github_issue_summarization

(Deprecated) End-to-End kubeflow tutorial using a Sequence-to-Sequence model

Note: This example does not currently work correctly, and has been deprecated. It will be updated or replaced soon.

This example demonstrates how you can use kubeflow end-to-end to train and serve a Sequence-to-Sequence model on an existing kubernetes cluster. This tutorial is based upon @hamelsmu's article "How To Create Data Products That Are Magical Using Sequence-to-Sequence Models".

Goals

There are two primary goals for this tutorial:

• Demonstrate an End-to-End kubeflow example
• Present an End-to-End Sequence-to-Sequence model

By the end of this tutorial, you should learn how to:

• Setup a Kubeflow cluster on an existing Kubernetes deployment
• Spawn a Jupyter Notebook on the cluster
• Spawn a shared-persistent storage across the cluster to store large datasets
• Train a Sequence-to-Sequence model using TensorFlow and GPUs on the cluster
• Serve the model using Seldon Core
• Query the model from a simple front-end application

Steps:

1. Setup a Kubeflow cluster
2. Training the model. You can train the model using any of the following methods using Jupyter Notebook or using TFJob:
   • Training the model using a Jupyter Notebook
   • Training the model using TFJob
   • Distributed Training using estimator and TFJob
3. Serving the model
4. Querying the model
5. Teardown