mnist

Kubeflow Fairing E2E MNIST Case: Building, Training and Serving

You may see more details in the Kubeflow examples mnist. The difference is that the example end-to-end takes Kubeflow Fairing to build docker image and launch TFJob for distributed training, and then create a InferenceService (KFServing CRD) to deploy model service.

This example guides you through:

• Taking an example TensorFlow model and modifying it to support distributed training.
• Using Kubeflow Fairing to build docker image and launch a TFJob to train model.
• Using Kubeflow Fairing to create InferenceService (KFServing CR) to deploy the trained model.
• Cleaning up the TFJob and InferenceService using kubeflow-tfjob and kfserving SDK client.

Steps

1. Launch a Jupyter notebook

2. Open the notebook mnist_e2e_on_prem.ipynb

3. Follow the notebook to train and deploy MNIST on Kubeflow.