AWS Batch facilitates parallel processing of large datasets, optimizing resource utilization and reducing processing time. This is particularly advantageous for training deep learning and machine learning models, where large datasets can significantly impact training times. The primary challenge lies in processing substantial data simultaneously due to memory constraints.
In this Cloud Lab, you’ll train a model for predicting house prices and observe how training this model sequentially and in parallel affects the training time and performance. You’ll utilize AWS Batch for the model training by creating a Docker image of your training jobs and storing them in an ECR repository. You will then use this Docker image to create the job definition for the training jobs. Finally, you will create the jobs to train your model both sequentially and using parallel computing.
After completing this Cloud Lab, you’ll be able to train your machine learning models by using AWS Batch and reduce the training time of your models. Below is the high-level architecture diagram for this Cloud Lab:
