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Question 152 - Professional Machine Learning Engineer discussion

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You work with a team of researchers to develop state-of-the-art algorithms for financial analysis. Your team develops and debugs complex models in TensorFlow. You want to maintain the ease of debugging while also reducing the model training time. How should you set up your training environment?

A.
Configure a v3-8 TPU VM SSH into the VM to tram and debug the model.
Answers
A.
Configure a v3-8 TPU VM SSH into the VM to tram and debug the model.
B.
Configure a v3-8 TPU node Use Cloud Shell to SSH into the Host VM to train and debug the model.
Answers
B.
Configure a v3-8 TPU node Use Cloud Shell to SSH into the Host VM to train and debug the model.
C.
Configure a M-standard-4 VM with 4 NVIDIA P100 GPUs SSH into the VM and use Parameter Server Strategy to train the model.
Answers
C.
Configure a M-standard-4 VM with 4 NVIDIA P100 GPUs SSH into the VM and use Parameter Server Strategy to train the model.
D.
Configure a M-standard-4 VM with 4 NVIDIA P100 GPUs SSH into the VM and use MultiWorkerMirroredStrategy to train the model.
Answers
D.
Configure a M-standard-4 VM with 4 NVIDIA P100 GPUs SSH into the VM and use MultiWorkerMirroredStrategy to train the model.
Suggested answer: A

Explanation:

A TPU VM is a virtual machine that has direct access to a Cloud TPU device. TPU VMs provide a simpler and more flexible way to use Cloud TPUs, as they eliminate the need for a separate host VM and network setup. TPU VMs also support interactive debugging tools such as TensorFlow Debugger (tfdbg) and Python Debugger (pdb), which can help researchers develop and troubleshoot complex models. A v3-8 TPU VM has 8 TPU cores, which can provide high performance and scalability for training large models. SSHing into the TPU VM allows the user to run and debug the TensorFlow code directly on the TPU device, without any network overhead or data transfer issues.Reference:

1: TPU VMs Overview

2: TPU VMs Quickstart

3: Debugging TensorFlow Models on Cloud TPUs

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asked 18/09/2024
Alexander Yakovenko
34 questions
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