Python Efficient Repeat Use of Model

After creating a model (especially a DL Python Model), that model will be used to make predictions given new input data. Sometimes the need to make such predictions can occur much later in a Workflow and in different Python nodes. Loading and reloading these models to make a prediction can become costly. This Workflow introduces a pattern for preserving a DL Python Model in memory such that it can be used to make predictions on-demand without needing to reload it each time. It consists of three reusable Components: 1. a Component to preserve the model in memory and make it possible to call the model's predict() method, on-demand; 2. a Component to call the model's predict() method; and 3. a Component to release the model from memory, once it is no longer needed anywhere in the Workflow. As a practical demo, this Workflow employs Keras+TensorFlow2's definition of resnet50 to recognize common objects depicted in one or more input images.

This is a companion discussion topic for the original entry at