1. Check the status of your Daft deployment:

   kubectl get pods -l app.kubernetes.io/instance={{ .Release.Name }}

{{- if .Values.distributed }}

2. Your Daft cluster is running in distributed mode with Ray:

   Ray Head:     {{ include "daft.fullname" . }}-head
   Ray Workers:  {{ include "daft.fullname" . }}-worker ({{ .Values.worker.replicas }} replicas)
   Job:          {{ include "daft.fullname" . }}-job
   {{- if .Values.job.script }}
   Script:       {{ include "daft.fullname" . }}-job-script (mounted at /usr/src/daft/main.py)
   {{- end }}

3. Monitor the Ray cluster status:

   kubectl logs deployment/{{ include "daft.fullname" . }}-head -c ray-head

4. Access the Ray dashboard (when ready):
{{- if .Values.monitoring.enabled }}

   kubectl port-forward service/{{ include "daft.headServiceName" . }} 8265:8265 3000:3000

   Ray Dashboard     : http://localhost:8265
   Grafana (optional): http://localhost:3000 (admin/admin)
{{- else }}

   kubectl port-forward service/{{ include "daft.headServiceName" . }} 8265:8265

   Ray Dashboard: http://localhost:8265
{{- end }}

5. Check your job status:

   kubectl get job {{ include "daft.fullname" . }}-job
   kubectl logs job/{{ include "daft.fullname" . }}-job

{{- else }}

2. Your Daft job is running in simple mode (no Ray cluster):

   Job: {{ include "daft.fullname" . }}-job
   {{- if .Values.job.script }}
   Script: {{ include "daft.fullname" . }}-job-script (mounted at /usr/src/daft/main.py)
   {{- end }}

3. Check your job status:

   kubectl get job {{ include "daft.fullname" . }}-job
   kubectl logs job/{{ include "daft.fullname" . }}-job

{{- end }}

{{- if not .Values.job.script }}

To inject a Python script directly into the job, use:
  helm install my-job ./daft --set-file job.script=my_script.py

{{- end }}

For more information, visit the Daft documentation.
