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 "example_2" of the tutorial sample code, File:Kubernetes samples.zip. As a first step, run the docker-compose and push the resulting container to the CCU registry,

> docker-compose up --build
[Wait a bit until the program has started, then ^C]
> docker push ccu.uni-konstanz.de:5000/your.username/tf-mnist-tb:0.1

Also, create the Kubernetes scripts as before in the kubernetes subdirectory. Then, check out "pvc.yaml".

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
...

We will not cover the details of Tensorboard logging here, see the example code in "application/src/tf_mnist.py" for some initial ideas. Make sure to provide the correct log directory when creating the writer instance for the logs. I suggest to create a new subdirectory for each run of the program and hold the PVC, so that you can compare different runs, like this:

from datetime import datetime

tb_base_directory = "/mnt/tensorboard/"
now = datetime.now()
subdir = now.strftime("%Y%m%d-%H%M%S")
tb_out_directory = tb_base_directory + subdir
writer = tf.summary.FileWriter( tb_out_directory, sess.graph )

Otherwise, please refer to some of the excellent online tutorials on Tensorboard, e.g. this here.

Viewing the Tensorboard of the job

First, find out the compute node your pod was allocated to.

> kubectl get pods | grep your-username
NAME                                  READY   STATUS      RESTARTS   AGE
your-username-tf-mnist-tb-pvc-mqt9m   1/1     Running     0          3m4s

> kubectl describe pod your-username-tf-mnist-tb-pvc-mqt9m | grep Node
Node:               glasya/134.34.226.30

Your pod is running on Glasya, IP 134.34.226.30. You can now point your browser to 134.34.226.30:6116 to access the Tensorboard instance for the node. Note that it lists the logs for all currently mounted PVs. To find out which directory your PV corresponds to, you need to check which PV your PVC was bound to, and inspect its data:

> kubectl get pvc | grep your-username
your-username-tf-mnist-tb-pvc   Bound    local-pv-d07aa16c   25Gi       RWO            local-tensorboard   19m

> kubectl describe pv local-pv-d07aa16c | grep Path
Path:  /mnt/tensorboard/glasya-pv-tb-25gb-2

This means that your logs will be the ones prefixed by "glasya-pv-tb-25gb-2" in the Tensorboard instance.