Welcome to our end-to-end example of distributed image classification algorithm. In this demo, we will use the Amazon sagemaker image classification algorithm to train on the caltech-256 dataset. To get started, we need to set up the environment with a few prerequisite steps, for permissions, configurations, and so on. Here we set up the linkage and authentication to AWS services. Download the data and transfer to S3 for use in training.

We can also independently test the model performance on a test set using any of the trained model snapshots saved at the checkpoint location. If ever the model performance improves initially and then starts to flatten or decrease it might be a good idea to reduce the learning rate at that point while resuming training from where it left off. All one would need to do is use the model snapshot from the 15th epoch, which would be a minor change to the code above.