Step #1: Classification Training
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.
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.
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.