ERROR: (gcloud.beta.container.clusters.create) ResponseError: code=403, message=Insufficient regional quota to satisfy request: resource "SSD_TOTAL_GB

Course Q&A Machine Learning Engineering for Production(MLOps) Deploying Machine Learning Models in Production
1

I get this error everytime I run this lab, any suggestions?

ERROR: (gcloud.beta.container.clusters.create) ResponseError: code=403, message=Insufficient regional quota to satisfy request: resource “SSD_TOTAL_GB”: request requires ‘600.0’ and is short ‘100.0’. project has a quota of ‘500.0’ with ‘500.0’ available. View and manage quotas at https://console.cloud.google.com/iam-admin/quotas?usage=USED&project=qwiklabs-gcp-03-ef85958f8150.

2

Hi Ali! Please let us know which lab you are referring to. Thank you!

3

I’m getting the same issue unfortunately.

Issue occurs in Lab " Implementing Canary Releases of TensorFlow Model Deployments with Kubernetes and Anthos Service Mesh" on Task 2 " Task 2. Set up your GKE cluster" in Step 1:

  1. Now run the following command in Cloud Shell to create the Kubernetes cluster cluster-1:
gcloud config set compute/zone ${CLUSTER_ZONE}
gcloud beta container clusters create ${CLUSTER_NAME} \
    --machine-type=n1-standard-4 \
    --num-nodes=6 \
    --workload-pool=${WORKLOAD_POOL} \
    --logging=SYSTEM,WORKLOAD \
    --monitoring=SYSTEM \
    --subnetwork=default \
    --release-channel=stable \
    --labels mesh_id=${MESH_ID}
4

I am also getting the same issue running this :
gcloud config set compute/zone {CLUSTER_ZONE} gcloud beta container clusters create {CLUSTER_NAME}
–machine-type=n1-standard-4
–num-nodes=6
–workload-pool={WORKLOAD_POOL} \ --logging=SYSTEM,WORKLOAD \ --monitoring=SYSTEM \ --subnetwork=default \ --release-channel=stable \ --labels mesh_id={MESH_ID}

5

Hi everyone! We are now investigating this issue. Will update this thread asap. Thank you!

6

Hello again! It seems like the cluster settings need to be changed to satisfy the quota requirements. As a temporary solution, please reduce num-nodes to 4 instead of 6 when executing the cluster create command in Task 2. We’ll file a report to our partners so they can push a more permanent fix. Thank you!

7

For clarity, the command should look like this:

gcloud config set compute/zone ${CLUSTER_ZONE}
gcloud beta container clusters create ${CLUSTER_NAME} \
    --machine-type=n1-standard-4 \
    --num-nodes=4 \
    --workload-pool=${WORKLOAD_POOL} \
    --logging=SYSTEM,WORKLOAD \
    --monitoring=SYSTEM \
    --subnetwork=default \
    --release-channel=stable \
    --labels mesh_id=${MESH_ID}
8

@chris.favila Thanks for suggestion, it works with temporary fix.

9

Hi, not sure if it’s related but after applying that fix, later in task 7 after running

kubectl apply -f tf-serving/deployment-resnet50.yaml

the deployment never turns to available, even after waiting for almost an hour

kubectl get deployments
NAME                        READY   UP-TO-DATE   AVAILABLE   AGE
image-classifier-resnet50   0/1     1            0           58m

The lab runs out of time before the deployment is available. Everything ran fine before that, no errors besides the one mentioned in this thread, where I’ve replaced num-nodes from 6 to 4