CHART NAME: {{ .Chart.Name  }}
CHART VERSION: {{ .Chart.Version  }}
APP VERSION: {{ .Chart.AppVersion  }}

** Please be patient while the chart is being deployed **

{{- if .Values.diagnosticMode.enabled }}
The chart has been deployed in diagnostic mode. All probes have been disabled and the command has been overwritten with:

  command: {{- include "common.tplvalues.render" (dict "value" .Values.diagnosticMode.command "context" $) | nindent 4 }}
  args: {{- include "common.tplvalues.render" (dict "value" .Values.diagnosticMode.args "context" $) | nindent 4 }}

Get the list of pods by executing:

  kubectl get pods --namespace {{ include "common.names.namespace" . | quote }} -l app.kubernetes.io/instance={{ .Release.Name }}

Access the pod you want to debug by executing

  kubectl exec --namespace {{ include "common.names.namespace" . | quote }} -ti <NAME OF THE POD> -- bash

{{- else }}

{{- if .Values.run.enabled }}
{{- if .Values.run.source.launchCommand }}
The following command will be executed:

  {{- include "common.tplvalues.render" (dict "value" .Values.run.source.launchCommand "context" $) | nindent 2 }}

You can see the logs of each running node with:
    kubectl logs [POD_NAME]

and the list of pods:
    kubectl get pods --namespace {{ include "common.names.namespace" . }} -l "app.kubernetes.io/name={{ include "common.names.name" . }},app.kubernetes.io/instance={{ .Release.Name }}"
{{- else }}
You didn't specify any entrypoint to your code.
To run it, you can either deploy again using the `source.launchCommand` option to specify your entrypoint, or execute it manually by jumping into the pods:

1. Get the running pods
    kubectl get pods --namespace {{ include "common.names.namespace" . }} -l "app.kubernetes.io/name={{ include "common.names.name" . }},app.kubernetes.io/instance={{ .Release.Name }}"

2. Get into a pod
    kubectl exec -ti [POD_NAME] bash

3. Execute your script as you would normally do.
{{- end }}

{{- end }}

{{- if .Values.tracking.enabled }}
MLflow Tracking Server can be accessed through the following DNS name from within your cluster:

    {{ include "mlflow.v0.tracking.fullname" . }}.{{ .Release.Namespace }}.svc.{{ .Values.clusterDomain }} (port {{ include "mlflow.v0.tracking.port" . }})

To access your MLflow site from outside the cluster follow the steps below:

{{- if .Values.tracking.ingress.enabled }}

1. Get the MLflow URL and associate MLflow hostname to your cluster external IP:

   export CLUSTER_IP=$(minikube ip) # On Minikube. Use: `kubectl cluster-info` on others K8s clusters
   echo "MLflow URL: http{{ if .Values.tracking.ingress.tls }}s{{ end }}://{{ .Values.tracking.ingress.hostname }}/"
   echo "$CLUSTER_IP  {{ .Values.tracking.ingress.hostname }}" | sudo tee -a /etc/hosts

{{- else }}
{{- $port := include "mlflow.v0.tracking.port" . | toString }}

1. Get the MLflow URL by running these commands:

{{- if contains "NodePort" .Values.tracking.service.type }}

   export NODE_PORT=$(kubectl get --namespace {{ .Release.Namespace }} -o jsonpath="{.spec.ports[0].nodePort}" services {{ include "mlflow.v0.tracking.fullname" . }})
   export NODE_IP=$(kubectl get nodes --namespace {{ .Release.Namespace }} -o jsonpath="{.items[0].status.addresses[0].address}")
   echo "MLflow URL: {{ include "mlflow.v0.tracking.protocol" . }}://$NODE_IP:$NODE_PORT/"

{{- else if contains "LoadBalancer" .Values.tracking.service.type }}

  NOTE: It may take a few minutes for the LoadBalancer IP to be available.
        Watch the status with: 'kubectl get svc --namespace {{ .Release.Namespace }} -w {{ include "mlflow.v0.tracking.fullname" . }}'

   export SERVICE_IP=$(kubectl get svc --namespace {{ .Release.Namespace }} {{ include "mlflow.v0.tracking.fullname" . }} --template "{{ "{{ range (index .status.loadBalancer.ingress 0) }}{{ . }}{{ end }}" }}")
   echo "MLflow URL: {{ include "mlflow.v0.tracking.protocol" . }}://$SERVICE_IP{{- if ne $port "80" }}:{{ include "mlflow.v0.tracking.port" . }}{{ end }}/"

{{- else if contains "ClusterIP"  .Values.tracking.service.type }}

   kubectl port-forward --namespace {{ .Release.Namespace }} svc/{{ include "mlflow.v0.tracking.fullname" . }} {{ include "mlflow.v0.tracking.port" . }}:{{ include "mlflow.v0.tracking.port" . }} &
   echo "MLflow URL: {{ include "mlflow.v0.tracking.protocol" . }}://127.0.0.1{{- if ne $port "80" }}:{{ include "mlflow.v0.tracking.port" . }}{{ end }}//"

{{- end }}
{{- end }}

2. Open a browser and access MLflow using the obtained URL.

{{- if .Values.tracking.enabled }}
3. Login with the following credentials below to see your blog:

  echo Username: $(kubectl get secret --namespace {{ .Release.Namespace }} {{ include "mlflow.v0.tracking.fullname" . }} -o jsonpath="{ .data.{{ include "mlflow.v0.tracking.userKey" . }} }" | base64 -d)
  echo Password: $(kubectl get secret --namespace {{ .Release.Namespace }} {{ include "mlflow.v0.tracking.fullname" . }} -o jsonpath="{.data.{{ include "mlflow.v0.tracking.passwordKey" . }} }" | base64 -d)
{{- end }}
{{- end }}
{{- end }}

{{- include "common.warnings.rollingTag" .Values.image }}
{{- include "mlflow.v0.validateValues" . }}
{{- include "common.warnings.resources" (dict "sections" (list "run" "tracking" "volumePermissions") "context" $) }}
{{- include "common.warnings.modifiedImages" (dict "images" (list .Values.image .Values.gitImage .Values.volumePermissions.image .Values.waitContainer.image) "context" $) }}