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Hello,
The size of your model should be a factor in selecting an instance. If your model exceeds an instance's available RAM, select a different instance type with enough memory for your application.
Amazon EC2 P3 Instances have up to 8 NVIDIA Tesla V100 GPUs.
Amazon EC2 P4 Instances have up to 8 NVIDIA Tesla A100 GPUs.
Amazon EC2 G3 Instances have up to 4 NVIDIA Tesla M60 GPUs.
Amazon EC2 G4 Instances have up to 4 NVIDIA T4 GPUs.
Amazon EC2 G5 Instances have up to 8 NVIDIA A10G GPUs.
Amazon EC2 G5g Instances have Arm-based AWS Graviton2 processors
Refer this link- https://docs.aws.amazon.com/dlami/latest/devguide/gpu.html for more details on the selection of instances.
Hope this helps, If yes, please click accepted answer.
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Yes, the size of the model is one thing - mine is pretty large (5 MByte), but the data set can be 1000's of times larger than that. So holding batches in memory of the GPU is critical. Hence I chose p3.8xlarge, which is the smallest I can get away with. But the question wasn't about that, it was what AMI instance to choose. I listed three that were no use and was asking the community what they have found to work well with tensorflow, good utilization of GPUs, and potentially distributed training.