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PV/PVC support in AMLArc training job

Now you can leverage Kubernetes native way to mount various data storage via Persistent Volume (PV) and Persistent Volume Claim (PVC).

  1. Create PV, take NFS as example,
apiVersion: v1
kind: PersistentVolume
metadata:
  name: nfs-pv 
spec:
  capacity:
    storage: 1Gi 
  accessModes:
    - ReadWriteMany 
  persistentVolumeReclaimPolicy: Retain
  storageClassName: ""
  nfs: 
    path: /share/nfs
    server: 20.98.110.84 
    readOnly: false
  1. Cteate PVC. In metadata, add label ml.azure.com/pvc: "true" to indicate the PVC can be mounted to the upcoming training job, and add annotation ml.azure.com/mountpath: <mount path> to specify the mount path.
apiVersion: v1
kind: PersistentVolumeClaim
metadata:
  name: nfs-pvc  
  namespace: default
  labels:
    ml.azure.com/pvc: "true"
  annotations:
    ml.azure.com/mountpath: "/mnt/nfs"
spec:
  storageClassName: ""
  accessModes:
  - ReadWriteMany      
  resources:
     requests:
       storage: 1Gi

How AML will use the PVC

The training job in the same namespace with the PVC will be mounted the volume automatically. Then data scientist can access the mount path in the training job.

By default, the job will be created in default namespace. IT operator can decide/update the namespace in attached compute target configuration.