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?
Click on the arrows to vote for the correct answer
A. B. C. D.A.
The error message suggests that the Deep Learning VM instance cannot find the specified GPU accelerator type, which is the NVIDIA Tesla K80. To resolve the issue, the following steps can be taken:
A. Ensure that you have GPU quota in the selected region: One possible reason for the error could be that you do not have enough GPU quota in the selected region to create the instance with the specified GPU accelerator type. To check if this is the case, you can go to the Google Cloud Console, navigate to the Quotas page, and look for the GPU quota for the specified region. If you do not have enough GPU quota, you can request an increase in the quota limit.
B. Ensure that the required GPU is available in the selected region: Another reason for the error could be that the specified GPU accelerator type is not available in the selected region. To verify this, you can check the list of available GPU accelerator types for the region using the following command in the Cloud Shell: gcloud compute accelerator-types list --filter="zone:REGION". If the specified GPU accelerator type is not listed, you can either select a different region that supports the required GPU accelerator type or choose a different GPU accelerator type that is available in the selected region.
C. Ensure that you have preemptible GPU quota in the selected region: If you are trying to use preemptible instances, you will need to have preemptible GPU quota in the selected region. You can check the preemptible GPU quota for the region in the Quotas page and request an increase if needed.
D. Ensure that the selected GPU has enough GPU memory for the workload: Finally, it is important to ensure that the selected GPU accelerator type has enough GPU memory to handle the workload of the deep learning model for semantic image segmentation. If the GPU memory is insufficient, you can either reduce the batch size or switch to a GPU accelerator type with more memory.
In summary, to resolve the error, you should verify that you have enough GPU quota in the selected region, the required GPU accelerator type is available in the selected region, you have preemptible GPU quota if using preemptible instances, and the selected GPU accelerator type has enough GPU memory for the workload.