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Answer: Train the model using a VM with a GPU hardware accelerator.
The best way to reduce the TensorFlow training time in a cost-effective manner is to use a VM with a GPU hardware accelerator. TensorFlow can take advantage of GPUs to significantly speed up training time for many models. GPUs are a cost-effective choice compared to TPUs, which are more expensive and may not support custom TensorFlow operations that must run partially on CPUs. Therefore, option C is the best choice.
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Given a scenario where you have a TensorFlow machine learning model executing on Compute Engine virtual machines (n2-standard-32), it currently takes two days to complete the training process. The model includes custom TensorFlow operations that require partial execution on a CPU. Your objective is to cut down the training duration while maintaining cost efficiency. How should you proceed?
A
Change the VM type to n2-highmem-32.
B
Change the VM type to e2-standard-32.
C
Train the model using a VM with a GPU hardware accelerator.
D
Train the model using a VM with a TPU hardware accelerator.