Professional Machine Learning Engineer Exam - Question 50

Question

Your team is building a convolutional neural network (CNN)-based architecture from scratch. The preliminary experiments running on your on-premises CPU-only infrastructure were encouraging, but have slow convergence. You have been asked to speed up model training to reduce time-to-market. You want to experiment with virtual machines (VMs) on Google Cloud to leverage more powerful hardware. Your code does not include any manual device placement and has not been wrapped in Estimator model-level abstraction. Which environment should you train your model on?

Examice · Accepted Answer

To speed up model training for a convolutional neural network (CNN) and to avoid manual setup, a Deep Learning VM with pre-installed libraries and a GPU is the ideal choice. This environment allows the use of powerful GPU hardware, which is critical for efficient training of CNN models, and comes with all necessary dependencies pre-installed, reducing the overhead associated with manual installations. The n1-standard-2 machine with one GPU provides a good balance for such preliminary experiments, offering substantial computational power without the complexity of manual device placement.

celia20200410 · Answer

ANS: C

to support CNN, you should use GPU. 
for preliminary experiment, pre-installed pkgs/libs are good choice.

https://cloud.google.com/deep-learning-vm/docs/cli#creating_an_instance_with_one_or_more_gpus
https://cloud.google.com/deep-learning-vm/docs/introduction#pre-installed_packages

Paul_Dirac · Answer

Code without manual device placement => default to CPU if TPU is present or to the lowest order GPU if multiple GPUs are present. => Not A, B.
D: already using CPU and needing GPU for CNN.
Ans: C

suresh_vn · Answer

"has not been wrapped in Estimator model-level abstraction"
How you can use GPU?
D in my opinion, E-family using for high CPU tasks

shankalman717 · Answer

Critical sentece: Your code does not include any manual device placement and has not been wrapped in Estimator model-level abstraction.

So only answer we have. it's D.

BenMS · Answer

Critical sentence: Your code does not include any manual device placement and has not been wrapped in Estimator model-level abstraction.

So only answer we have. it's D.

Sum_Sum · Answer

Agree with  celia20200410 - C

NamitSehgal · Answer

C is correct

mmona19 · Answer

the question is asking speed up time to market which can happen if model trains fast. so TPU VM can be a solution. https://cloud.google.com/blog/products/compute/introducing-cloud-tpu-vms option A. if question asks most managed way than answer is deep learning container with everything installed. C

Mohamed_Mossad · Answer

Answer C
========
Explanation
"speed up model training" will make us biased towards GPU,TPU options
by options eliminations we may need to stay away of any manual installations , so using preconfigered deep learning will speed up time to market

ares81 · Answer

It's C.

SergioRubiano · Answer

You should use GPU.

Melampos · Answer

thinking in fastest way

M25 · Answer

Went with C

Liting · Answer

Should use the deep learning VM with GPU. 
TPU should be selected only if necessary, coz it incurs high cost. GPU in this case is enough.

Mickey321 · Answer

keyword: Your code does not include any manual device placement and has not been wrapped in Estimator model-level abstraction.

gscharly · Answer

Agree with celia20200410 - C

PhilipKoku · Answer

C) GPU and all pre-installed libraries.

Professional Machine Learning Engineer Exam - Question 50

Discussion