ExamTopics Logo
Mail Us[email protected]
Menu
Google Discussions
exam questions

Exam Professional Machine Learning Engineer All Questions

View all questions & answers for the Professional Machine Learning Engineer exam
Go to Exam

Exam Professional Machine Learning Engineer topic 1 question 126 discussion

Actual exam question from Google's Professional Machine Learning Engineer
Question #: 126
Topic #: 1
[All Professional Machine Learning Engineer Questions]

You are training an object detection model using a Cloud TPU v2. Training time is taking longer than expected. Based on this simplified trace obtained with a Cloud TPU profile, what action should you take to decrease training time in a cost-efficient way?

  • A. Move from Cloud TPU v2 to Cloud TPU v3 and increase batch size.
  • B. Move from Cloud TPU v2 to 8 NVIDIA V100 GPUs and increase batch size.
  • C. Rewrite your input function to resize and reshape the input images.
  • D. Rewrite your input function using parallel reads, parallel processing, and prefetch.
Show Suggested Answer Hide Answer
Suggested Answer: D 🗳️
by mymy9418 at Dec. 18, 2022, 3:20 a.m.

Comments

Chosen Answer:
This is a voting comment (?). It is better to Upvote an existing comment if you don't have anything to add.
Switch to a voting comment New
Submit Cancel
pshemol
Highly Voted 2 years, 1 month ago
Selected Answer: D
parallel reads, parallel processing, and prefetch is needed here
upvoted 7 times
...
fitri001
Most Recent 9 months, 2 weeks ago
Selected Answer: D
Optimizing the data pipeline with parallel reads, processing, and prefetching can significantly improve training speed on TPUs by reducing I/O wait times. This approach utilizes the TPU's capabilities more effectively and avoids extra costs associated with hardware upgrades.
upvoted 3 times
fitri001
9 months, 2 weeks ago
A. Moving to a different TPU version (v3) and increasing the batch size might improve training speed, but it's an expensive solution without a guarantee of the most efficient outcome. B. Switching to GPUs (V100) also increases costs and may not be optimized for your specific workload.
upvoted 1 times
fitri001
9 months, 2 weeks ago
(C) can be part of the preprocessing step, but it likely won't address the core issue if the bottleneck is related to how data is being fed into the training process.
upvoted 1 times
...
...
...
M25
1 year, 9 months ago
Selected Answer: D
Went with D
upvoted 1 times
...
TNT87
1 year, 11 months ago
Selected Answer: D
Based on the profile, it appears that the Compute time is relatively low compared to the HostToDevice and DeviceToHost time. This suggests that the data transfer between the host (CPU) and the TPU device is a bottleneck. Therefore, the best action to decrease training time in a cost-efficient way would be to reduce the amount of data transferred between the host and the device.
upvoted 2 times
...
hiromi
2 years, 1 month ago
Selected Answer: D
D - https://www.tensorflow.org/guide/data_performance
upvoted 4 times
...
mymy9418
2 years, 1 month ago
Selected Answer: D
i didn't see v3 has any benefit than v2 https://cloud.google.com/tpu/docs/system-architecture-tpu-vm#performance_benefits_of_tpu_v3_over_v2
upvoted 1 times
...
Community vote distribution
A (35%)
C (25%)
B (20%)
Other
Most Voted
A voting comment increases the vote count for the chosen answer by one.

Upvoting a comment with a selected answer will also increase the vote count towards that answer by one. So if you see a comment that you already agree with, you can upvote it instead of posting a new comment.

SaveCancel