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XGBoost template

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On this page we will show you the following:

How to create a deployment that uses a built xgboost model to make predictions on the prices of houses based on some criteria about the house. you can also download this page as a notebook and run it yourself. This example uses the House Sales in King County, USA Dataset. Link to the dataset

If you download and run this entire notebook after filling in your access token, the xgboost deployment will be deployed to your UbiOps environment. You can thus check your environment after running to explore. You can also check the individual steps in this notebook to see what we did exactly and how you can adapt it to your own use case.

We recommend to run the cells step by step, as some cells can take a few minutes to finish. You can run everything in one go as well and it will work, just allow a few minutes for building the individual deployments.

Establishing a connection with your UbiOps environment

Add your API token and your project name. We provide a deployment name and deployment version name. Afterwards we initialize the client library. This way we can deploy the XGBoost model to your environment.


DEPLOYMENT_NAME = 'xgboost-deployment'

# Import all necessary libraries
import shutil
import os
import ubiops

client = ubiops.ApiClient(ubiops.Configuration(api_key={'Authorization': API_TOKEN}, 
api = ubiops.CoreApi(client)

# This will create a new local folder to use for deployment files later

Creating the model

This example will be based on this kaggle about making predictions with XGboost and Linear Regression.

In this document we focus on deploying the model to UbiOps rather than on developing a model. Without elaborating much, we train a simple XGboost model and save the resulting file to our deployment package directory.

After running this cell you should see a comparision between the sklearn model and the xgboost model regarding the accuracy score and the RMSE (Root Mean Square Error)

Let us first install the python packages we will need for our model

!pip install sklearn
!pip install xgboost
!pip install numpy
!pip install pandas
!pip install joblib
import numpy as np
import pandas as pd
import xgboost
import math
from scipy.stats import pearsonr
from sklearn.linear_model import LinearRegression
from sklearn import tree, linear_model
from sklearn.model_selection import train_test_split
from sklearn.metrics import explained_variance_score
import joblib

# Read the data into a data frame
data = pd.read_csv('')

# Train a simple linear regression model
regr = linear_model.LinearRegression()
new_data = data[['sqft_living','grade', 'sqft_above', 'sqft_living15','bathrooms','view','sqft_basement','lat','waterfront','yr_built','bedrooms']]

X = new_data.values
y = data.price.values

# Create train test sets
X_train, X_test, y_train, y_test = train_test_split(X, y ,test_size=0.2)
# Train the model, y_train)

# Check how the sklearn model scores on accuracy on our test set
sklearn_score = regr.score(X_test,y_test)
# Print the score of the sklearn model (Not great)
print(f'Score of the sklearn model: {sklearn_score}')

# Calculate the Root Mean Squared Error
print("RMSE of the sklearn model: %.2f"
      % math.sqrt(np.mean((regr.predict(X_test) - y_test) ** 2)))

# Let's try XGboost algorithm to see if we can get better results
xgb = xgboost.XGBRegressor(n_estimators=100, learning_rate=0.08, gamma=0, subsample=0.75,
                           colsample_bytree=1, max_depth=7)

traindf, testdf = train_test_split(X_train, test_size = 0.2)
# Train the model,y_train)

# Make predictions using the xgboost model
predictions = xgb.predict(X_test)

# Check how the xgboost model scores on accuracy on our test set
xgboost_score = explained_variance_score(predictions,y_test)

print(f'Score of the xgboost model {xgboost_score}')

# Calculate the Root Mean Squared Error
print("RMSE of the xgboost model: %.2f"
      % math.sqrt(np.mean((predictions - y_test) ** 2)))

# Save the model to our empty deployment package directory
joblib.dump(xgb, 'xgboost-deployment/xgboost_model.joblib') 
print('XGBoost model built and saved successfully!')

Creating the XGboost deployment

Now that we have saved our model it is time to create a deployment in UbiOps that will make use of it.

In the cell below you can view the which will take data about the house we wish to predict the price of. As you can see in the initialization step we load the model we created earlier, then in the request method we make use of it to make a prediction. The input to this model is data: a csv file with the house data to predict its price.

%%writefile xgboost-deployment/
The file containing the deployment code is required to be called '' and should contain the 'Deployment'
class and 'request' method.

import pandas as pd
import numpy as np
import os
from joblib import load

class Deployment:

    def __init__(self, base_directory, context):
        Initialisation method for the deployment. It can for example be used for loading modules that have to be kept in
        memory or setting up connections. Load your external model files (such as pickles or .h5 files) here.

        print("Initialising xgboost model")

        XGBOOST_MODEL = os.path.join(base_directory, "xgboost_model.joblib")
        self.model = load(XGBOOST_MODEL)

    def request(self, data):
        Method for deployment requests, called separately for each individual request.
        print('Loading data')
        input_data = pd.read_csv(data['data'])

        print("Prediction being made")
        prediction = self.model.predict(input_data.values)

        # Writing the prediction to a csv for further use
        print('Writing prediction to csv')
        pd.DataFrame(prediction).to_csv('prediction.csv', header = ['house_prices'], index_label= 'index')

        return {
            "prediction": 'prediction.csv'
%%writefile xgboost-deployment/requirements.txt


Deploying to UbiOps

Now we have all the pieces we need to create our deployment on UbiOps. In the cell below a deployment is being created, then a version of the deployment is created and the deployment code is zipped and uploaded to that version.

# Create the deployment
deployment_template = ubiops.DeploymentCreate(
    description='XGBoost deployment',
        {'name':'data', 'data_type':'file'},
        {'name':'prediction', 'data_type':'file'}
    labels={'demo': 'xgboost'}


# Create the version
version_template = ubiops.DeploymentVersionCreate(
    maximum_idle_time=1800, # = 30 minutes
    request_retention_mode='none' # we don't need request storage in this example


# Zip the deployment package
shutil.make_archive('xgboost-deployment', 'zip', '.', 'xgboost-deployment')

# Upload the zipped deployment package
file_upload_result =api.revisions_file_upload(

All done! Let's close the client properly.

Note: The notebook shown on this page runs on Python 3.9 and uses UbiOps CLient Library 3.15.0.

Making a request and exploring further

You can go ahead to the Web App and take a look in the user interface at what you have just built. If you want you can create a request to the XGboost deployment using the "dummy_data_to_predict.csv". The dummy data is a small test subset from the original data.

So there we have it! We have created a deployment and using the XGboost library. You can use this notebook to base your own deployments on. Just adapt the code in the deployment packages and alter the input and output fields as you wish and you should be good to go.

For any questions, feel free to reach out to us via the customer service portal: