit.

In your Python notebook, define a function that trains and stores a model. In this example we'll hard-code the training data to keep things simple:

import pandas as pd
from sklearn.linear_model import LinearRegression

def train_my_predictor():

    # Typically these DataFrames would come from Modelbit datasets
    X_train = pd.DataFrame({"feature_one": [1, 2, 3], "feature_two": [2, 4, 6]})
    y_train = pd.DataFrame({"result": [3, 6, 9]})

    # Our model training code
    regression = LinearRegression().fit(X_train, y_train)

    # Store the trained model in the registry named "my_predictor"
    mb.add_model("my_predictor", regression)

# Call your training function to check that it works
train_my_predictor()

This training function uses add_model to store the trained regression in the model registry.

Create the training job​

That training function is all you need to create a training job in Modelbit. Call add_job to turn that function (and its dependencies) into a training job in Modelbit:

mb.add_job(train_my_predictor)

A link to your training job will appear. Click that to view your training job in Modelbit.

Running your training job​

tip

See examples of parameterized runs, instance sizes, and jobs with dependencies in the run_job API reference.

There are two ways to start a job.

  • In the web app: Within your training job detail screen, click the Run Job button. That'll open a form for you enter some specifics, like the size of the machine. Click Run Job at the bottom of the form to begin executing your job.
  • Python API: You can start a job using run_job. For this job, you'd run mb.run_job("train_my_predictor")

Using trained models in deployments​

To use my_predictor in a deployment, retrieve it with get_model:

# Example deployment function
def make_predictions(a: int, b: int):
    regression = mb.get_model("my_predictor")
    return regression.predict([[a, b]])[0][0]

mb.deploy(make_predictions)