README.md

[WIP] End-to-End kubeflow tutorial using a Sequence-to-Sequence model

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:

• End-to-End kubeflow example
• 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 up a Jupyter Notebook on the cluster
• Spawn up a shared-persistent storage across the cluster to store large datasets
• Train a Sequence-to-Sequence model using TensorFlow on the cluster using GPUs
• 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 either using Jupyter Notebook or using TFJob.
   1. Training the model using a Jupyter Notebook
   2. Training the model using TFJob
   3. Distributed Training using tensor2tensor and TFJob
3. Serving the model
4. Querying the model
5. Teardown