Google Professional Machine Learning Engineer
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You are tasked with training a custom language model for your company using a large dataset. To handle the computational load effectively, you decide to employ the Reduction Server strategy on Google's Vertex AI, which helps optimize bandwidth and latency for multi-node distributed training. You need to configure the worker pools for this distributed training job on Vertex AI. What configuration should you choose for the worker pools to ensure optimal performance?
A
Configure the machines of the first two worker pools to have GPUs, and to use a container image where your training code runs. Configure the third worker pool to have GPUs, and use the reductionserver container image.
B
Configure the machines of the first two worker pools to have GPUs and to use a container image where your training code runs. Configure the third worker pool to use the reductionserver container image without accelerators, and choose a machine type that prioritizes bandwidth.
C
Configure the machines of the first two worker pools to have TPUs and to use a container image where your training code runs. Configure the third worker pool without accelerators, and use the reductionserver container image without accelerators, and choose a machine type that prioritizes bandwidth.
D
Configure the machines of the first two pools to have TPUs, and to use a container image where your training code runs. Configure the third pool to have TPUs, and use the reductionserver container image.