Loading a linear learner model with Apache MXNet in Python
In the previous recipe, we ran a training job using the SageMaker Python SDK. In this recipe, we will use Apache MXNet and Gluon to load the model, extract its parameters, and perform predictions locally. If you are wondering what Gluon is and how it differs from Apache MXNet, Gluon is a high-level API for deep learning, while Apache MXNet is the deep learning framework usually categorized with TensorFlow and PyTorch:
Figure 1.40 – Using Apache MXNet to load the model and extract the parameters of the model
That said, the objective of this recipe is to show that the model file uploaded to the Amazon S3 bucket after the training step can be loaded and analyzed using Apache MXNet, as shown in Figure 1.40:
Getting ready
Here are the prerequisites for this recipe:
- This recipe continues on from Training your first model in Python. Make sure that you have completed the steps in that...
