Tutorials:Monitoring with Tensorboard on the GPU cluster

Tensorboard support on the GPU cluster

Tensorboard is a monitoring tool for machine learning training, which provides a web browser interface on a port of the server (6116 in our cluster). Each compute node has its own instance of Tensorboard running, which is exposed on node-domain:6116. Tensorboard parses the content of a particular directory of the node. Subdirectories can be mounted as the persistent volume storage class "local-tensorboard" and used to write logs.

Local persistent volumes for Tensorboard logging

The following obtains a persistent volume claim for a local PV for data storage, as well as a PV for Tensorboard logging. Note that both can be done with a single config file. Code examples can be found in the subdirectory "kubernetes/example_3" of the tutorial sample code, File:Kubernetes samples.zip.

apiVersion: v1
kind: PersistentVolumeClaim
metadata:
  # the name of the PVC, we refer to this in the container configuration
  name: your-username-tf-mnist-pvc

spec:
  resources:
    requests:
      # storage resource request. This PVC can only be bound to volumes which
      # have at least 8 GiB of storage available.
      storage: 8Gi

  # the requested storage class here is fast data storage.
  storageClassName: local-ssd

  # leave these unchanged, they must match the PV type, otherwise binding will fail
  accessModes:
    - ReadWriteOnce
  volumeMode: Filesystem
---
apiVersion: v1
kind: PersistentVolumeClaim
metadata:
  # the second claim is for tensorboard logging, it needs its own ID.
  name: your-username-tf-mnist-tb-pvc

spec:
  resources:
    requests:
      # Tensorboard logging typically requires not that much storage.
      storage: 2Gi

  # this storage class is parsed by the local Tensorboard instance
  # exposed to the network at port 6116.
  storageClassName: local-tensorboard

  # leave these unchanged, they must match the PV type, otherwise binding will fail
  accessModes:
    - ReadWriteOnce
  volumeMode: Filesystem

Remember to prepend names with your username to make them unique. When the claim is defined to your satisfaction, apply it like this:

> kubectl apply -f pvc.yml

You can again check on the status of this (and every other) claim:

> kubectl get pvc
NAME              STATUS    VOLUME   CAPACITY   ACCESS MODES   STORAGECLASS        AGE
tf-mnist-pvc      Pending                                      local-ssd           11s
tf-mnist-tb-pvc   Pending                                      local-tensorboard   11s

Since the claim has not been used by a container yet, it is not yet bound to a persitent volume (PV). The contents of the PV can be accessed like any other PV, see previous tutorial.

Logging to Tensorboard from within your container

In your job file, make sure both PVC are mounted to the container. We use "/mnt/tensorboard" as the mount point for the tensorboard log directory.

...
       containers:
       - name: your-username-tf-mnist-tb
        volumeMounts:
        - mountPath: "/tmp/data"
          name: pvc-mnist
        - mountPath: "/mnt/tensorboard"
          name: pvc-mnist-tb
...
      volumes:
        - name: pvc-mnist
          persistentVolumeClaim:
            claimName: test-user-tf-mnist-pvc
        - name: pvc-mnist-tb
          persistentVolumeClaim:
            claimName: test-user-tf-mnist-tb-pvc
...