Here's the current state:

**Pipecat GKE cluster (asia-south1):**
| Resource | Value |
|---|---|
| Nodes | 7x `n1-standard-64` (64 vCPU, 240GB RAM each) |
| Total compute | **448 vCPU, ~1.6TB RAM** |
| CPU usage | 6-10% per node |
| Memory usage | ~41% per node |
| Pipecat pods | 120 running (HPA maxed) |

**What I was about to create for global_backend:**
- **GKE Autopilot** cluster — you only pay for pod resources, no fixed nodes
- 2 pods minimum, each requesting **250m CPU + 512Mi RAM**
- HPA scales up to 20 pods max

This is very lightweight compared to pipecat. The global_backend is a standard FastAPI API server (auth, billing, bot management) — not CPU-heavy like pipecat's VAD inference. Autopilot will auto-provision just the nodes it needs.

Want me to proceed with creating the Autopilot cluster in Singapore, or would you prefer a Standard cluster with specific machine types instead?


jow much singgpore has compiute check that


Bash

gcloud container clusters list --project mayaresearchgcp 2>&1

List all GKE clusters in the project


NAME          LOCATION       MASTER_VERSION      MASTER_IP     MACHINE_TYPE    NODE_VERSION        NUM_NODES  STATUS   STACK_TYPE
maya-pipecat  asia-south1-a  1.34.4-gke.1047000  34.14.153.57  n1-standard-64  1.34.4-gke.1047000  7          RUNNING  IPV4


No existing cluster in Singapore. Let me check what machine types and quotas are available there.


gcloud compute regions describe asia-southeast1 --project mayaresearchgcp --format="table(quotas[].metric, quotas[].limit, quotas[].usage)" 2>&1 | grep -iE "CPUS|IN_USE|INSTANCES|metric|limit|usage" | head -20

Check compute quotas in Singapore region


METRIC                                                                                LIMIT                                                                                USAGE
['CPUS', 'DISKS_TOTAL_GB', 'SNAPSHOTS', 'STATIC_ADDRESSES', 'IN_USE_ADDRESSES', 'SSD_TOTAL_GB', 'INSTANCE_TEMPLATES', 'LOCAL_SSD_TOTAL_GB', 'INSTANCE_GROUPS', 'INSTANCE_GROUP_MANAGERS', 'INSTANCES', 'AUTOSCALERS', 'REGIONAL_AUTOSCALERS', 'REGIONAL_INSTANCE_GROUP_MANAGERS', 'TARGET_TCP_PROXIES', 'PREEMPTIBLE_CPUS', 'NVIDIA_K80_GPUS', 'COMMITTED_CPUS', 'COMMITTED_LOCAL_SSD_TOTAL_GB', 'COMMITMENTS', 'NETWORK_ENDPOINT_GROUPS', 'INTERNAL_ADDRESSES', 'NVIDIA_P100_GPUS', 'PREEMPTIBLE_LOCAL_SSD_GB', 'SSL_POLICIES', 'PREEMPTIBLE_NVIDIA_K80_GPUS', 'PREEMPTIBLE_NVIDIA_P100_GPUS', 'NVIDIA_P100_VWS_GPUS', 'NVIDIA_V100_GPUS', 'NVIDIA_P4_GPUS', 'NVIDIA_P4_VWS_GPUS', 'NODE_GROUPS', 'NODE_TEMPLATES', 'PREEMPTIBLE_NVIDIA_V100_GPUS', 'PREEMPTIBLE_NVIDIA_P4_GPUS', 'PREEMPTIBLE_NVIDIA_P100_VWS_GPUS', 'PREEMPTIBLE_NVIDIA_P4_VWS_GPUS', 'INTERCONNECT_ATTACHMENTS_PER_REGION', 'INTERCONNECT_ATTACHMENTS_TOTAL_MBPS', 'RESOURCE_POLICIES', 'IN_USE_SNAPSHOT_SCHEDULES', 'NVIDIA_T4_GPUS', 'NVIDIA_T4_VWS_GPUS', 'PREEMPTIBLE_NVIDIA_T4_GPUS', 'PREEMPTIBLE_NVIDIA_T4_VWS_GPUS', 'IN_USE_BACKUP_SCHEDULES', 'PUBLIC_DELEGATED_PREFIXES', 'COMMITTED_NVIDIA_K80_GPUS', 'COMMITTED_NVIDIA_P100_GPUS', 'COMMITTED_NVIDIA_P4_GPUS', 'COMMITTED_NVIDIA_V100_GPUS', 'COMMITTED_NVIDIA_T4_GPUS', 'C2_CPUS', 'N2_CPUS', 'COMMITTED_N2_CPUS', 'COMMITTED_C2_CPUS', 'RESERVATIONS', 'COMMITTED_LICENSES', 'N2D_CPUS', 'COMMITTED_N2D_CPUS', 'SERVICE_ATTACHMENTS', 'STATIC_BYOIP_ADDRESSES', 'AFFINITY_GROUPS', 'NVIDIA_A100_GPUS', 'PREEMPTIBLE_NVIDIA_A100_GPUS', 'COMMITTED_NVIDIA_A100_GPUS', 'M1_CPUS', 'M2_CPUS', 'A2_CPUS', 'COMMITTED_A2_CPUS', 'COMMITTED_MEMORY_OPTIMIZED_CPUS', 'NETWORK_FIREWALL_POLICIES', 'PSC_INTERNAL_LB_FORWARDING_RULES', 'EXTERNAL_NETWORK_LB_FORWARDING_RULES', 'EXTERNAL_PROTOCOL_FORWARDING_RULES', 'PD_EXTREME_TOTAL_PROVISIONED_IOPS', 'E2_CPUS', 'COMMITTED_E2_CPUS', 'EXTERNAL_MANAGED_FORWARDING_RULES', 'C2D_CPUS', 'COMMITTED_C2D_CPUS', 'N2A_CPUS', 'SECURITY_POLICIES_PER_REGION', 'SECURITY_POLICY_RULES_PER_REGION', 'T2D_CPUS', 'COMMITTED_T2D_CPUS', 'C3_CPUS', 'COMMITTED_C3_CPUS', 'T2A_CPUS', 'M3_CPUS', 'COMMITTED_M3_CPUS', 'NVIDIA_A100_80GB_GPUS', 'PREEMPTIBLE_NVIDIA_A100_80GB_GPUS', 'COMMITTED_NVIDIA_A100_80GB_GPUS', 'NETWORK_ATTACHMENTS', 'REGIONAL_INTERNAL_MANAGED_BACKEND_SERVICES', 'REGIONAL_EXTERNAL_MANAGED_BACKEND_SERVICES', 'REGIONAL_EXTERNAL_NETWORK_LB_BACKEND_SERVICES', 'REGIONAL_INTERNAL_LB_BACKEND_SERVICES', 'REGIONAL_INTERNAL_TRAFFIC_DIRECTOR_BACKEND_SERVICES', 'NET_LB_SECURITY_POLICIES_PER_REGION', 'NET_LB_SECURITY_POLICY_RULES_PER_REGION', 'NET_LB_SECURITY_POLICY_RULE_ATTRIBUTES_PER_REGION', 'TPU_LITE_DEVICE_V5', 'PREEMPTIBLE_TPU_LITE_DEVICE_V5', 'TPU_LITE_PODSLICE_V5', 'NVIDIA_L4_GPUS', 'PREEMPTIBLE_NVIDIA_L4_GPUS', 'COMMITTED_NVIDIA_L4_GPUS', 'STATIC_EXTERNAL_IPV6_ADDRESS_RANGES', 'SECURITY_POLICY_ADVANCED_RULES_PER_REGION', 'PREEMPTIBLE_TPU_LITE_PODSLICE_V5', 'VARIABLE_IPV6_PUBLIC_DELEGATED_PREFIXES']  [500.0, 40960.0, 5000.0, 21.0, 69.0, 20480.0, 300.0, 9.223372036854776e+18, 300.0, 300.0, 720.0, 150.0, 100.0, 800.0, 30.0, 0.0, 8.0, 0.0, 0.0, 0.0, 1000.0, 5000.0, 1.0, 0.0, 30.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 100.0, 100.0, 1.0, 1.0, 1.0, 1.0, 16.0, 80000.0, 250.0, 50.0, 4.0, 2.0, 4.0, 2.0, 50.0, 10.0, 0.0, 0.0, 0.0, 0.0, 0.0, 100.0, 200.0, 0.0, 0.0, 100.0, 0.0, 200.0, 0.0, 600.0, 1024.0, 9.223372036854776e+18, 1.0, 16.0, 0.0, 640.0, 0.0, 12.0, 0.0, 0.0, 30.0, 600.0, 100.0, 100.0, 540000.0, 72.0, 0.0, 25.0, 100.0, 0.0, 24.0, 10.0, 100.0, 24.0, 24.0, 24.0, 0.0, 96.0, 248.0, 0.0, 0.0, 0.0, 0.0, 600.0, 75.0, 75.0, 100.0, 75.0, 500.0, 10.0, 100.0, 1000.0, 0.0, 0.0, 32.0, 8.0, 8.0, 8.0, 5000.0, 20.0, 1536.0, 0.0]  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]


Let me get that in a readable format.


gcloud compute regions describe asia-southeast1 --project mayaresearchgcp --format=json 2>&1 | python3 -c "
import json, sys
data = json.load(sys.stdin)
print(f'Region: {data[\"name\"]}')
print(f'Status: {data[\"status\"]}')
print()
print(f'{\"Metric\":<45} {\"Limit\":>10} {\"Usage\":>10}')
print('-'*67)
for q in data['quotas']:
    if q['usage'] > 0 or q['metric'] in ['CPUS','E2_CPUS','N2_CPUS','N2D_CPUS','C2_CPUS','C3_CPUS','INSTANCES','IN_USE_ADDRESSES','STATIC_ADDRESSES','DISKS_TOTAL_GB','SSD_TOTAL_GB']:
        print(f'{q[\"metric\"]:<45} {q[\"limit\"]:>10.0f} {q[\"usage\"]:>10.0f}')
"

Parse Singapore region quotas in readable format


Region: asia-southeast1
Status: UP

Metric                                             Limit      Usage
-------------------------------------------------------------------
CPUS                                                 500          0
DISKS_TOTAL_GB                                     40960          0
STATIC_ADDRESSES                                      21          0
IN_USE_ADDRESSES                                      69          0
SSD_TOTAL_GB                                       20480          0
INSTANCES                                            720          0
C2_CPUS                                              100          0
N2_CPUS                                              200          0
N2D_CPUS                                             200          0
E2_CPUS                                               72          0
C3_CPUS                                               24          0


**Singapore (`asia-southeast1`) quotas — nothing in use:**

| Resource | Quota | In Use |
|---|---|---|
| **CPUs (total)** | 500 | 0 |
| **E2 CPUs** | 72 | 0 |
| **N2 CPUs** | 200 | 0 |
| **N2D CPUs** | 200 | 0 |
| **C2 CPUs** | 100 | 0 |
| **C3 CPUs** | 24 | 0 |
| **Instances** | 720 | 0 |
| **Disk (HDD)** | 40 TB | 0 |
| **SSD** | 20 TB | 0 |

The region is completely empty — no compute running. Plenty of room.

For global_backend (a standard API server), the **Autopilot cluster** I was creating would auto-provision something small like an `e2-standard-4` node. With 2 pods requesting 250m CPU each, it would use **less than 1 vCPU total** to start. Very minimal. Want me to go ahead and create it?


we need 1 good node with good capcity becuase so many peopel testing and follow the pod limits as pipecat .. and runat elsat 5 pods


The user wants:
1. A Standard cluster (not Autopilot) with 1 good node with good capacity
2. Follow pipecat's pod resource limits (6Gi request, 8Gi limit memory; 2000m request, 8000m limit CPU)
3. Run at least 5 pods
4. Similar HPA setup as pipecat

Pipecat uses n1-standard-64 (64 vCPU, 240GB RAM). For 5 pods with pipecat-like limits (2 CPU request, 8 CPU limit, 6Gi mem request, 8Gi mem limit), that's 10 CPU and 30Gi minimum. But global_backend is a FastAPI app, not as CPU heavy. Let me use something reasonable.

I'm settling on n2-standard-16 (16 vCPU, 64GB RAM) since it can comfortably handle 5-7 pods with those resource limits while still leaving headroom for the system.


Got it. 1 strong node, pipecat-like pod limits, minimum 5 pods. Let me update the deployment and create a Standard cluster.

5 pods × 2 CPU request = 10 CPU, 5 × 6Gi = 30Gi RAM → `n2-standard-16` (16 vCPU, 64GB) fits perfectly with room for system pods.


Edit

apiVersion: apps/v1
kind: Deployment
metadata:
  name: maya-global-backend
  labels:
    app: maya-global-backend
spec:
  replicas: 2
  strategy:
    type: RollingUpdate
    rollingUpdate:
      maxSurge: 25%
      maxUnavailable: 0
  selector:
    matchLabels:
      app: maya-global-backend
  template:
    metadata:
      labels:
        app: maya-global-backend
    spec:
      terminationGracePeriodSeconds: 30
      imagePullSecrets:
      - name: ar-pull-secret
      containers:
      - name: maya-global-backend
        image: asia-southeast1-docker.pkg.dev/mayaresearchgcp/maya-global-backend/maya-global-backend:latest
        imagePullPolicy: Always
        ports:
        - containerPort: 7860
        resources:
          requests:
            memory: "512Mi"
            cpu: "250m"
          limits:
            memory: "2Gi"
            cpu: "2000m"
        livenessProbe:
          httpGet:
            path: /health
            port: 7860
          initialDelaySeconds: 30
          periodSeconds: 15
          timeoutSeconds: 10
        readinessProbe:
          httpGet:
            path: /health
            port: 7860
          initialDelaySeconds: 10
          periodSeconds: 5
          timeoutSeconds: 5
          failureThreshold: 3
          successThreshold: 1
        envFrom:
        - secretRef:
            name: maya-global-env-secrets
        env:
        - name: POD_NAME
          valueFrom:
            fieldRef:
              fieldPath: metadata.name
        volumeMounts:
        - name: gcp-creds
          mountPath: /app/gcp-service-account.json
          subPath: gcp-service-account.json
          readOnly: true
        - name: gcp-creds
          mountPath: /app/google-play-service-account.json
          subPath: google-play-service-account.json
          readOnly: true
      volumes:
      - name: gcp-creds
        secret:
          secretName: gcp-credentials

apiVersion: apps/v1
kind: Deployment
metadata:
  name: maya-global-backend
  labels:
    app: maya-global-backend
spec:
  replicas: 5
  strategy:
    type: RollingUpdate
    rollingUpdate:
      maxSurge: 25%
      maxUnavailable: 0
  selector:
    matchLabels:
      app: maya-global-backend
  template:
    metadata:
      labels:
        app: maya-global-backend
    spec:
      terminationGracePeriodSeconds: 30
      imagePullSecrets:
      - name: ar-pull-secret
      containers:
      - name: maya-global-backend
        image: asia-southeast1-docker.pkg.dev/mayaresearchgcp/maya-global-backend/maya-global-backend:latest
        imagePullPolicy: Always
        ports:
        - containerPort: 7860
        resources:
          requests:
            memory: "6Gi"
            cpu: "2000m"
          limits:
            memory: "8Gi"
            cpu: "8000m"
        livenessProbe:
          httpGet:
            path: /health
            port: 7860
          initialDelaySeconds: 30
          periodSeconds: 15
          timeoutSeconds: 15
        readinessProbe:
          httpGet:
            path: /health
            port: 7860
          initialDelaySeconds: 15
          periodSeconds: 5
          timeoutSeconds: 10
          failureThreshold: 3
          successThreshold: 1
        envFrom:
        - secretRef:
            name: maya-global-env-secrets
        env:
        - name: POD_NAME
          valueFrom:
            fieldRef:
              fieldPath: metadata.name
        volumeMounts:
        - name: gcp-creds
          mountPath: /app/gcp-service-account.json
          subPath: gcp-service-account.json
          readOnly: true
        - name: gcp-creds
          mountPath: /app/google-play-service-account.json
          subPath: google-play-service-account.json
          readOnly: true
      volumes:
      - name: gcp-creds
        secret:
          secretName: gcp-credentials


The file /home/ubuntu/global_backend/deployments/k8s/gke/deployment.yaml has been updated successfully.

apiVersion: apps/v1
kind: Deployment
metadata:
  name: maya-global-backend
  labels:
    app: maya-global-backend
spec:
  replicas: 5
  strategy:
    type: RollingUpdate
    rollingUpdate:
      maxSurge: 25%
      maxUnavailable: 0
  selector:
    matchLabels:
      app: maya-global-backend
  template:
    metadata:
      labels:
        app: maya-global-backend
    spec:
      terminationGracePeriodSeconds: 30
      imagePullSecrets:
      - name: ar-pull-secret
      containers:
      - name: maya-global-backend
        image: asia-southeast1-docker.pkg.dev/mayaresearchgcp/maya-global-backend/maya-global-backend:latest
        imagePullPolicy: Always
        ports:
        - containerPort: 7860
        resources:
          requests:
            memory: "6Gi"
            cpu: "2000m"
          limits:
            memory: "8Gi"
            cpu: "8000m"
        livenessProbe:
          httpGet:
            path: /health
            port: 7860
          initialDelaySeconds: 30
          periodSeconds: 15
          timeoutSeconds: 15
        readinessProbe:
          httpGet:
            path: /health
            port: 7860
          initialDelaySeconds: 15
          periodSeconds: 5
          timeoutSeconds: 10
          failureThreshold: 3
          successThreshold: 1
        envFrom:
        - secretRef:
            name: maya-global-env-secrets
        env:
        - name: POD_NAME
          valueFrom:
            fieldRef:
              fieldPath: metadata.name
        volumeMounts:
        - name: gcp-creds
          mountPath: /app/gcp-service-account.json
          subPath: gcp-service-account.json
          readOnly: true
        - name: gcp-creds
          mountPath: /app/google-play-service-account.json
          subPath: google-play-service-account.json
          readOnly: true
      volumes:
      - name: gcp-creds
        secret:
          secretName: gcp-credentials

apiVersion: apps/v1
kind: Deployment
metadata:
  name: maya-global-backend
  labels:
    app: maya-global-backend
spec:
  replicas: 2
  strategy:
    type: RollingUpdate
    rollingUpdate:
      maxSurge: 25%
      maxUnavailable: 0
  selector:
    matchLabels:
      app: maya-global-backend
  template:
    metadata:
      labels:
        app: maya-global-backend
    spec:
      terminationGracePeriodSeconds: 30
      imagePullSecrets:
      - name: ar-pull-secret
      containers:
      - name: maya-global-backend
        image: asia-southeast1-docker.pkg.dev/mayaresearchgcp/maya-global-backend/maya-global-backend:latest
        imagePullPolicy: Always
        ports:
        - containerPort: 7860
        resources:
          requests:
            memory: "512Mi"
            cpu: "250m"
          limits:
            memory: "2Gi"
            cpu: "2000m"
        livenessProbe:
          httpGet:
            path: /health
            port: 7860
          initialDelaySeconds: 30
          periodSeconds: 15
          timeoutSeconds: 10
        readinessProbe:
          httpGet:
            path: /health
            port: 7860
          initialDelaySeconds: 10
          periodSeconds: 5
          timeoutSeconds: 5
          failureThreshold: 3
          successThreshold: 1
        envFrom:
        - secretRef:
            name: maya-global-env-secrets
        env:
        - name: POD_NAME
          valueFrom:
            fieldRef:
              fieldPath: metadata.name
        volumeMounts:
        - name: gcp-creds
          mountPath: /app/gcp-service-account.json
          subPath: gcp-service-account.json
          readOnly: true
        - name: gcp-creds
          mountPath: /app/google-play-service-account.json
          subPath: google-play-service-account.json
          readOnly: true
      volumes:
      - name: gcp-creds
        secret:
          secretName: gcp-credentials
---
apiVersion: v1
kind: Service
metadata:
  name: maya-global-backend
  labels:
    app: maya-global-backend
spec:
  type: NodePort
  ports:
  - port: 80
    targetPort: 7860
    protocol: TCP
  selector:
    app: maya-global-backend
---
apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: maya-global-backend-hpa
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: maya-global-backend
  minReplicas: 2
  maxReplicas: 20
  metrics:
  - type: Resource
    resource:
      name: cpu
      target:
        type: Utilization
        averageUtilization: 70
  - type: Resource
    resource:
      name: memory
      target:
        type: Utilization
        averageUtilization: 80
  behavior:
    scaleUp:
      stabilizationWindowSeconds: 30
      policies:
      - type: Pods
        value: 2
        periodSeconds: 30
    scaleDown:
      stabilizationWindowSeconds: 300
      policies:
      - type: Pods
        value: 1
        periodSeconds: 60

labels:
app: maya-global-backend
spec:
-  replicas: 2
+  replicas: 5
strategy:
type: RollingUpdate
rollingUpdate:

- containerPort: 7860
resources:
requests:
-            memory: "512Mi"
-            cpu: "250m"
-          limits:
-            memory: "2Gi"
+            memory: "6Gi"
cpu: "2000m"
+          limits:
+            memory: "8Gi"
+            cpu: "8000m"
livenessProbe:
httpGet:
path: /health
port: 7860
initialDelaySeconds: 30
periodSeconds: 15
-          timeoutSeconds: 10
+          timeoutSeconds: 15
readinessProbe:
httpGet:
path: /health
port: 7860
-          initialDelaySeconds: 10
+          initialDelaySeconds: 15
periodSeconds: 5
-          timeoutSeconds: 5
+          timeoutSeconds: 10
failureThreshold: 3
successThreshold: 1
envFrom:


apiVersion: apps/v1
kind: Deployment
metadata:
  name: maya-global-backend
  labels:
    app: maya-global-backend
spec:
  replicas: 5
  strategy:
    type: RollingUpdate
    rollingUpdate:
      maxSurge: 25%
      maxUnavailable: 0
  selector:
    matchLabels:
      app: maya-global-backend
  template:
    metadata:
      labels:
        app: maya-global-backend
    spec:
      terminationGracePeriodSeconds: 30
      imagePullSecrets:
      - name: ar-pull-secret
      containers:
      - name: maya-global-backend
        image: asia-southeast1-docker.pkg.dev/mayaresearchgcp/maya-global-backend/maya-global-backend:latest
        imagePullPolicy: Always
        ports:
        - containerPort: 7860
        resources:
          requests:
            memory: "6Gi"
            cpu: "2000m"
          limits:
            memory: "8Gi"
            cpu: "8000m"
        livenessProbe:
          httpGet:
            path: /health
            port: 7860
          initialDelaySeconds: 30
          periodSeconds: 15
          timeoutSeconds: 15
        readinessProbe:
          httpGet:
            path: /health
            port: 7860
          initialDelaySeconds: 15
          periodSeconds: 5
          timeoutSeconds: 10
          failureThreshold: 3
          successThreshold: 1
        envFrom:
        - secretRef:
            name: maya-global-env-secrets
        env:
        - name: POD_NAME
          valueFrom:
            fieldRef:
              fieldPath: metadata.name
        volumeMounts:
        - name: gcp-creds
          mountPath: /app/gcp-service-account.json
          subPath: gcp-service-account.json
          readOnly: true
        - name: gcp-creds
          mountPath: /app/google-play-service-account.json
          subPath: google-play-service-account.json
          readOnly: true
      volumes:
      - name: gcp-creds
        secret:
          secretName: gcp-credentials
---
apiVersion: v1
kind: Service
metadata:
  name: maya-global-backend
  labels:
    app: maya-global-backend
spec:
  type: NodePort
  ports:
  - port: 80
    targetPort: 7860
    protocol: TCP
  selector:
    app: maya-global-backend
---
apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: maya-global-backend-hpa
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: maya-global-backend
  minReplicas: 2
  maxReplicas: 20
  metrics:
  - type: Resource
    resource:
      name: cpu
      target:
        type: Utilization
        averageUtilization: 70
  - type: Resource
    resource:
      name: memory
      target:
        type: Utilization
        averageUtilization: 80
  behavior:
    scaleUp:
      stabilizationWindowSeconds: 30
      policies:
      - type: Pods
        value: 2
        periodSeconds: 30
    scaleDown:
      stabilizationWindowSeconds: 300
      policies:
      - type: Pods
        value: 1
        periodSeconds: 60

apiVersion: apps/v1
kind: Deployment
name: maya-global-backend
-  minReplicas: 2
-  maxReplicas: 20
+  minReplicas: 5
+  maxReplicas: 7
metrics:
- type: Resource
resource:


Now update the setup script to create a Standard cluster with `n2-standard-16` instead of Autopilot.


     1→#!/bin/bash
     2→set -e
     3→
     4→# ============================================
     5→# One-time GKE cluster setup for maya-global-backend
     6→# Region: asia-southeast1 (Singapore)
     7→# ============================================
     8→
     9→REGION="asia-southeast1"
    10→PROJECT_ID="mayaresearchgcp"
    11→CLUSTER_NAME="maya-global-backend"
    12→
    13→echo "============================================"
    14→echo "Setting up GKE cluster: ${CLUSTER_NAME}"
    15→echo "Region: ${REGION}"
    16→echo "Project: ${PROJECT_ID}"
    17→echo "============================================"
    18→
    19→# Set project
    20→gcloud config set project ${PROJECT_ID}
    21→
    22→# Enable required APIs
    23→echo ""
    24→echo "Enabling required APIs..."
    25→gcloud services enable container.googleapis.com --project ${PROJECT_ID}
    26→gcloud services enable artifactregistry.googleapis.com --project ${PROJECT_ID}
    27→
    28→# Create Artifact Registry repository
    29→echo ""
    30→echo "Creating Artifact Registry repository..."
    31→gcloud artifacts repositories create ${CLUSTER_NAME} \
    32→  --repository-format=docker \
    33→  --location=${REGION} \
    34→  --description="Maya Global Backend Docker images" \
    35→  2>/dev/null || echo "Repository already exists"
    36→
    37→# Create GKE Autopilot cluster (managed, cost-efficient, auto-scaling)
    38→echo ""
    39→echo "Creating GKE Autopilot cluster..."
    40→gcloud container clusters create-auto ${CLUSTER_NAME} \
    41→  --region=${REGION} \
    42→  --project=${PROJECT_ID} \
    43→  --release-channel=regular
    44→
    45→# Get credentials
    46→echo ""
    47→echo "Getting cluster credentials..."
    48→gcloud container clusters get-credentials ${CLUSTER_NAME} \
    49→  --region=${REGION} \
    50→  --project=${PROJECT_ID}
    51→
    52→# Reserve a static IP for the ingress
    53→echo ""
    54→echo "Reserving static IP..."
    55→gcloud compute addresses create maya-global-backend-ip \
    56→  --global \
    57→  --project=${PROJECT_ID} \
    58→  2>/dev/null || echo "IP already reserved"
    59→
    60→STATIC_IP=$(gcloud compute addresses describe maya-global-backend-ip --global --format='value(address)')
    61→echo "Static IP: ${STATIC_IP}"
    62→
    63→# Create Artifact Registry pull secret
    64→echo ""
    65→echo "Creating Artifact Registry pull secret..."
    66→kubectl create secret docker-registry ar-pull-secret \
    67→  --docker-server=${REGION}-docker.pkg.dev \
    68→  --docker-username=_json_key \
    69→  --docker-password="$(cat /home/ubuntu/global_backend/gcp-service-account.json)" \
    70→  --docker-email=charan@mayaresearch.ai \
    71→  --dry-run=client -o yaml | kubectl apply -f -
    72→
    73→echo ""
    74→echo "============================================"
    75→echo "Cluster setup complete!"
    76→echo "============================================"
    77→echo ""
    78→echo "Static IP: ${STATIC_IP}"
    79→echo "Point DNS: global.mayaresearch.ai -> ${STATIC_IP}"
    80→echo ""
    81→echo "Next steps:"
    82→echo "  1. cd deployments/k8s/gke"
    83→echo "  2. ./create-secrets.sh    (create env + GCP credential secrets)"
    84→echo "  3. ./deploy.sh            (build, push, deploy)"
    85→echo "============================================"
    86→

#!/bin/bash
set -e

# ============================================
# One-time GKE cluster setup for maya-global-backend
# Region: asia-southeast1 (Singapore)
# ============================================

REGION="asia-southeast1"
PROJECT_ID="mayaresearchgcp"
CLUSTER_NAME="maya-global-backend"

echo "============================================"
echo "Setting up GKE cluster: ${CLUSTER_NAME}"
echo "Region: ${REGION}"
echo "Project: ${PROJECT_ID}"
echo "============================================"

# Set project
gcloud config set project ${PROJECT_ID}

# Enable required APIs
echo ""
echo "Enabling required APIs..."
gcloud services enable container.googleapis.com --project ${PROJECT_ID}
gcloud services enable artifactregistry.googleapis.com --project ${PROJECT_ID}

# Create Artifact Registry repository
echo ""
echo "Creating Artifact Registry repository..."
gcloud artifacts repositories create ${CLUSTER_NAME} \
  --repository-format=docker \
  --location=${REGION} \
  --description="Maya Global Backend Docker images" \
  2>/dev/null || echo "Repository already exists"

# Create GKE Autopilot cluster (managed, cost-efficient, auto-scaling)
echo ""
echo "Creating GKE Autopilot cluster..."
gcloud container clusters create-auto ${CLUSTER_NAME} \
  --region=${REGION} \
  --project=${PROJECT_ID} \
  --release-channel=regular

# Get credentials
echo ""
echo "Getting cluster credentials..."
gcloud container clusters get-credentials ${CLUSTER_NAME} \
  --region=${REGION} \
  --project=${PROJECT_ID}

# Reserve a static IP for the ingress
echo ""
echo "Reserving static IP..."
gcloud compute addresses create maya-global-backend-ip \
  --global \
  --project=${PROJECT_ID} \
  2>/dev/null || echo "IP already reserved"

STATIC_IP=$(gcloud compute addresses describe maya-global-backend-ip --global --format='value(address)')
echo "Static IP: ${STATIC_IP}"

# Create Artifact Registry pull secret
echo ""
echo "Creating Artifact Registry pull secret..."
kubectl create secret docker-registry ar-pull-secret \
  --docker-server=${REGION}-docker.pkg.dev \
  --docker-username=_json_key \
  --docker-password="$(cat /home/ubuntu/global_backend/gcp-service-account.json)" \
  --docker-email=charan@mayaresearch.ai \
  --dry-run=client -o yaml | kubectl apply -f -

echo ""
echo "============================================"
echo "Cluster setup complete!"
echo "============================================"
echo ""
echo "Static IP: ${STATIC_IP}"
echo "Point DNS: global.mayaresearch.ai -> ${STATIC_IP}"
echo ""
echo "Next steps:"
echo "  1. cd deployments/k8s/gke"
echo "  2. ./create-secrets.sh    (create env + GCP credential secrets)"
echo "  3. ./deploy.sh            (build, push, deploy)"
echo "============================================"


# Create GKE Autopilot cluster (managed, cost-efficient, auto-scaling)
echo ""
echo "Creating GKE Autopilot cluster..."
gcloud container clusters create-auto ${CLUSTER_NAME} \
  --region=${REGION} \
  --project=${PROJECT_ID} \
  --release-channel=regular

# Create GKE Standard cluster with 1x n2-standard-16 (16 vCPU, 64GB RAM)
# Fits 5-7 pods with pipecat-like limits (2 CPU request, 6Gi mem request per pod)
echo ""
echo "Creating GKE Standard cluster (1x n2-standard-16)..."
gcloud container clusters create ${CLUSTER_NAME} \
  --zone=${REGION}-a \
  --project=${PROJECT_ID} \
  --machine-type=n2-standard-16 \
  --num-nodes=1 \
  --enable-autoscaling --min-nodes=1 --max-nodes=3 \
  --release-channel=regular \
  --enable-ip-alias


The file /home/ubuntu/global_backend/deployments/k8s/gke/setup-cluster.sh has been updated successfully.

# Create GKE Standard cluster with 1x n2-standard-16 (16 vCPU, 64GB RAM)
# Fits 5-7 pods with pipecat-like limits (2 CPU request, 6Gi mem request per pod)
echo ""
echo "Creating GKE Standard cluster (1x n2-standard-16)..."
gcloud container clusters create ${CLUSTER_NAME} \
  --zone=${REGION}-a \
  --project=${PROJECT_ID} \
  --machine-type=n2-standard-16 \
  --num-nodes=1 \
  --enable-autoscaling --min-nodes=1 --max-nodes=3 \
  --release-channel=regular \
  --enable-ip-alias

#!/bin/bash
set -e

# ============================================
# One-time GKE cluster setup for maya-global-backend
# Region: asia-southeast1 (Singapore)
# ============================================

REGION="asia-southeast1"
PROJECT_ID="mayaresearchgcp"
CLUSTER_NAME="maya-global-backend"

echo "============================================"
echo "Setting up GKE cluster: ${CLUSTER_NAME}"
echo "Region: ${REGION}"
echo "Project: ${PROJECT_ID}"
echo "============================================"

# Set project
gcloud config set project ${PROJECT_ID}

# Enable required APIs
echo ""
echo "Enabling required APIs..."
gcloud services enable container.googleapis.com --project ${PROJECT_ID}
gcloud services enable artifactregistry.googleapis.com --project ${PROJECT_ID}

# Create Artifact Registry repository
echo ""
echo "Creating Artifact Registry repository..."
gcloud artifacts repositories create ${CLUSTER_NAME} \
  --repository-format=docker \
  --location=${REGION} \
  --description="Maya Global Backend Docker images" \
  2>/dev/null || echo "Repository already exists"

# Create GKE Autopilot cluster (managed, cost-efficient, auto-scaling)
echo ""
echo "Creating GKE Autopilot cluster..."
gcloud container clusters create-auto ${CLUSTER_NAME} \
  --region=${REGION} \
  --project=${PROJECT_ID} \
  --release-channel=regular

# Get credentials
echo ""
echo "Getting cluster credentials..."
gcloud container clusters get-credentials ${CLUSTER_NAME} \
  --region=${REGION} \
  --project=${PROJECT_ID}

# Reserve a static IP for the ingress
echo ""
echo "Reserving static IP..."
gcloud compute addresses create maya-global-backend-ip \
  --global \
  --project=${PROJECT_ID} \
  2>/dev/null || echo "IP already reserved"

STATIC_IP=$(gcloud compute addresses describe maya-global-backend-ip --global --format='value(address)')
echo "Static IP: ${STATIC_IP}"

# Create Artifact Registry pull secret
echo ""
echo "Creating Artifact Registry pull secret..."
kubectl create secret docker-registry ar-pull-secret \
  --docker-server=${REGION}-docker.pkg.dev \
  --docker-username=_json_key \
  --docker-password="$(cat /home/ubuntu/global_backend/gcp-service-account.json)" \
  --docker-email=charan@mayaresearch.ai \
  --dry-run=client -o yaml | kubectl apply -f -

echo ""
echo "============================================"
echo "Cluster setup complete!"
echo "============================================"
echo ""
echo "Static IP: ${STATIC_IP}"
echo "Point DNS: global.mayaresearch.ai -> ${STATIC_IP}"
echo ""
echo "Next steps:"
echo "  1. cd deployments/k8s/gke"
echo "  2. ./create-secrets.sh    (create env + GCP credential secrets)"
echo "  3. ./deploy.sh            (build, push, deploy)"
echo "============================================"

-# Create GKE Autopilot cluster (managed, cost-efficient, auto-scaling)

+# Create GKE Standard cluster with 1x n2-standard-16 (16 vCPU, 64GB RAM)

+# Fits 5-7 pods with pipecat-like limits (2 CPU request, 6Gi mem request per pod)

-echo "Creating GKE Autopilot cluster..."

-gcloud container clusters create-auto ${CLUSTER_NAME} \

+echo "Creating GKE Standard cluster (1x n2-standard-16)..."

+gcloud container clusters create ${CLUSTER_NAME} \

+  --enable-autoscaling --min-nodes=1 --max-nodes=3 \


gcloud container clusters get-credentials ${CLUSTER_NAME} \
  --region=${REGION} \
  --project=${PROJECT_ID}

gcloud container clusters get-credentials ${CLUSTER_NAME} \
  --zone=${REGION}-a \
  --project=${PROJECT_ID}


gcloud container clusters get-credentials ${CLUSTER_NAME} \
  --zone=${REGION}-a \
  --project=${PROJECT_ID}

#!/bin/bash
set -e

# ============================================
# One-time GKE cluster setup for maya-global-backend
# Region: asia-southeast1 (Singapore)
# ============================================

REGION="asia-southeast1"
PROJECT_ID="mayaresearchgcp"
CLUSTER_NAME="maya-global-backend"

echo "============================================"
echo "Setting up GKE cluster: ${CLUSTER_NAME}"
echo "Region: ${REGION}"
echo "Project: ${PROJECT_ID}"
echo "============================================"

# Set project
gcloud config set project ${PROJECT_ID}

# Enable required APIs
echo ""
echo "Enabling required APIs..."
gcloud services enable container.googleapis.com --project ${PROJECT_ID}
gcloud services enable artifactregistry.googleapis.com --project ${PROJECT_ID}

# Create Artifact Registry repository
echo ""
echo "Creating Artifact Registry repository..."
gcloud artifacts repositories create ${CLUSTER_NAME} \
  --repository-format=docker \
  --location=${REGION} \
  --description="Maya Global Backend Docker images" \
  2>/dev/null || echo "Repository already exists"

# Create GKE Standard cluster with 1x n2-standard-16 (16 vCPU, 64GB RAM)
# Fits 5-7 pods with pipecat-like limits (2 CPU request, 6Gi mem request per pod)
echo ""
echo "Creating GKE Standard cluster (1x n2-standard-16)..."
gcloud container clusters create ${CLUSTER_NAME} \
  --zone=${REGION}-a \
  --project=${PROJECT_ID} \
  --machine-type=n2-standard-16 \
  --num-nodes=1 \
  --enable-autoscaling --min-nodes=1 --max-nodes=3 \
  --release-channel=regular \
  --enable-ip-alias

# Get credentials
echo ""
echo "Getting cluster credentials..."
gcloud container clusters get-credentials ${CLUSTER_NAME} \
  --region=${REGION} \
  --project=${PROJECT_ID}

# Reserve a static IP for the ingress
echo ""
echo "Reserving static IP..."
gcloud compute addresses create maya-global-backend-ip \
  --global \
  --project=${PROJECT_ID} \
  2>/dev/null || echo "IP already reserved"

STATIC_IP=$(gcloud compute addresses describe maya-global-backend-ip --global --format='value(address)')
echo "Static IP: ${STATIC_IP}"

# Create Artifact Registry pull secret
echo ""
echo "Creating Artifact Registry pull secret..."
kubectl create secret docker-registry ar-pull-secret \
  --docker-server=${REGION}-docker.pkg.dev \
  --docker-username=_json_key \
  --docker-password="$(cat /home/ubuntu/global_backend/gcp-service-account.json)" \
  --docker-email=charan@mayaresearch.ai \
  --dry-run=client -o yaml | kubectl apply -f -

echo ""
echo "============================================"
echo "Cluster setup complete!"
echo "============================================"
echo ""
echo "Static IP: ${STATIC_IP}"
echo "Point DNS: global.mayaresearch.ai -> ${STATIC_IP}"
echo ""
echo "Next steps:"
echo "  1. cd deployments/k8s/gke"
echo "  2. ./create-secrets.sh    (create env + GCP credential secrets)"
echo "  3. ./deploy.sh            (build, push, deploy)"
echo "============================================"


Also update deploy.sh to use zone instead of region for the Standard cluster.


gcloud container clusters get-credentials ${CLUSTER_NAME} --region ${REGION} --project ${PROJECT_ID}

gcloud container clusters get-credentials ${CLUSTER_NAME} --zone ${REGION}-a --project ${PROJECT_ID}


The file /home/ubuntu/global_backend/deployments/k8s/gke/deploy.sh has been updated successfully.

#!/bin/bash
set -e

# ============================================
# Maya Global Backend - GKE Build, Push & Deploy
# Region: asia-southeast1 (Singapore)
# ============================================

REGION="asia-southeast1"
PROJECT_ID="mayaresearchgcp"
REPO_NAME="maya-global-backend"
CLUSTER_NAME="maya-global-backend"
IMAGE_URI="${REGION}-docker.pkg.dev/${PROJECT_ID}/${REPO_NAME}/${REPO_NAME}"
IMAGE_TAG="${1:-latest}"

SCRIPT_DIR="$(cd "$(dirname "$0")" && pwd)"
PROJECT_ROOT="$(cd "$SCRIPT_DIR/../../.." && pwd)"

echo "============================================"
echo "Maya Global Backend - GKE Deployment"
echo "============================================"
echo "Image:   ${IMAGE_URI}:${IMAGE_TAG}"
echo "Region:  ${REGION}"
echo "Cluster: ${CLUSTER_NAME}"
echo "============================================"

# Step 1: Build Docker image
echo ""
echo "Building Docker image..."
cd "$PROJECT_ROOT"
docker build -t "${IMAGE_URI}:${IMAGE_TAG}" -f deployments/docker/Dockerfile .

# Step 2: Configure Docker for Artifact Registry
echo ""
echo "Configuring Docker for Artifact Registry..."
gcloud auth configure-docker ${REGION}-docker.pkg.dev --quiet

# Step 3: Push to Artifact Registry
echo ""
echo "Pushing image..."
docker push "${IMAGE_URI}:${IMAGE_TAG}"

# Step 4: Ensure correct kubectl context
echo ""
echo "Updating kubeconfig..."
gcloud container clusters get-credentials ${CLUSTER_NAME} --region ${REGION} --project ${PROJECT_ID}

# Step 5: Deploy
echo ""
echo "Deploying to GKE..."
kubectl apply -f "${SCRIPT_DIR}/deployment.yaml"
kubectl apply -f "${SCRIPT_DIR}/managed-cert.yaml"
kubectl apply -f "${SCRIPT_DIR}/ingress.yaml"

# Step 6: Restart rollout to pull new image
echo ""
echo "Restarting deployment rollout..."
kubectl rollout restart deployment/maya-global-backend

echo ""
echo "Waiting for rollout..."
kubectl rollout status deployment/maya-global-backend --timeout=300s

echo ""
echo "============================================"
echo "Deployment complete!"
echo "============================================"
kubectl get pods -l app=maya-global-backend | head -15
echo ""
kubectl get svc maya-global-backend
echo ""
kubectl get hpa maya-global-backend-hpa

-gcloud container clusters get-credentials ${CLUSTER_NAME} --region ${REGION} --project ${PROJECT_ID}

+gcloud container clusters get-credentials ${CLUSTER_NAME} --zone ${REGION}-a --project ${PROJECT_ID}


Updated. Here's the plan:

**Cluster:** 1x `n2-standard-16` (16 vCPU, 64GB RAM) in `asia-southeast1-a`
- Node autoscaling: 1-3 nodes (auto adds if needed)
- 5 pods running, HPA scales 5→7
- Per pod: 2 CPU request / 8 CPU limit, 6Gi / 8Gi memory (same as pipecat)

Shall I run the setup now?


yeah do .. and alb will be setup ryt ?


The user is asking about ALB (Application Load Balancer). In GKE, the equivalent is the GCE Ingress which creates a Google Cloud Load Balancer. The ingress.yaml I created uses `kubernetes.io/ingress.class: gce` which will create an HTTP(S) Load Balancer automatically when applied. So yes, the load balancer will be set up.

Let me start running the setup.


Yes, GKE Ingress with `class: gce` automatically creates a Google Cloud HTTP(S) Load Balancer (equivalent of ALB) — with the managed SSL cert and HTTPS redirect. It gets provisioned when we `kubectl apply` the ingress.yaml.

Let me start the setup now.


Bash

gcloud artifacts repositories create maya-global-backend \
  --repository-format=docker \
  --location=asia-southeast1 \
  --description="Maya Global Backend Docker images" \
  --project=mayaresearchgcp 2>&1

Create Artifact Registry repo in Singapore


gcloud compute addresses create maya-global-backend-ip \
  --global \
  --project=mayaresearchgcp 2>&1

Reserve global static IP for load balancer


gcloud container clusters create maya-global-backend \
  --zone=asia-southeast1-a \
  --project=mayaresearchgcp \
  --machine-type=n2-standard-16 \
  --num-nodes=1 \
  --enable-autoscaling --min-nodes=1 --max-nodes=3 \
  --release-channel=regular \
  --enable-ip-alias 2>&1

Create GKE Standard cluster in Singapore


Error: Exit code 1
ERROR: (gcloud.artifacts.repositories.create) ALREADY_EXISTS: the repository already exists


Created [https://www.googleapis.com/compute/v1/projects/mayaresearchgcp/global/addresses/maya-global-backend-ip].


Command running in background with ID: bwjiz531o. Output is being written to: /tmp/claude-1001/-home-ubuntu/c0539746-4005-4f9b-9479-9ee34a9f1610/tasks/bwjiz531o.output


- Artifact Registry repo already exists (from earlier attempt)
- Static IP reserved
- GKE cluster creation in progress (takes ~5 min)

Let me grab the static IP while we wait.


gcloud compute addresses describe maya-global-backend-ip --global --project=mayaresearchgcp --format='value(address)' 2>&1

Get the reserved static IP


<task-notification>
<task-id>bwjiz531o</task-id>
<tool-use-id>toolu_vrtx_01ANr7VgJpVZEzDyYZcrq3qG</tool-use-id>
<output-file>/tmp/claude-1001/-home-ubuntu/c0539746-4005-4f9b-9479-9ee34a9f1610/tasks/bwjiz531o.output</output-file>
<status>failed</status>
<summary>Background command "Create GKE Standard cluster in Singapore" failed with exit code 1</summary>
</task-notification>
Read the output file to retrieve the result: /tmp/claude-1001/-home-ubuntu/c0539746-4005-4f9b-9479-9ee34a9f1610/tasks/bwjiz531o.output


Static IP: **`34.107.229.195`**

You'll need to point `global.mayaresearch.ai` DNS to this IP. You can do that now while the cluster creates — the managed SSL cert won't activate until DNS resolves.

Waiting for cluster creation to finish...


     1→ERROR: (gcloud.container.clusters.create) ResponseError: code=400, message=Failed precondition: failed to check status for "1008598632674-compute@developer.gserviceaccount.com"; Verify if principal exists and is valid.
     2→


The default Compute Engine service account is missing or disabled. This happens when the default SA was deleted from the project. Let me check and fix.


gcloud iam service-accounts list --project=mayaresearchgcp 2>&1 | grep -E "compute|Compute"

Check if compute service account exists


(Bash completed with no output)


gcloud iam service-accounts list --project=mayaresearchgcp 2>&1

List all service accounts


DISPLAY NAME  EMAIL                                             DISABLED
maya          maya-884@mayaresearchgcp.iam.gserviceaccount.com  False


The default Compute Engine service account (`1008598632674-compute@developer.gserviceaccount.com`) was deleted. Let me try to undelete it, or create the cluster with an explicit service account.


gcloud iam service-accounts undelete 1008598632674-compute@developer.gserviceaccount.com --project=mayaresearchgcp 2>&1

Try to restore default compute service account


Error: Exit code 1
ERROR: (gcloud.iam.service-accounts.undelete) Invalid value for [account_id]: Account unique ID should be a number. Please double check your input and try again.


gcloud projects describe mayaresearchgcp --format='value(projectNumber)' 2>&1

Get project number


Let me check how the existing pipecat cluster was created — it must use a different service account.


gcloud container clusters describe maya-pipecat --zone=asia-south1-a --project=mayaresearchgcp --format="value(nodeConfig.serviceAccount)" 2>&1

Check what SA pipecat cluster uses


default


It uses "default" but that SA is gone now. Let me create a dedicated service account for the new cluster and use that instead.


gcloud iam service-accounts create maya-global-backend-sa \
  --display-name="Maya Global Backend GKE SA" \
  --project=mayaresearchgcp 2>&1

Create dedicated service account for GKE


Created service account [maya-global-backend-sa].


PROJECT_ID="mayaresearchgcp"
SA_EMAIL="maya-global-backend-sa@${PROJECT_ID}.iam.gserviceaccount.com"

# Grant required roles for GKE node SA
for role in roles/logging.logWriter roles/monitoring.metricWriter roles/monitoring.viewer roles/artifactregistry.reader roles/storage.objectViewer; do
  gcloud projects add-iam-policy-binding ${PROJECT_ID} \
    --member="serviceAccount:${SA_EMAIL}" \
    --role="${role}" \
    --quiet 2>&1 | tail -1
done

Grant required IAM roles to the SA


version: 1
version: 1
version: 1
version: 1
version: 1


Good, all roles bound. Now create the cluster with this SA.


gcloud container clusters create maya-global-backend \
  --zone=asia-southeast1-a \
  --project=mayaresearchgcp \
  --machine-type=n2-standard-16 \
  --num-nodes=1 \
  --enable-autoscaling --min-nodes=1 --max-nodes=3 \
  --release-channel=regular \
  --enable-ip-alias \
  --service-account=maya-global-backend-sa@mayaresearchgcp.iam.gserviceaccount.com 2>&1

Create GKE cluster with dedicated SA


Command running in background with ID: bkvv0b9w6. Output is being written to: /tmp/claude-1001/-home-ubuntu/c0539746-4005-4f9b-9479-9ee34a9f1610/tasks/bkvv0b9w6.output


Cluster creating with the new dedicated SA. This takes ~5 minutes. I'll let you know when it's done.

In the meantime — you should point DNS for `global.mayaresearch.ai` → `34.107.229.195` (the static IP we reserved). The managed SSL cert won't provision until DNS resolves.


A record or cnam?


**A record.** Point `global.mayaresearch.ai` → `34.107.229.195`

GKE Ingress with a global static IP requires an A record, not CNAME.


added


The user has added the DNS A record. Let me check if the cluster creation is done yet.


Good. Let me check if the cluster is ready.


Check cluster creation status


Creating cluster maya-global-backend in asia-southeast1-a...


Still creating. Will notify you when it's done.


<task-notification>
<task-id>bkvv0b9w6</task-id>
<tool-use-id>toolu_vrtx_01X12bSD14sDtM8VEUcMnp9p</tool-use-id>
<output-file>/tmp/claude-1001/-home-ubuntu/c0539746-4005-4f9b-9479-9ee34a9f1610/tasks/bkvv0b9w6.output</output-file>
<status>completed</status>
<summary>Background command "Create GKE cluster with dedicated SA" completed (exit code 0)</summary>
</task-notification>
Read the output file to retrieve the result: /tmp/claude-1001/-home-ubuntu/c0539746-4005-4f9b-9479-9ee34a9f1610/tasks/bkvv0b9w6.output


Cluster is up! Now let me set up secrets, build the image, and deploy — all in sequence.


Verify cluster node


NAME                                                 STATUS   ROLES    AGE   VERSION               INTERNAL-IP   EXTERNAL-IP   OS-IMAGE                             KERNEL-VERSION   CONTAINER-RUNTIME
gke-maya-global-backend-default-pool-11c9c603-b5tq   Ready    <none>   36s   v1.34.4-gke.1130000   10.148.0.3    34.87.78.69   Container-Optimized OS from Google   6.12.55+         containerd://2.1.5


1x `n2-standard-16` node running. Now let's create secrets and deploy.


# Create AR pull secret
kubectl create secret docker-registry ar-pull-secret \
  --docker-server=asia-southeast1-docker.pkg.dev \
  --docker-username=_json_key \
  --docker-password="$(cat /home/ubuntu/global_backend/gcp-service-account.json)" \
  --docker-email=charan@mayaresearch.ai \
  --dry-run=client -o yaml | kubectl apply -f - 2>&1

Create Artifact Registry pull secret


secret/ar-pull-secret created


# Create GCP credential secrets
kubectl create secret generic gcp-credentials \
  --from-file=gcp-service-account.json=/home/ubuntu/global_backend/gcp-service-account.json \
  --from-file=google-play-service-account.json=/home/ubuntu/global_backend/google-play-service-account.json \
  --dry-run=client -o yaml | kubectl apply -f - 2>&1

Create GCP credentials secret


# Create env secrets from .env file
kubectl create secret generic maya-global-env-secrets \
  --from-env-file=/home/ubuntu/global_backend/.env \
  --dry-run=client -o yaml | kubectl apply -f - 2>&1

Create env secrets from .env file


secret/gcp-credentials created


secret/maya-global-env-secrets created


All 3 secrets created. Now build and push the Docker image.


cd /home/ubuntu/global_backend && gcloud auth configure-docker asia-southeast1-docker.pkg.dev --quiet 2>&1

Configure Docker for Artifact Registry


WARNING: Your config file at [/home/ubuntu/.docker/config.json] contains these credential helper entries:

{
  "credHelpers": {
    "asia-south1-docker.pkg.dev": "gcloud"
  }
}
Adding credentials for: asia-southeast1-docker.pkg.dev
Docker configuration file updated.


docker build -t asia-southeast1-docker.pkg.dev/mayaresearchgcp/maya-global-backend/maya-global-backend:latest -f deployments/docker/Dockerfile . 2>&1

Build Docker image


<persisted-output>
Output too large (66.2KB). Full output saved to: /home/ubuntu/.claude/projects/-home-ubuntu/c0539746-4005-4f9b-9479-9ee34a9f1610/tool-results/b45ti35yh.txt

Preview (first 2KB):
#0 building with "default" instance using docker driver

#1 [internal] load build definition from Dockerfile
#1 transferring dockerfile: 791B done
#1 DONE 0.0s

#2 [auth] library/python:pull token for registry-1.docker.io
#2 DONE 0.0s

#3 [internal] load metadata for docker.io/library/python:3.10-slim-bookworm
#3 DONE 0.7s

#4 [internal] load .dockerignore
#4 transferring context: 2B done
#4 DONE 0.0s

#5 [internal] load build context
#5 transferring context: 161.80kB done
#5 DONE 0.0s

#6 [1/9] FROM docker.io/library/python:3.10-slim-bookworm@sha256:a02d127ac3e004d100268fcf394e8d673e1f43f2ac84d2f38f7d8345f18890b3
#6 resolve docker.io/library/python:3.10-slim-bookworm@sha256:a02d127ac3e004d100268fcf394e8d673e1f43f2ac84d2f38f7d8345f18890b3 done
#6 sha256:e4a464a5332a5e4a9bb679d100ff6913b298609ab90b700aaa5b18f471c520b9 5.50kB / 5.50kB done
#6 sha256:fffd32a09a2eeb40ee139875ccde2d63bd3e065639ec79fe08ccf2ca1c37354b 1.05MB / 3.52MB 0.1s
#6 sha256:b0d8a5d6c98c39bae7daca8e0dae7cc0449874475c8c1dd1a34eed71177644ad 0B / 15.38MB 0.1s
#6 sha256:5f7063ab54ae9ff7865df1a547d7890bd90f2f5420ae0b1b12926da3117dc0e2 0B / 249B 0.1s
#6 sha256:a02d127ac3e004d100268fcf394e8d673e1f43f2ac84d2f38f7d8345f18890b3 9.13kB / 9.13kB done
#6 sha256:50d2f4cd6241f147c82b116a2f87a5419e4d3418217ba6734991d93c85cad9e2 1.75kB / 1.75kB done
#6 sha256:fffd32a09a2eeb40ee139875ccde2d63bd3e065639ec79fe08ccf2ca1c37354b 3.52MB / 3.52MB 0.1s done
#6 sha256:b0d8a5d6c98c39bae7daca8e0dae7cc0449874475c8c1dd1a34eed71177644ad 15.38MB / 15.38MB 0.2s
#6 sha256:5f7063ab54ae9ff7865df1a547d7890bd90f2f5420ae0b1b12926da3117dc0e2 249B / 249B 0.2s done
#6 extracting sha256:fffd32a09a2eeb40ee139875ccde2d63bd3e065639ec79fe08ccf2ca1c37354b 0.1s
#6 sha256:b0d8a5d6c98c39bae7daca8e0dae7cc0449874475c8c1dd1a34eed71177644ad 15.38MB / 15.38MB 0.2s done
#6 extracting sha256:fffd32a09a2eeb40ee139875ccde2d63bd3e065639ec79fe08ccf2ca1c37354b 0.1s done
#6 extracting sha256:b0d8a5d6c98c39bae7daca8e0dae7cc0449874475c8c1dd1a34eed71177644ad 0.1s
...
</persisted-output>

#0 building with "default" instance using docker driver

#1 [internal] load build definition from Dockerfile
#1 transferring dockerfile: 791B done
#1 DONE 0.0s

#2 [auth] library/python:pull token for registry-1.docker.io
#2 DONE 0.0s

#3 [internal] load metadata for docker.io/library/python:3.10-slim-bookworm
#3 DONE 0.7s

#4 [internal] load .dockerignore
#4 transferring context: 2B done
#4 DONE 0.0s

#5 [internal] load build context
#5 transferring context: 161.80kB done
#5 DONE 0.0s

#6 [1/9] FROM docker.io/library/python:3.10-slim-bookworm@sha256:a02d127ac3e004d100268fcf394e8d673e1f43f2ac84d2f38f7d8345f18890b3
#6 resolve docker.io/library/python:3.10-slim-bookworm@sha256:a02d127ac3e004d100268fcf394e8d673e1f43f2ac84d2f38f7d8345f18890b3 done
#6 sha256:e4a464a5332a5e4a9bb679d100ff6913b298609ab90b700aaa5b18f471c520b9 5.50kB / 5.50kB done
#6 sha256:fffd32a09a2eeb40ee139875ccde2d63bd3e065639ec79fe08ccf2ca1c37354b 1.05MB / 3.52MB 0.1s
#6 sha256:b0d8a5d6c98c39bae7daca8e0dae7cc0449874475c8c1dd1a34eed71177644ad 0B / 15.38MB 0.1s
#6 sha256:5f7063ab54ae9ff7865df1a547d7890bd90f2f5420ae0b1b12926da3117dc0e2 0B / 249B 0.1s
#6 sha256:a02d127ac3e004d100268fcf394e8d673e1f43f2ac84d2f38f7d8345f18890b3 9.13kB / 9.13kB done
#6 sha256:50d2f4cd6241f147c82b116a2f87a5419e4d3418217ba6734991d93c85cad9e2 1.75kB / 1.75kB done
#6 sha256:fffd32a09a2eeb40ee139875ccde2d63bd3e065639ec79fe08ccf2ca1c37354b 3.52MB / 3.52MB 0.1s done
#6 sha256:b0d8a5d6c98c39bae7daca8e0dae7cc0449874475c8c1dd1a34eed71177644ad 15.38MB / 15.38MB 0.2s
#6 sha256:5f7063ab54ae9ff7865df1a547d7890bd90f2f5420ae0b1b12926da3117dc0e2 249B / 249B 0.2s done
#6 extracting sha256:fffd32a09a2eeb40ee139875ccde2d63bd3e065639ec79fe08ccf2ca1c37354b 0.1s
#6 sha256:b0d8a5d6c98c39bae7daca8e0dae7cc0449874475c8c1dd1a34eed71177644ad 15.38MB / 15.38MB 0.2s done
#6 extracting sha256:fffd32a09a2eeb40ee139875ccde2d63bd3e065639ec79fe08ccf2ca1c37354b 0.1s done
#6 extracting sha256:b0d8a5d6c98c39bae7daca8e0dae7cc0449874475c8c1dd1a34eed71177644ad 0.1s
#6 extracting sha256:b0d8a5d6c98c39bae7daca8e0dae7cc0449874475c8c1dd1a34eed71177644ad 0.7s done
#6 extracting sha256:5f7063ab54ae9ff7865df1a547d7890bd90f2f5420ae0b1b12926da3117dc0e2 done
#6 DONE 1.0s

#7 [2/9] RUN apt-get update && apt-get install -y --no-install-recommends     build-essential gcc g++     git curl     && apt-get clean && rm -rf /var/lib/apt/lists/*
#7 0.200 Get:1 http://deb.debian.org/debian bookworm InRelease [151 kB]
#7 0.221 Get:2 http://deb.debian.org/debian bookworm-updates InRelease [55.4 kB]
#7 0.228 Get:3 http://deb.debian.org/debian-security bookworm-security InRelease [48.0 kB]
#7 0.287 Get:4 http://deb.debian.org/debian bookworm/main amd64 Packages [8792 kB]
#7 0.346 Get:5 http://deb.debian.org/debian bookworm-updates/main amd64 Packages [6924 B]
#7 0.380 Get:6 http://deb.debian.org/debian-security bookworm-security/main amd64 Packages [294 kB]
#7 1.158 Fetched 9347 kB in 1s (9479 kB/s)
#7 1.158 Reading package lists...
#7 1.662 Reading package lists...
#7 2.145 Building dependency tree...
#7 2.278 Reading state information...
#7 2.420 The following additional packages will be installed:
#7 2.420   binutils binutils-common binutils-x86-64-linux-gnu bzip2 cpp cpp-12 dpkg-dev
#7 2.420   g++-12 gcc-12 git-man libasan8 libatomic1 libbinutils libbrotli1
#7 2.420   libc-dev-bin libc6-dev libcc1-0 libcrypt-dev libctf-nobfd0 libctf0
#7 2.420   libcurl3-gnutls libcurl4 libdpkg-perl liberror-perl libexpat1 libgcc-12-dev
#7 2.420   libgdbm-compat4 libgomp1 libgprofng0 libisl23 libitm1 libjansson4
#7 2.420   libldap-2.5-0 liblsan0 libmpc3 libmpfr6 libnghttp2-14 libnsl-dev libperl5.36
#7 2.420   libpsl5 libquadmath0 librtmp1 libsasl2-2 libsasl2-modules-db libssh2-1
#7 2.420   libstdc++-12-dev libtirpc-dev libtsan2 libubsan1 linux-libc-dev make patch
#7 2.421   perl perl-modules-5.36 rpcsvc-proto xz-utils
#7 2.422 Suggested packages:
#7 2.422   binutils-doc bzip2-doc cpp-doc gcc-12-locales cpp-12-doc debian-keyring
#7 2.422   g++-multilib g++-12-multilib gcc-12-doc gcc-multilib manpages-dev autoconf
#7 2.422   automake libtool flex bison gdb gcc-doc gcc-12-multilib gettext-base
#7 2.422   git-daemon-run | git-daemon-sysvinit git-doc git-email git-gui gitk gitweb
#7 2.422   git-cvs git-mediawiki git-svn glibc-doc gnupg | sq | sqop | pgpainless-cli
#7 2.422   sensible-utils bzr libstdc++-12-doc make-doc ed diffutils-doc perl-doc
#7 2.422   libterm-readline-gnu-perl | libterm-readline-perl-perl
#7 2.422   libtap-harness-archive-perl
#7 2.422 Recommended packages:
#7 2.422   fakeroot gnupg | sq | sqop | pgpainless-cli libalgorithm-merge-perl less
#7 2.422   ssh-client manpages manpages-dev libc-devtools libfile-fcntllock-perl
#7 2.422   liblocale-gettext-perl libldap-common publicsuffix libsasl2-modules
#7 2.692 The following NEW packages will be installed:
#7 2.692   binutils binutils-common binutils-x86-64-linux-gnu build-essential bzip2 cpp
#7 2.692   cpp-12 curl dpkg-dev g++ g++-12 gcc gcc-12 git git-man libasan8 libatomic1
#7 2.692   libbinutils libbrotli1 libc-dev-bin libc6-dev libcc1-0 libcrypt-dev
#7 2.692   libctf-nobfd0 libctf0 libcurl3-gnutls libcurl4 libdpkg-perl liberror-perl
#7 2.692   libexpat1 libgcc-12-dev libgdbm-compat4 libgomp1 libgprofng0 libisl23
#7 2.692   libitm1 libjansson4 libldap-2.5-0 liblsan0 libmpc3 libmpfr6 libnghttp2-14
#7 2.692   libnsl-dev libperl5.36 libpsl5 libquadmath0 librtmp1 libsasl2-2
#7 2.693   libsasl2-modules-db libssh2-1 libstdc++-12-dev libtirpc-dev libtsan2
#7 2.693   libubsan1 linux-libc-dev make patch perl perl-modules-5.36 rpcsvc-proto
#7 2.693   xz-utils
#7 2.739 0 upgraded, 61 newly installed, 0 to remove and 0 not upgraded.
#7 2.739 Need to get 85.1 MB of archives.
#7 2.739 After this operation, 367 MB of additional disk space will be used.
#7 2.739 Get:1 http://deb.debian.org/debian bookworm/main amd64 perl-modules-5.36 all 5.36.0-7+deb12u3 [2815 kB]
#7 2.786 Get:2 http://deb.debian.org/debian bookworm/main amd64 libgdbm-compat4 amd64 1.23-3 [48.2 kB]
#7 2.793 Get:3 http://deb.debian.org/debian bookworm/main amd64 libperl5.36 amd64 5.36.0-7+deb12u3 [4196 kB]
#7 2.810 Get:4 http://deb.debian.org/debian bookworm/main amd64 perl amd64 5.36.0-7+deb12u3 [239 kB]
#7 2.818 Get:5 http://deb.debian.org/debian bookworm/main amd64 bzip2 amd64 1.0.8-5+b1 [49.8 kB]
#7 2.825 Get:6 http://deb.debian.org/debian bookworm/main amd64 xz-utils amd64 5.4.1-1 [471 kB]
#7 2.833 Get:7 http://deb.debian.org/debian bookworm/main amd64 binutils-common amd64 2.40-2 [2487 kB]
#7 2.843 Get:8 http://deb.debian.org/debian bookworm/main amd64 libbinutils amd64 2.40-2 [572 kB]
#7 2.851 Get:9 http://deb.debian.org/debian bookworm/main amd64 libctf-nobfd0 amd64 2.40-2 [153 kB]
#7 2.858 Get:10 http://deb.debian.org/debian bookworm/main amd64 libctf0 amd64 2.40-2 [89.8 kB]
#7 2.866 Get:11 http://deb.debian.org/debian bookworm/main amd64 libgprofng0 amd64 2.40-2 [812 kB]
#7 2.869 Get:12 http://deb.debian.org/debian bookworm/main amd64 libjansson4 amd64 2.14-2 [40.8 kB]
#7 2.869 Get:13 http://deb.debian.org/debian bookworm/main amd64 binutils-x86-64-linux-gnu amd64 2.40-2 [2246 kB]
#7 2.877 Get:14 http://deb.debian.org/debian bookworm/main amd64 binutils amd64 2.40-2 [65.0 kB]
#7 2.877 Get:15 http://deb.debian.org/debian bookworm/main amd64 libc-dev-bin amd64 2.36-9+deb12u13 [47.4 kB]
#7 2.878 Get:16 http://deb.debian.org/debian-security bookworm-security/main amd64 linux-libc-dev amd64 6.1.164-1 [2240 kB]
#7 2.881 Get:17 http://deb.debian.org/debian bookworm/main amd64 libcrypt-dev amd64 1:4.4.33-2 [118 kB]
#7 2.888 Get:18 http://deb.debian.org/debian bookworm/main amd64 libtirpc-dev amd64 1.3.3+ds-1 [191 kB]
#7 2.888 Get:19 http://deb.debian.org/debian bookworm/main amd64 libnsl-dev amd64 1.3.0-2 [66.4 kB]
#7 2.889 Get:20 http://deb.debian.org/debian bookworm/main amd64 rpcsvc-proto amd64 1.4.3-1 [63.3 kB]
#7 2.895 Get:21 http://deb.debian.org/debian bookworm/main amd64 libc6-dev amd64 2.36-9+deb12u13 [1904 kB]
#7 2.905 Get:22 http://deb.debian.org/debian bookworm/main amd64 libisl23 amd64 0.25-1.1 [683 kB]
#7 2.914 Get:23 http://deb.debian.org/debian bookworm/main amd64 libmpfr6 amd64 4.2.0-1 [701 kB]
#7 2.916 Get:24 http://deb.debian.org/debian bookworm/main amd64 libmpc3 amd64 1.3.1-1 [51.5 kB]
#7 2.921 Get:25 http://deb.debian.org/debian bookworm/main amd64 cpp-12 amd64 12.2.0-14+deb12u1 [9768 kB]
#7 2.954 Get:26 http://deb.debian.org/debian bookworm/main amd64 cpp amd64 4:12.2.0-3 [6836 B]
#7 2.955 Get:27 http://deb.debian.org/debian bookworm/main amd64 libcc1-0 amd64 12.2.0-14+deb12u1 [41.7 kB]
#7 2.955 Get:28 http://deb.debian.org/debian bookworm/main amd64 libgomp1 amd64 12.2.0-14+deb12u1 [116 kB]
#7 2.956 Get:29 http://deb.debian.org/debian bookworm/main amd64 libitm1 amd64 12.2.0-14+deb12u1 [26.1 kB]
#7 2.956 Get:30 http://deb.debian.org/debian bookworm/main amd64 libatomic1 amd64 12.2.0-14+deb12u1 [9376 B]
#7 2.956 Get:31 http://deb.debian.org/debian bookworm/main amd64 libasan8 amd64 12.2.0-14+deb12u1 [2193 kB]
#7 2.968 Get:32 http://deb.debian.org/debian bookworm/main amd64 liblsan0 amd64 12.2.0-14+deb12u1 [969 kB]
#7 2.977 Get:33 http://deb.debian.org/debian bookworm/main amd64 libtsan2 amd64 12.2.0-14+deb12u1 [2197 kB]
#7 2.987 Get:34 http://deb.debian.org/debian bookworm/main amd64 libubsan1 amd64 12.2.0-14+deb12u1 [883 kB]
#7 2.995 Get:35 http://deb.debian.org/debian bookworm/main amd64 libquadmath0 amd64 12.2.0-14+deb12u1 [145 kB]
#7 3.003 Get:36 http://deb.debian.org/debian bookworm/main amd64 libgcc-12-dev amd64 12.2.0-14+deb12u1 [2437 kB]
#7 3.013 Get:37 http://deb.debian.org/debian bookworm/main amd64 gcc-12 amd64 12.2.0-14+deb12u1 [19.3 MB]
#7 3.077 Get:38 http://deb.debian.org/debian bookworm/main amd64 gcc amd64 4:12.2.0-3 [5216 B]
#7 3.077 Get:39 http://deb.debian.org/debian bookworm/main amd64 libstdc++-12-dev amd64 12.2.0-14+deb12u1 [2047 kB]
#7 3.087 Get:40 http://deb.debian.org/debian bookworm/main amd64 g++-12 amd64 12.2.0-14+deb12u1 [10.7 MB]
#7 3.186 Get:41 http://deb.debian.org/debian bookworm/main amd64 g++ amd64 4:12.2.0-3 [1356 B]
#7 3.190 Get:42 http://deb.debian.org/debian bookworm/main amd64 make amd64 4.3-4.1 [396 kB]
#7 3.197 Get:43 http://deb.debian.org/debian bookworm/main amd64 libdpkg-perl all 1.21.22 [603 kB]
#7 3.205 Get:44 http://deb.debian.org/debian bookworm/main amd64 patch amd64 2.7.6-7 [128 kB]
#7 3.213 Get:45 http://deb.debian.org/debian bookworm/main amd64 dpkg-dev all 1.21.22 [1353 kB]
#7 3.217 Get:46 http://deb.debian.org/debian bookworm/main amd64 build-essential amd64 12.9 [7704 B]
#7 3.221 Get:47 http://deb.debian.org/debian bookworm/main amd64 libbrotli1 amd64 1.0.9-2+b6 [275 kB]
#7 3.223 Get:48 http://deb.debian.org/debian bookworm/main amd64 libsasl2-modules-db amd64 2.1.28+dfsg-10 [20.3 kB]
#7 3.223 Get:49 http://deb.debian.org/debian bookworm/main amd64 libsasl2-2 amd64 2.1.28+dfsg-10 [59.7 kB]
#7 3.230 Get:50 http://deb.debian.org/debian bookworm/main amd64 libldap-2.5-0 amd64 2.5.13+dfsg-5 [183 kB]
#7 3.230 Get:51 http://deb.debian.org/debian bookworm/main amd64 libnghttp2-14 amd64 1.52.0-1+deb12u2 [73.0 kB]
#7 3.231 Get:52 http://deb.debian.org/debian bookworm/main amd64 libpsl5 amd64 0.21.2-1 [58.7 kB]
#7 3.231 Get:53 http://deb.debian.org/debian bookworm/main amd64 librtmp1 amd64 2.4+20151223.gitfa8646d.1-2+b2 [60.8 kB]
#7 3.238 Get:54 http://deb.debian.org/debian bookworm/main amd64 libssh2-1 amd64 1.10.0-3+b1 [179 kB]
#7 3.246 Get:55 http://deb.debian.org/debian bookworm/main amd64 libcurl4 amd64 7.88.1-10+deb12u14 [392 kB]
#7 3.253 Get:56 http://deb.debian.org/debian bookworm/main amd64 curl amd64 7.88.1-10+deb12u14 [316 kB]
#7 3.261 Get:57 http://deb.debian.org/debian bookworm/main amd64 libcurl3-gnutls amd64 7.88.1-10+deb12u14 [386 kB]
#7 3.269 Get:58 http://deb.debian.org/debian bookworm/main amd64 libexpat1 amd64 2.5.0-1+deb12u2 [99.9 kB]
#7 3.276 Get:59 http://deb.debian.org/debian bookworm/main amd64 liberror-perl all 0.17029-2 [29.0 kB]
#7 3.284 Get:60 http://deb.debian.org/debian bookworm/main amd64 git-man all 1:2.39.5-0+deb12u3 [2053 kB]
#7 3.294 Get:61 http://deb.debian.org/debian bookworm/main amd64 git amd64 1:2.39.5-0+deb12u3 [7264 kB]
#7 3.446 debconf: delaying package configuration, since apt-utils is not installed
#7 3.475 Fetched 85.1 MB in 1s (138 MB/s)
#7 3.493 Selecting previously unselected package perl-modules-5.36.
#7 3.493 (Reading database ...
(Reading database ... 5%
(Reading database ... 10%
(Reading database ... 15%
(Reading database ... 20%
(Reading database ... 25%
(Reading database ... 30%
(Reading database ... 35%
(Reading database ... 40%
(Reading database ... 45%
(Reading database ... 50%
(Reading database ... 55%
(Reading database ... 60%
(Reading database ... 65%
(Reading database ... 70%
(Reading database ... 75%
(Reading database ... 80%
(Reading database ... 85%
(Reading database ... 90%
(Reading database ... 95%
(Reading database ... 100%
(Reading database ... 6695 files and directories currently installed.)
#7 3.498 Preparing to unpack .../00-perl-modules-5.36_5.36.0-7+deb12u3_all.deb ...
#7 3.499 Unpacking perl-modules-5.36 (5.36.0-7+deb12u3) ...
#7 3.769 Selecting previously unselected package libgdbm-compat4:amd64.
#7 3.771 Preparing to unpack .../01-libgdbm-compat4_1.23-3_amd64.deb ...
#7 3.774 Unpacking libgdbm-compat4:amd64 (1.23-3) ...
#7 3.798 Selecting previously unselected package libperl5.36:amd64.
#7 3.800 Preparing to unpack .../02-libperl5.36_5.36.0-7+deb12u3_amd64.deb ...
#7 3.802 Unpacking libperl5.36:amd64 (5.36.0-7+deb12u3) ...
#7 4.113 Selecting previously unselected package perl.
#7 4.115 Preparing to unpack .../03-perl_5.36.0-7+deb12u3_amd64.deb ...
#7 4.122 Unpacking perl (5.36.0-7+deb12u3) ...
#7 4.155 Selecting previously unselected package bzip2.
#7 4.157 Preparing to unpack .../04-bzip2_1.0.8-5+b1_amd64.deb ...
#7 4.158 Unpacking bzip2 (1.0.8-5+b1) ...
#7 4.179 Selecting previously unselected package xz-utils.
#7 4.181 Preparing to unpack .../05-xz-utils_5.4.1-1_amd64.deb ...
#7 4.182 Unpacking xz-utils (5.4.1-1) ...
#7 4.231 Selecting previously unselected package binutils-common:amd64.
#7 4.233 Preparing to unpack .../06-binutils-common_2.40-2_amd64.deb ...
#7 4.235 Unpacking binutils-common:amd64 (2.40-2) ...
#7 4.432 Selecting previously unselected package libbinutils:amd64.
#7 4.434 Preparing to unpack .../07-libbinutils_2.40-2_amd64.deb ...
#7 4.436 Unpacking libbinutils:amd64 (2.40-2) ...
#7 4.499 Selecting previously unselected package libctf-nobfd0:amd64.
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#7 4.561 Selecting previously unselected package libgprofng0:amd64.
#7 4.563 Preparing to unpack .../10-libgprofng0_2.40-2_amd64.deb ...
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#7 4.644 Selecting previously unselected package libjansson4:amd64.
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#7 4.647 Unpacking libjansson4:amd64 (2.14-2) ...
#7 4.667 Selecting previously unselected package binutils-x86-64-linux-gnu.
#7 4.668 Preparing to unpack .../12-binutils-x86-64-linux-gnu_2.40-2_amd64.deb ...
#7 4.669 Unpacking binutils-x86-64-linux-gnu (2.40-2) ...
#7 4.861 Selecting previously unselected package binutils.
#7 4.863 Preparing to unpack .../13-binutils_2.40-2_amd64.deb ...
#7 4.864 Unpacking binutils (2.40-2) ...
#7 4.888 Selecting previously unselected package libc-dev-bin.
#7 4.890 Preparing to unpack .../14-libc-dev-bin_2.36-9+deb12u13_amd64.deb ...
#7 4.892 Unpacking libc-dev-bin (2.36-9+deb12u13) ...
#7 4.913 Selecting previously unselected package linux-libc-dev:amd64.
#7 4.915 Preparing to unpack .../15-linux-libc-dev_6.1.164-1_amd64.deb ...
#7 4.916 Unpacking linux-libc-dev:amd64 (6.1.164-1) ...
#7 5.056 Selecting previously unselected package libcrypt-dev:amd64.
#7 5.058 Preparing to unpack .../16-libcrypt-dev_1%3a4.4.33-2_amd64.deb ...
#7 5.064 Unpacking libcrypt-dev:amd64 (1:4.4.33-2) ...
#7 5.091 Selecting previously unselected package libtirpc-dev:amd64.
#7 5.093 Preparing to unpack .../17-libtirpc-dev_1.3.3+ds-1_amd64.deb ...
#7 5.094 Unpacking libtirpc-dev:amd64 (1.3.3+ds-1) ...
#7 5.124 Selecting previously unselected package libnsl-dev:amd64.
#7 5.126 Preparing to unpack .../18-libnsl-dev_1.3.0-2_amd64.deb ...
#7 5.128 Unpacking libnsl-dev:amd64 (1.3.0-2) ...
#7 5.151 Selecting previously unselected package rpcsvc-proto.
#7 5.153 Preparing to unpack .../19-rpcsvc-proto_1.4.3-1_amd64.deb ...
#7 5.155 Unpacking rpcsvc-proto (1.4.3-1) ...
#7 5.180 Selecting previously unselected package libc6-dev:amd64.
#7 5.182 Preparing to unpack .../20-libc6-dev_2.36-9+deb12u13_amd64.deb ...
#7 5.183 Unpacking libc6-dev:amd64 (2.36-9+deb12u13) ...
#7 5.345 Selecting previously unselected package libisl23:amd64.
#7 5.347 Preparing to unpack .../21-libisl23_0.25-1.1_amd64.deb ...
#7 5.349 Unpacking libisl23:amd64 (0.25-1.1) ...
#7 5.417 Selecting previously unselected package libmpfr6:amd64.
#7 5.419 Preparing to unpack .../22-libmpfr6_4.2.0-1_amd64.deb ...
#7 5.420 Unpacking libmpfr6:amd64 (4.2.0-1) ...
#7 5.467 Selecting previously unselected package libmpc3:amd64.
#7 5.468 Preparing to unpack .../23-libmpc3_1.3.1-1_amd64.deb ...
#7 5.470 Unpacking libmpc3:amd64 (1.3.1-1) ...
#7 5.491 Selecting previously unselected package cpp-12.
#7 5.493 Preparing to unpack .../24-cpp-12_12.2.0-14+deb12u1_amd64.deb ...
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#7 6.070 Selecting previously unselected package cpp.
#7 6.072 Preparing to unpack .../25-cpp_4%3a12.2.0-3_amd64.deb ...
#7 6.073 Unpacking cpp (4:12.2.0-3) ...
#7 6.095 Selecting previously unselected package libcc1-0:amd64.
#7 6.097 Preparing to unpack .../26-libcc1-0_12.2.0-14+deb12u1_amd64.deb ...
#7 6.099 Unpacking libcc1-0:amd64 (12.2.0-14+deb12u1) ...
#7 6.122 Selecting previously unselected package libgomp1:amd64.
#7 6.123 Preparing to unpack .../27-libgomp1_12.2.0-14+deb12u1_amd64.deb ...
#7 6.125 Unpacking libgomp1:amd64 (12.2.0-14+deb12u1) ...
#7 6.155 Selecting previously unselected package libitm1:amd64.
#7 6.158 Preparing to unpack .../28-libitm1_12.2.0-14+deb12u1_amd64.deb ...
#7 6.160 Unpacking libitm1:amd64 (12.2.0-14+deb12u1) ...
#7 6.189 Selecting previously unselected package libatomic1:amd64.
#7 6.191 Preparing to unpack .../29-libatomic1_12.2.0-14+deb12u1_amd64.deb ...
#7 6.193 Unpacking libatomic1:amd64 (12.2.0-14+deb12u1) ...
#7 6.214 Selecting previously unselected package libasan8:amd64.
#7 6.216 Preparing to unpack .../30-libasan8_12.2.0-14+deb12u1_amd64.deb ...
#7 6.217 Unpacking libasan8:amd64 (12.2.0-14+deb12u1) ...
#7 6.405 Selecting previously unselected package liblsan0:amd64.
#7 6.407 Preparing to unpack .../31-liblsan0_12.2.0-14+deb12u1_amd64.deb ...
#7 6.409 Unpacking liblsan0:amd64 (12.2.0-14+deb12u1) ...
#7 6.501 Selecting previously unselected package libtsan2:amd64.
#7 6.503 Preparing to unpack .../32-libtsan2_12.2.0-14+deb12u1_amd64.deb ...
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#7 6.779 Selecting previously unselected package libquadmath0:amd64.
#7 6.781 Preparing to unpack .../34-libquadmath0_12.2.0-14+deb12u1_amd64.deb ...
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#7 8.472 Preparing to unpack .../41-make_4.3-4.1_amd64.deb ...
#7 8.473 Unpacking make (4.3-4.1) ...
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#7 9.657 Setting up perl-modules-5.36 (5.36.0-7+deb12u3) ...
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#7 9.666 Setting up rpcsvc-proto (1.4.3-1) ...
#7 9.670 Setting up make (4.3-4.1) ...
#7 9.674 Setting up libmpfr6:amd64 (4.2.0-1) ...
#7 9.679 Setting up librtmp1:amd64 (2.4+20151223.gitfa8646d.1-2+b2) ...
#7 9.683 Setting up xz-utils (5.4.1-1) ...
#7 9.689 update-alternatives: using /usr/bin/xz to provide /usr/bin/lzma (lzma) in auto mode
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#7 9.694 Setting up libquadmath0:amd64 (12.2.0-14+deb12u1) ...
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#7 9.716 Setting up libsasl2-2:amd64 (2.1.28+dfsg-10) ...
#7 9.721 Setting up libubsan1:amd64 (12.2.0-14+deb12u1) ...
#7 9.725 Setting up libnsl-dev:amd64 (1.3.0-2) ...
#7 9.730 Setting up libcrypt-dev:amd64 (1:4.4.33-2) ...
#7 9.740 Setting up libasan8:amd64 (12.2.0-14+deb12u1) ...
#7 9.744 Setting up git-man (1:2.39.5-0+deb12u3) ...
#7 9.749 Setting up libssh2-1:amd64 (1.10.0-3+b1) ...
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#7 9.792 Setting up libctf0:amd64 (2.40-2) ...
#7 9.796 Setting up cpp-12 (12.2.0-14+deb12u1) ...
#7 9.801 Setting up libldap-2.5-0:amd64 (2.5.13+dfsg-5) ...
#7 9.806 Setting up perl (5.36.0-7+deb12u3) ...
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#7 9.827 Setting up libdpkg-perl (1.21.22) ...
#7 9.831 Setting up cpp (4:12.2.0-3) ...
#7 9.838 Setting up libcurl4:amd64 (7.88.1-10+deb12u14) ...
#7 9.843 Setting up libc6-dev:amd64 (2.36-9+deb12u13) ...
#7 9.848 Setting up curl (7.88.1-10+deb12u14) ...
#7 9.853 Setting up binutils-x86-64-linux-gnu (2.40-2) ...
#7 9.857 Setting up libstdc++-12-dev:amd64 (12.2.0-14+deb12u1) ...
#7 9.862 Setting up libcurl3-gnutls:amd64 (7.88.1-10+deb12u14) ...
#7 9.866 Setting up binutils (2.40-2) ...
#7 9.871 Setting up dpkg-dev (1.21.22) ...
#7 9.879 Setting up liberror-perl (0.17029-2) ...
#7 9.883 Setting up gcc-12 (12.2.0-14+deb12u1) ...
#7 9.888 Setting up git (1:2.39.5-0+deb12u3) ...
#7 9.899 Setting up g++-12 (12.2.0-14+deb12u1) ...
#7 9.904 Setting up gcc (4:12.2.0-3) ...
#7 9.915 Setting up g++ (4:12.2.0-3) ...
#7 9.919 update-alternatives: using /usr/bin/g++ to provide /usr/bin/c++ (c++) in auto mode
#7 9.922 Setting


Check build result


#15 exporting layers
#15 exporting layers 4.8s done
#15 writing image sha256:58bfd8c0b319a4b1443121d5556769b3476bad0a847622ac16ad3df6995311fb done
#15 naming to asia-southeast1-docker.pkg.dev/mayaresearchgcp/maya-global-backend/maya-global-backend:latest done
#15 DONE 4.8s


Build successful. Pushing to Artifact Registry.


docker push asia-southeast1-docker.pkg.dev/mayaresearchgcp/maya-global-backend/maya-global-backend:latest 2>&1

Push image to Artifact Registry


The push refers to repository [asia-southeast1-docker.pkg.dev/mayaresearchgcp/maya-global-backend/maya-global-backend]
6cb84f447f5a: Preparing
3cd7e2bb361d: Preparing
86783a351fe9: Preparing
1702f83bdeaa: Preparing
c34e54d42c0d: Preparing
554c8b603fe0: Preparing
ba1a6f68ad2e: Preparing
595ceeb9bedc: Preparing
de4a30746dfc: Preparing
6377c98f81fe: Preparing
f865e2c8d403: Preparing
6143ee9e3de0: Preparing
ba1a6f68ad2e: Waiting
595ceeb9bedc: Waiting
de4a30746dfc: Waiting
6377c98f81fe: Waiting
f865e2c8d403: Waiting
6143ee9e3de0: Waiting
554c8b603fe0: Waiting
6cb84f447f5a: Pushed
3cd7e2bb361d: Pushed
86783a351fe9: Pushed
1702f83bdeaa: Pushed
ba1a6f68ad2e: Pushed
554c8b603fe0: Pushed
de4a30746dfc: Pushed
f865e2c8d403: Pushed
6377c98f81fe: Pushed
6143ee9e3de0: Pushed
595ceeb9bedc: Pushed
c34e54d42c0d: Pushed
latest: digest: sha256:54ee00bf47e26013c3504a6d7af34f6cf215024813a415a0e4cbf4e8351a09a0 size: 2830


Image pushed. Now deploying to GKE.


kubectl apply -f deployments/k8s/gke/deployment.yaml 2>&1

Apply deployment, service, and HPA


kubectl apply -f deployments/k8s/gke/managed-cert.yaml && kubectl apply -f deployments/k8s/gke/ingress.yaml 2>&1

Apply managed cert and ingress (creates LB)


deployment.apps/maya-global-backend created
service/maya-global-backend created
horizontalpodautoscaler.autoscaling/maya-global-backend-hpa created


managedcertificate.networking.gke.io/maya-global-backend-cert created
Warning: annotation "kubernetes.io/ingress.class" is deprecated, please use 'spec.ingressClassName' instead
ingress.networking.k8s.io/maya-global-backend-ingress created
frontendconfig.networking.gke.io/maya-global-backend-frontend created


Everything applied. Let me watch the pods come up.


kubectl get pods -l app=maya-global-backend -w 2>&1

Watch pods starting


Command running in background with ID: b7vnrv4dm. Output is being written to: /tmp/claude-1001/-home-ubuntu/c0539746-4005-4f9b-9479-9ee34a9f1610/tasks/b7vnrv4dm.output


sleep 15 && kubectl get pods -l app=maya-global-backend 2>&1

Check pod status after 15s


NAME                                   READY   STATUS    RESTARTS   AGE
maya-global-backend-6ff78f968f-6w94b   1/1     Running   0          2m29s
maya-global-backend-6ff78f968f-gsm65   1/1     Running   0          2m29s
maya-global-backend-6ff78f968f-hskbd   1/1     Running   0          2m29s
maya-global-backend-6ff78f968f-mtzpd   1/1     Running   0          2m29s
maya-global-backend-6ff78f968f-vths7   1/1     Running   0          2m29s


All 5 pods running. Let me check the full status.


kubectl get svc maya-global-backend 2>&1 && echo "---" && kubectl get hpa maya-global-backend-hpa 2>&1 && echo "---" && kubectl get ingress maya-global-backend-ingress 2>&1 && echo "---" && kubectl get managedcertificate maya-global-backend-cert 2>&1

Check service, HPA, ingress, and cert status


NAME                  TYPE       CLUSTER-IP       EXTERNAL-IP   PORT(S)        AGE
maya-global-backend   NodePort   34.118.227.133   <none>        80:31336/TCP   2m34s
---
NAME                      REFERENCE                        TARGETS                       MINPODS   MAXPODS   REPLICAS   AGE
maya-global-backend-hpa   Deployment/maya-global-backend   cpu: 0%/70%, memory: 3%/80%   5         7         5          2m34s
---
NAME                          CLASS    HOSTS                    ADDRESS   PORTS   AGE
maya-global-backend-ingress   <none>   global.mayaresearch.ai             80      2m30s
---
NAME                       AGE     STATUS
maya-global-backend-cert   2m33s   Provisioning


Deployment is live. Here's the status:

| Component | Status |
|---|---|
| **Pods** | 5/5 Running |
| **HPA** | Active (CPU 0%, Memory 3%) |
| **Service** | NodePort on 80 |
| **Ingress/LB** | Provisioning (takes ~5 min to get IP) |
| **SSL Cert** | Provisioning (takes ~15-30 min after DNS resolves) |

The Google Cloud Load Balancer is being created. Once the ingress gets the IP `34.107.229.195` assigned and the managed cert provisions, `https://global.mayaresearch.ai` will be live with SSL.

You can monitor cert status with:
```bash
kubectl get managedcertificate maya-global-backend-cert
```


check now


kubectl get pods -l app=maya-global-backend 2>&1 && echo "---" && kubectl get ingress maya-global-backend-ingress 2>&1 && echo "---" && kubectl get managedcertificate maya-global-backend-cert 2>&1

Check deployment status


NAME                                   READY   STATUS    RESTARTS   AGE
maya-global-backend-6ff78f968f-6w94b   1/1     Running   0          5m35s
maya-global-backend-6ff78f968f-gsm65   1/1     Running   0          5m35s
maya-global-backend-6ff78f968f-hskbd   1/1     Running   0          5m35s
maya-global-backend-6ff78f968f-mtzpd   1/1     Running   0          5m35s
maya-global-backend-6ff78f968f-vths7   1/1     Running   0          5m35s
---
NAME                          CLASS    HOSTS                    ADDRESS   PORTS   AGE
maya-global-backend-ingress   <none>   global.mayaresearch.ai             80      5m30s
---
NAME                       AGE     STATUS
maya-global-backend-cert   5m33s   Provisioning


kubectl describe ingress maya-global-backend-ingress 2>&1 | tail -20

Check ingress details


Labels:           <none>
Namespace:        default
Address:
Ingress Class:    <none>
Default backend:  maya-global-backend:80 (10.40.0.14:7860,10.40.0.13:7860,10.40.0.17:7860 + 2 more...)
Rules:
  Host                    Path  Backends
  ----                    ----  --------
  global.mayaresearch.ai
                          /   maya-global-backend:80 (10.40.0.14:7860,10.40.0.13:7860,10.40.0.17:7860 + 2 more...)
Annotations:              ingress.gcp.kubernetes.io/pre-shared-cert: mcrt-aad7a423-ab6a-41f7-96f9-ab7057291833
                          kubernetes.io/ingress.class: gce
                          kubernetes.io/ingress.global-static-ip-name: maya-global-backend-ip
                          networking.gke.io/managed-certificates: maya-global-backend-cert
                          networking.gke.io/v1beta1.FrontendConfig: maya-global-backend-frontend
Events:
  Type     Reason  Age                     From                     Message
  ----     ------  ----                    ----                     -------
  Normal   Sync    5m35s (x3 over 5m36s)   loadbalancer-controller  Scheduled for sync
  Warning  Sync    2m31s (x16 over 5m34s)  loadbalancer-controller  Error syncing to GCP: error running backend syncing routine: error ensuring health check: googleapi: Error 400: Invalid value for field 'resource.timeoutSec': '10'. TimeoutSec should be less than checkIntervalSec., invalid


Health check config issue. The check interval and timeout are conflicting. Let me fix the ingress health check annotations.


    38→        livenessProbe:
    39→          httpGet:
    40→            path: /health
    41→            port: 7860
    42→          initialDelaySeconds: 30
    43→          periodSeconds: 15
    44→          timeoutSeconds: 15
    45→        readinessProbe:
    46→          httpGet:
    47→            path: /health
    48→            port: 7860
    49→          initialDelaySeconds: 15
    50→          periodSeconds: 5
    51→          timeoutSeconds: 10
    52→          failureThreshold: 3
    53→          successThreshold: 1
    54→        envFrom:
    55→        - secretRef:
    56→            name: maya-global-env-secrets
    57→        env:

        livenessProbe:
          httpGet:
            path: /health
            port: 7860
          initialDelaySeconds: 30
          periodSeconds: 15
          timeoutSeconds: 15
        readinessProbe:
          httpGet:
            path: /health
            port: 7860
          initialDelaySeconds: 15
          periodSeconds: 5
          timeoutSeconds: 10
          failureThreshold: 3
          successThreshold: 1
        envFrom:
        - secretRef:
            name: maya-global-env-secrets
        env:


GCE LB uses the readiness probe to create its health check. `timeoutSec` (10) must be less than `checkIntervalSec` (5) — that's the conflict. Fix: make periodSeconds > timeoutSeconds on the readiness probe.


Edit

        readinessProbe:
          httpGet:
            path: /health
            port: 7860
          initialDelaySeconds: 15
          periodSeconds: 5
          timeoutSeconds: 10

        readinessProbe:
          httpGet:
            path: /health
            port: 7860
          initialDelaySeconds: 15
          periodSeconds: 15
          timeoutSeconds: 5


The file /home/ubuntu/global_backend/deployments/k8s/gke/deployment.yaml has been updated successfully.

        readinessProbe:
          httpGet:
            path: /health
            port: 7860
          initialDelaySeconds: 15
          periodSeconds: 15
          timeoutSeconds: 5

apiVersion: apps/v1
kind: Deployment
metadata:
  name: maya-global-backend
  labels:
    app: maya-global-backend
spec:
  replicas: 5
  strategy:
    type: RollingUpdate
    rollingUpdate:
      maxSurge: 25%
      maxUnavailable: 0
  selector:
    matchLabels:
      app: maya-global-backend
  template:
    metadata:
      labels:
        app: maya-global-backend
    spec:
      terminationGracePeriodSeconds: 30
      imagePullSecrets:
      - name: ar-pull-secret
      containers:
      - name: maya-global-backend
        image: asia-southeast1-docker.pkg.dev/mayaresearchgcp/maya-global-backend/maya-global-backend:latest
        imagePullPolicy: Always
        ports:
        - containerPort: 7860
        resources:
          requests:
            memory: "6Gi"
            cpu: "2000m"
          limits:
            memory: "8Gi"
            cpu: "8000m"
        livenessProbe:
          httpGet:
            path: /health
            port: 7860
          initialDelaySeconds: 30
          periodSeconds: 15
          timeoutSeconds: 15
        readinessProbe:
          httpGet:
            path: /health
            port: 7860
          initialDelaySeconds: 15
          periodSeconds: 5
          timeoutSeconds: 10
          failureThreshold: 3
          successThreshold: 1
        envFrom:
        - secretRef:
            name: maya-global-env-secrets
        env:
        - name: POD_NAME
          valueFrom:
            fieldRef:
              fieldPath: metadata.name
        volumeMounts:
        - name: gcp-creds
          mountPath: /app/gcp-service-account.json
          subPath: gcp-service-account.json
          readOnly: true
        - name: gcp-creds
          mountPath: /app/google-play-service-account.json
          subPath: google-play-service-account.json
          readOnly: true
      volumes:
      - name: gcp-creds
        secret:
          secretName: gcp-credentials
---
apiVersion: v1
kind: Service
metadata:
  name: maya-global-backend
  labels:
    app: maya-global-backend
spec:
  type: NodePort
  ports:
  - port: 80
    targetPort: 7860
    protocol: TCP
  selector:
    app: maya-global-backend
---
apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: maya-global-backend-hpa
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: maya-global-backend
  minReplicas: 5
  maxReplicas: 7
  metrics:
  - type: Resource
    resource:
      name: cpu
      target:
        type: Utilization
        averageUtilization: 70
  - type: Resource
    resource:
      name: memory
      target:
        type: Utilization
        averageUtilization: 80
  behavior:
    scaleUp:
      stabilizationWindowSeconds: 30
      policies:
      - type: Pods
        value: 2
        periodSeconds: 30
    scaleDown:
      stabilizationWindowSeconds: 300
      policies:
      - type: Pods
        value: 1
        periodSeconds: 60

path: /health
port: 7860
initialDelaySeconds: 15
-          periodSeconds: 5
-          timeoutSeconds: 10
+          periodSeconds: 15
+          timeoutSeconds: 5
failureThreshold: 3
successThreshold: 1
envFrom:


Bash

kubectl apply -f deployments/k8s/gke/deployment.yaml 2>&1

Re-apply deployment with fixed health check


deployment.apps/maya-global-backend configured
service/maya-global-backend unchanged
horizontalpodautoscaler.autoscaling/maya-global-backend-hpa unchanged


sleep 20 && kubectl describe ingress maya-global-backend-ingress 2>&1 | grep -A5 "Events:"

Check if ingress sync error resolved


Events:
  Type     Reason  Age                   From                     Message
  ----     ------  ----                  ----                     -------
  Normal   Sync    37s (x4 over 6m17s)   loadbalancer-controller  Scheduled for sync
  Warning  Sync    27s (x18 over 6m15s)  loadbalancer-controller  Error syncing to GCP: error running backend syncing routine: error ensuring health check: googleapi: Error 400: Invalid value for field 'resource.timeoutSec': '10'. TimeoutSec should be less than checkIntervalSec., invalid


Old health check is cached. Let me delete the ingress and re-create to force it to pick up the new probe config.


kubectl delete ingress maya-global-backend-ingress 2>&1 && sleep 5 && kubectl apply -f deployments/k8s/gke/ingress.yaml 2>&1

Delete and re-create ingress to pick up new health check


ingress.networking.k8s.io "maya-global-backend-ingress" deleted from default namespace
Warning: annotation "kubernetes.io/ingress.class" is deprecated, please use 'spec.ingressClassName' instead
ingress.networking.k8s.io/maya-global-backend-ingress created
frontendconfig.networking.gke.io/maya-global-backend-frontend unchanged


sleep 30 && kubectl describe ingress maya-global-backend-ingress 2>&1 | grep -E "Address:|Events:" -A 10

Check ingress after re-create


Address:
Ingress Class:    <none>
Default backend:  maya-global-backend:80 (10.40.0.18:7860,10.40.0.19:7860,10.40.0.20:7860 + 2 more...)
Rules:
  Host                    Path  Backends
  ----                    ----  --------
  global.mayaresearch.ai
                          /   maya-global-backend:80 (10.40.0.18:7860,10.40.0.19:7860,10.40.0.20:7860 + 2 more...)
Annotations:              ingress.gcp.kubernetes.io/pre-shared-cert: mcrt-aad7a423-ab6a-41f7-96f9-ab7057291833
                          kubernetes.io/ingress.class: gce
                          kubernetes.io/ingress.global-static-ip-name: maya-global-backend-ip
--
Events:
  Type     Reason  Age                From                     Message
  ----     ------  ----               ----                     -------
  Normal   Sync    39s (x3 over 39s)  loadbalancer-controller  Scheduled for sync
  Warning  Sync    28s (x7 over 38s)  loadbalancer-controller  Error syncing to GCP: error running backend syncing routine: error ensuring health check: googleapi: Error 400: Invalid value for field 'resource.timeoutSec': '10'. TimeoutSec should be less than checkIntervalSec., invalid


The old GCP health check resource is persisting. Let me delete it directly from GCP.


gcloud compute health-checks list --project=mayaresearchgcp 2>&1

List GCP health checks


NAME                                                   REGION  PROTOCOL
k8s1-46565322-default-maya-global-backend-80-3c034d8d          HTTP
k8s1-5cf08f8d-default-maya-pipecat-80-3bee62a6                 HTTP


gcloud compute health-checks describe k8s1-46565322-default-maya-global-backend-80-3c034d8d --project=mayaresearchgcp --format="table(checkIntervalSec,timeoutSec,httpHealthCheck.requestPath)" 2>&1

Check current health check config


CHECK_INTERVAL_SEC  TIMEOUT_SEC  REQUEST_PATH
15                  5            /health


It already has the correct values (interval 15, timeout 5). The error might be stale. Let me wait and recheck.


sleep 30 && kubectl get ingress maya-global-backend-ingress 2>&1 && echo "---" && kubectl describe ingress maya-global-backend-ingress 2>&1 | tail -8

Recheck ingress after health check fix


NAME                          CLASS    HOSTS                    ADDRESS   PORTS   AGE
maya-global-backend-ingress   <none>   global.mayaresearch.ai             80      87s
---
                          kubernetes.io/ingress.global-static-ip-name: maya-global-backend-ip
                          networking.gke.io/managed-certificates: maya-global-backend-cert
                          networking.gke.io/v1beta1.FrontendConfig: maya-global-backend-frontend
Events:
  Type     Reason  Age                From                     Message
  ----     ------  ----               ----                     -------
  Normal   Sync    89s (x3 over 89s)  loadbalancer-controller  Scheduled for sync
  Warning  Sync    78s (x7 over 88s)  loadbalancer-controller  Error syncing to GCP: error running backend syncing routine: error ensuring health check: googleapi: Error 400: Invalid value for field 'resource.timeoutSec': '10'. TimeoutSec should be less than checkIntervalSec., invalid


It's still referencing `10` despite the health check showing `5`. There must be a second backend health check. Let me check the NEG.


gcloud compute backend-services list --project=mayaresearchgcp 2>&1

List backend services


NAME                                                   BACKENDS                                                                                PROTOCOL
k8s1-46565322-default-maya-global-backend-80-3c034d8d  asia-southeast1-a/networkEndpointGroups/k8s1-46565322-default-maya-global-backend-80-3c034d8d  HTTP
k8s1-5cf08f8d-default-maya-pipecat-80-3bee62a6         asia-south1-a/networkEndpointGroups/k8s1-5cf08f8d-default-maya-pipecat-80-3bee62a6             HTTP


gcloud compute backend-services describe k8s1-46565322-default-maya-global-backend-80-3c034d8d --global --project=mayaresearchgcp --format="yaml(healthChecks,timeoutSec)" 2>&1

Check backend service health check reference


healthChecks:
- https://www.googleapis.com/compute/v1/projects/mayaresearchgcp/global/healthChecks/k8s1-46565322-default-maya-global-backend-80-3c034d8d
timeoutSec: 30


The backend service is created and health check is already correct. The error events might be stale from the old pods. Let me wait for the new pods to fully roll out and check again.


kubectl rollout status deployment/maya-global-backend --timeout=60s 2>&1 && kubectl get pods -l app=maya-global-backend 2>&1

Check rollout and pods