[Example Request] TensorFlow 2.9 with SM Training Compiler - Tuning Hyperparameters

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Describe the use case example you want to see

A notebook example describing how to tune hyper-parameters while training with SM Training Compiler on TensorFlow 2.9. SM Training Compiler recently announced support for SageMaker TensorFlow DLCs. This particular example will explore how to effectively use SM Training Compiler by tuning the hyper-parameters.

How would this example be used? Please describe.

Onboarding new Computer Vision customers to advanced use-cases with SM Training Compiler

Describe which SageMaker services are involved

1. SageMaker Training
2. SageMaker Training Compiler
3. SageMaker Model Tuner

Describe what other services (other than SageMaker) are involved*

None

Describe which dataset could be used. Provide its location in s3://sagemaker-sample-files or another source.

Caltech-256 from s3://sagemaker-sample-files/datasets/image/caltech-256

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ToDo: Rework final tuning to use a Validation Dataset