Version Model

Created a model? No? Create one now. Yes? Here’s what you should do next.

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Register Models

PureML assists you with creating, training, and tracking all of your machine learning project information, including ML models and datasets, using semantic versioning and full artifact logging.

This short tutorial will show you how to register and track machine learning models using PureML.

pip install pureml
pip install xgboost

from sklearn.datasets import load_iris
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
import xgboost as xgb
import pandas as pd
import pureml
from pureml.decorators.model import model

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Train and Register your Model Version

We will train a XGBClassifier to predict the classes of samples from Iris dataset in this tutorial. This is a straightforward training function that returns the trained model and prints the accuracy of our model. We will utilize the Iris data from sklearn.

X, y = load_iris(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)

def train():
  xgb_model = xgb.XGBClassifier()
  xgb_model.fit(X_train, y_train)
  predictions = xgb_model.predict(X_test)

  acc = accuracy(y_test, predictions)
  print("accuracy: ", acc)

  return xgb_model

xgb_model = train()

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Now let’s register our model to pureml

To register the model, add the @model decorator to the training function. PureML will register the returned trained model to your PureML project.

Let’s also replace print() with pureml.log() to register obtained model metrics to the project.

@model('iris_classifier:dev')
def train():
    xgb_model = xgb.XGBClassifier()
    xgb_model.fit(X_train, y_train)
    predictions = xgb_model.predict(X_test)

    acc = accuracy(y_test, predictions)

    pureml.log(metrics={'accuracy':acc})
    print("accuracy: ", acc)

    return xgb_model

xgb_model = train()

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Fetching a Model

Once you register your model to PureML, you can load the model from PureML using model module.

Let’s look at how to load and generate predictions with your model:

import pureml

model = pureml.model.fetch('iris_clasifier:dev')
X, y = load_iris(return_X_y=True)
preds = model.predict(X)

By default, fetch method fetches latest version of the model. A particular version of a model can be fetched by providing version parameter as the following.

model = pureml.model.fetch('iris_classifier:dev:v3')

Here, we have fetched the version v3 of the model iris_classifier.

print('Prediction: ', preds[0])

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Submit, approve and reject model version in review

By providing a comprehensive set of metrics and visualizations, PureML makes it easy to identify and correct any issues with its review feature and allows you to evaluate the quality of their data and the accuracy of their model. Owner can approve or reject to add in production.