- Basic Tf Serving The SageMaker TensorFlow Serving Container makes it easy to deploy trained TensorFlow models to a SageMaker Endpoint without the need for any custom model loading or inference code. The code shows how deploy one or more pre-trained models from TensorFlow Hub to a SageMaker Endpoint using the SageMaker Python SDK, and then use the model(s) to perform inference requests.
- Tf Serving with Elastic Inference This folder has code where the main objective is to show how to create an endpoint, backed by an Elastic Inference, to serve our pre-trained TensorFlow Serving model for predictions. With a more efficient cost per performance, Amazon Elastic Inference can prove to be useful for those looking to use GPUs for higher inference performance at a lower cost.
tf-serving
Folders and files
| Name | Name | Last commit date | ||
|---|---|---|---|---|
parent directory.. | ||||