# Create a simple Python script
cat > my_script.py <<'EOF'
# /// script
# dependencies = ["daft"]
# ///
import daft

df = daft.from_pydict({
    "name": ["Alice", "Bob", "Charlie"],
    "age": [25, 30, 35]
})

df.filter(df["age"] > 25).show()
EOF

# Deploy
helm install my-job oci://ghcr.io/eventual-inc/daft/quickstart \
  --set-file job.script=my_script.py

# View logs
kubectl logs -f job/my-job-quickstart-job

# Cleanup
helm uninstall my-job

# Create a simple Python script with Ray
cat > my_script.py <<'EOF'
# /// script
# dependencies = ["daft", "ray[client]==2.46.0"]
# ///

import daft
import ray

ray.init(runtime_env={"pip": ["daft"]})

df = daft.from_pydict({
    "name": ["Alice", "Bob", "Charlie"],
    "age": [25, 30, 35]
})

df.filter(df["age"] > 25).show()
EOF

# Deploy with Ray cluster
helm install distributed-job oci://ghcr.io/eventual-inc/daft/quickstart \
  --set distributed=true \
  --set worker.replicas=3 \
  --set-file job.script=my_script.py

# Access Ray Dashboard
kubectl port-forward service/distributed-job-quickstart-head 8265:8265
# Open http://localhost:8265

# Cleanup
helm uninstall distributed-job

helm install custom-image-job oci://ghcr.io/eventual-inc/daft/quickstart \
  --set image=my-image:tag \
  --set "job.command={python,my_script.py}"

# Get job status
kubectl get jobs -l app.kubernetes.io/instance=my-release

# View job logs
kubectl logs -f job/my-release-quickstart-job

# Check pod status
kubectl get pods -l app.kubernetes.io/instance=my-release

# Port forward to access both Ray Dashboard and Grafana
kubectl port-forward service/my-release-quickstart-head 8265:8265 3000:3000

# Open http://localhost:8265 in browser
# Grafana panels will be embedded directly in the Ray Dashboard

# Optionally open http://localhost:3000 to access Grafana directly
# Default credentials: admin/admin

# Get cluster status
kubectl exec deployment/my-release-quickstart-head -- ray status

# View cluster resources
kubectl exec deployment/my-release-quickstart-head -- python -c "import ray; ray.init(); print(ray.cluster_resources())"

# Scale workers using kubectl
kubectl scale deployment my-release-quickstart-worker --replicas=10

# Check job events
kubectl describe job my-release-quickstart-job

# Check pod events
kubectl describe pod -l app.kubernetes.io/component=job

# View pod logs
kubectl logs -l app.kubernetes.io/component=job

# Check head service
kubectl get service -l ray.io/node-type=head

# Check head logs
kubectl logs deployment/my-release-quickstart-head

# Check worker logs
kubectl logs deployment/my-release-quickstart-worker

job:
  resources:
    limits:
      memory: "16Gi"  # Increase as needed

head:
  resources:
    limits:
      memory: "32Gi"  # Increase as needed

worker:
  resources:
    limits:
      memory: "32Gi"  # Increase as needed

# Correct
helm install my-job oci://ghcr.io/eventual-inc/daft/quickstart --set-file job.script=script.py

# Incorrect
helm install my-job oci://ghcr.io/eventual-inc/daft/quickstart --set job.script=script.py

# requirements.txt
daft
ray[default]

helm uninstall my-release