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Version: 0.0.1

Developer Guide

This guide is for developers who deploy workloads and consume resources within team projects.

Responsibilities​

As a developer, you are responsible for:

  • ✅ Deploying applications within your project
  • ✅ Monitoring resource usage
  • ✅ Staying within quota limits
  • ✅ Optimizing resource consumption

Getting Started​

1. Get Kubeconfig​

Request kubeconfig from your team leader or administrator.

2. Set Context​

# Set context to your project namespace
kubectl config set-context --current --namespace=your-project

# Verify
kubectl config view --minify | grep namespace

3. Check Quota​

See your available resources:

kubectl describe quota

Deploying Workloads​

Basic Pod Deployment​

apiVersion: v1
kind: Pod
metadata:
  name: gpu-training-job
  namespace: your-project
spec:
  containers:
  - name: trainer
    image: your-ml-image:latest
    resources:
      requests:
        cpu: "4"
        memory: "16Gi"
        nvidia.com/gpu: "1"
      limits:
        cpu: "4"
        memory: "16Gi"
        nvidia.com/gpu: "1"

Using Deployments​

apiVersion: apps/v1
kind: Deployment
metadata:
  name: ml-inference
  namespace: your-project
spec:
  replicas: 2
  selector:
    matchLabels:
      app: ml-inference
  template:
    metadata:
      labels:
        app: ml-inference
    spec:
      containers:
      - name: inference
        image: your-inference-image:latest
        resources:
          requests:
            cpu: "2"
            memory: "8Gi"
            nvidia.com/gpu: "1"

Monitoring Usage​

Check Pod Resource Usage​

# View resource consumption
kubectl top pods

# Detailed pod information
kubectl describe pod <pod-name>

View Logs​

# Stream logs
kubectl logs -f <pod-name>

# Previous logs (if pod restarted)
kubectl logs --previous <pod-name>

Best Practices​

Resource Requests and Limits​

Always specify both requests and limits:

resources:
  requests:
    cpu: "2"
    memory: "8Gi"
  limits:
    cpu: "4"
    memory: "16Gi"

GPU Usage​

  • Request GPUs only when needed
  • Use GPU for compute-intensive tasks
  • Monitor GPU utilization

Clean Up​

Delete resources when no longer needed:

# Delete pod
kubectl delete pod <pod-name>

# Delete deployment
kubectl delete deployment <deployment-name>

# Clean up completed jobs
kubectl delete job --field-selector status.successful=1

Cost Optimization​

  • Right-size your resource requests
  • Use horizontal pod autoscaling
  • Clean up idle resources
  • Share GPUs when possible (if supported)

Troubleshooting​

Pod Pending (Insufficient Quota)​

If your pod is stuck in Pending state:

kubectl describe pod <pod-name>

Look for quota-related errors and reduce resource requests or ask your team leader for more quota.

Out of Memory (OOM)​

If pods are killed due to OOM:

  1. Check memory usage patterns
  2. Increase memory limits
  3. Optimize application memory usage

GPU Not Available​

Verify GPU requests:

kubectl get nodes -o custom-columns=NAME:.metadata.name,GPU:.status.allocatable."nvidia\.com/gpu"

Next Steps​