Thank you for using AWS DLAMI.
Looking at the above query, I understand that while using DLAMI for TensorFlow 2.9.1 it was observed that GPU utilization was zero and it was using CPU after adding tensorflow-gpu explicitly.
From the complete scenario shared, I assume that the tensorflow-gpu is added but eventually it is using the tensorflow-cpu, but again due to limited visibility on your setup and code used I cannot be sure enough to claim that. To further understand the issue more in depth, I'd recommend you to reach to AWS Premium Support by creating a support case so that the engineer can investigate the root cause of the issue.
But prior to that, I'd highly recommend using DL container on SageMaker instead using DLAMI on EC2. Along with that you use SageMaker distributed training libraries which are available only through the AWS deep learning containers for the TensorFlow. Additionally SageMaker also provides Bring you container, where the users can built their own container as per their requirements for the use case.
If using SageMaker also does not help overcome the issue, then as mentioned above please reach out to AWS Premium Support so that we can better assist you and help you overcome the issue.
 TensorFlow with Horovod - https://docs.aws.amazon.com/dlami/latest/devguide/tutorial-horovod-tensorflow.html
 Amazon SageMaker Distributed Training Libraries - https://docs.aws.amazon.com/sagemaker/latest/dg/distributed-training.html
 Use TensorFlow with Amazon SageMaker - https://docs.aws.amazon.com/sagemaker/latest/dg/tf.html
 Open a support case with AWS using the link: https://console.aws.amazon.com/support/home?#/case/create
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