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Exam Professional Machine Learning Engineer All Questions

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Exam Professional Machine Learning Engineer topic 1 question 209 discussion

Actual exam question from Google's Professional Machine Learning Engineer
Question #: 209
Topic #: 1
[All Professional Machine Learning Engineer Questions]

You are training a custom language model for your company using a large dataset. You plan to use the Reduction Server strategy on Vertex AI. You need to configure the worker pools of the distributed training job. What should you do?

  • 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.
Show Suggested Answer Hide Answer
Suggested Answer: B 🗳️
by pikachu007 at Jan. 13, 2024, 5:32 a.m.

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wences
2 months ago
Selected Answer: B
The real reason for answer B is the custom model, which means it was not suited well for TPU
upvoted 1 times
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fitri001
7 months, 1 week ago
Selected Answer: B
GPUs for Training: Configure the first two worker pools with GPUs to leverage the hardware acceleration capabilities for your custom language model training code. Reduction Server without GPUs: The third worker pool should use the reductionserver container image. This image is pre-configured for Reduction Server functionality and doesn't require GPUs. High-Bandwidth CPU: Choose a machine type with high bandwidth for the third pool since Reduction Server focuses on communication and gradient reduction.
upvoted 2 times
fitri001
7 months, 1 week ago
A. GPUs for Reduction Server: Reduction Server itself doesn't require or benefit from GPUs. It focuses on communication and reduction of gradients. It's better to use a CPU-based machine type for the third pool. C. TPUs instead of GPUs: While TPUs can be used for training some language models, Reduction Server specifically works with GPUs using the NCCL library. Configure your first two pools with GPUs for your training code. D. TPUs in Reduction Server pool: Similar to option A, Reduction Server doesn't benefit from TPUs. It's best to use a CPU with high bandwidth for the third pool.
upvoted 1 times
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pinimichele01
7 months, 3 weeks ago
Selected Answer: B
https://cloud.google.com/blog/topics/developers-practitioners/optimize-training-performance-reduction-server-vertex-ai In this article, we introduce Reduction Server, a new Vertex AI feature that optimizes bandwidth and latency of multi-node distributed training on NVIDIA GPUs for synchronous data parallel algorithms.
upvoted 1 times
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shadz10
10 months, 2 weeks ago
Selected Answer: B
TPUs are not supported for reductionserver so B
upvoted 3 times
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winston9
10 months, 2 weeks ago
Selected Answer: B
bandwidth is important for the reduction server
upvoted 2 times
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pikachu007
10 months, 2 weeks ago
Selected Answer: B
Worker Pools 1 and 2: These pools are responsible for the actual model training tasks. They require GPUs (or TPUs, if applicable to your model) to accelerate model computations. They run the container image containing your training code. Worker Pool 3: This pool is dedicated to the reduction server. It doesn't require accelerators (GPUs or TPUs) for gradient aggregation. Prioritize machines with high network bandwidth to optimize gradient exchange. Use the specific reductionserver
upvoted 2 times
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