You are training a deep learning model for semantic image segmentation with reduced training time. While using a Deep Learning VM Image, you receive the following error: The resource 'projects/deeplearning-platforn/zones/europe-west4-c/acceleratorTypes/nvidia-tesla-k80' was not found. What should you do?
The error 'The resource was not found' indicates that the specified GPU model is not available in the selected region. This means you need to confirm that the required GPU is available in the region you are working in. If the GPU is not available in that region, you should choose another region that supports the GPU you need.
ANS: B https://cloud.google.com/deep-learning-vm/docs/troubleshooting#resource_not_found https://cloud.google.com/compute/docs/gpus/gpu-regions-zones Resource not found Symptom: - The resource 'projects/deeplearning-platform/zones/europe-west4-c/acceleratorTypes/nvidia-tesla-k80' was not found Problem: You are trying to create an instance with one or more GPUs in a region where GPUs are not available (for example, an instance with a K80 GPU in europe-west4-c). Solution: To determine which region has the required GPU, see GPUs on Compute Engine.
it is B, the error message relates to Quota is different: https://cloud.google.com/deep-learning-vm/docs/troubleshooting#resource_not_found
The resource is not found because it doesn't exist in the region.
https://cloud.google.com/deep-learning-vm/docs/troubleshooting#resource_not_found
B obviously
The error says the resource was not found - hence B. If quota was the problem (A) then you'd see a different error message.
the question is asking what should you do not why is the error. Answer should be A. if you get that exception, make sure to check your limit for instance before running the job.
Went with B
B - because it's "cant be found"
It is clearly mentioned here: https://cloud.google.com/deep-learning-vm/docs/troubleshooting
Not all resources can be found in any region. Therefore - B
B) GPUs are only available in specific regions and zones