Train and Deploy on GCP from a Kubeflow Notebook

This guide introduces you to using Kubeflow Fairing to train and deploy a model to Kubeflow on Google Kubernetes Engine (GKE) and Google AI Platform Training.

Your Kubeflow deployment includes services for spawning and managing Jupyter notebooks. Kubeflow Fairing is preinstalled in the Kubeflow notebooks, along with a number of machine learning (ML) libraries.

Set up Kubeflow and access the Kubeflow notebook environment

Follow the Kubeflow notebook setup guide to install Kubeflow, access your Kubeflow hosted notebook environment, and create a new notebook server.

When selecting a Docker image and other settings for the baseline deployment of your notebook server, you can leave all the settings at the default value.

Run the example notebook

As an example, this guide uses a notebook that is hosted on Kubeflow to demonstrate how to:

• Train an XGBoost model in a notebook,
• Use Kubeflow Fairing to train an XGBoost model remotely on Kubeflow,
• Use Kubeflow Fairing to train an XGBoost model remotely on AI Platform Training,
• Use Kubeflow Fairing to deploy a trained model to Kubeflow, and
• Call the deployed endpoint for predictions.

Follow these instructions to run the XGBoost quickstart notebook:

1. Download the files used in this example and install the packages that the XGBoost quickstart notebook depends on.

   1. On the Jupyter dashboard for your notebook server, click New and select Terminal to start a new terminal session in your notebook environment. Use the terminal session to set up your notebook environment to run this example.

   2. Clone the Kubeflow Fairing repository to download the files used in this example.

      git clone https://github.com/kubeflow/fairing 
      

   3. Install the Python dependencies for the XGBoost quickstart notebook.

      pip3 install -r fairing/examples/prediction/requirements.txt
      

2. Use the notebook user interface to open the XGBoost quickstart notebook at fairing/examples/prediction/xgboost-high-level-apis.ipynb.

3. Follow the instructions in the notebook to:

   1. Train an XGBoost model in a notebook,
   2. Use Kubeflow Fairing to train an XGBoost model remotely on Kubeflow,
   3. Use Kubeflow Fairing to train an XGBoost model remotely on AI Platform Training,
   4. Use Kubeflow Fairing to deploy a trained model to Kubeflow, and
   5. Call the deployed endpoint for predictions.

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