Step #1: Classification Training

Train an AI Model for Image Classification of Online Product (VMware vSphere) (Latest Version)

The image classification lab will use four important links from the left-hand navigation pane throughout the course of the lab. The Openshift Grafana dashboard is used to visualize GPU metrics like GPU utilization during model training.

image-classification-nav.png

In this portion of the lab, the machine learning workflow is explored:

  • Examine and understand data.

  • Build an input pipeline.

  • Build the model.

  • Train the model.

  • Test the model.

  • Save the model into Triton Inference Server format.

Open and run through the image classification training Jupyter Notebook with image classification to train a MobileNet model on the Stanford Online Products dataset to get started.

Note

To run a cell on the Jupyer Notebook, click on the cell you want to run and press enter Shift + Enter. Linux bash commands can be run inside the Jupyter Notebook by adding a bang symbol (!) before the command inside the Jupyter Notebook cell.

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