Editing Tutorials:Run the example container on the cluster (section)

== Set up a Kubernetes job script ==

Download the [[File:Kubernetes_samples.zip|Kubernetes samples]] and look at the kubernetes subdirectory in example_1. Check out "make_config.sh" and run it after you have set the bash environment variable "KUBERNETES_USER" to your cluster username:

<syntaxhighlight lang="bash">
> export KUBERNETES_USER=your.username
> ./make_configs.sh
</syntaxhighlight>

This will create a number of yaml files (Kubernetes configuration files) from the templates in the "template" subdirectory. Check out the first example, "job-script.yaml":


<syntaxhighlight lang="yaml">
apiVersion: batch/v1
kind: Job
metadata:
  # name of the job
  name: your-username-tf-mnist

spec:
  template:
    spec:
      # List of containers belonging to the job starts here
      containers:
      # container name used for pod creation
      - name: your-username-tf-mnist-container
        # container image from the registry
        image: ccu.uni-konstanz.de:5000/your.username/tf_mnist:0.1

        # container resources requested from the node
        resources:
          # requests are minimum resource requirements
          requests:
            # this gives us a minimum 2 GiB of main memory to work with.
            memory: "2Gi"
            # you should allocate at least 1 CPU for machine learning jobs,
            # usually more if you for example have seperate threads for reading data
            # 1 CPU unit is 1 CPU core or hyperthread, depending on CPU architecture
            # Note that these are typically not a scarce resource on our GPU servers,
            # so you can be a bit generous.
            cpu: 1

          # limits are maximum resource allocations
          limits:
            # this gives an absolute limit of 3 GiB of main memory.
            # exceeding it will mean the container exits immediately with an error.
            memory: "3Gi"

            # CPU limit, but pod will usually not be killed for excessive CPU use
            cpu: 1

            # this requests a number of GPUs. GPUs will be allocated to the container
            # exclusively. No fractional GPUs can be requested.
            # When executing nvidia-smi in the container, it should show exactly this
            # number of GPUs.
            #
            # PLEASE DO NOT SET THE NUMBER TO ZERO, EVER, AND ALWAYS INCLUDE THIS LINE.
            # ALWAYS PUT IT IN THE SECTION "limits", NOT "requests".
            #
            # It is a known limitation of nVidias runtime that if zero GPUs are requested,
            # then actually *all* GPUs are exposed in the container.
            # We are looking for a fix to this.
            #
            nvidia.com/gpu: "1"

        # the command which is executed after container creation
        command: ["/application/run.sh"]


      # login credentials to the docker registry.
      # for convenience, a readonly credential is provided as a secret in each namespace.
      imagePullSecrets:
      - name: registry-ro-login

      # containers will never restart
      restartPolicy: Never

  # number of retries after failure.
  # since we typically have to fix something in this case, set to zero by default.
  backoffLimit: 0</syntaxhighlight>

When we start this job, it will create a single container based on the image we previously uploaded to the registry on a suitable node which serves the selected namespace of the cluster.

<syntaxhighlight lang="yaml">
> kubectl apply -f job-script.yaml
</syntaxhighlight>