test workload for GPU schedule with nodeAffinity

pod.yaml

kind: Pod
apiVersion: v1
metadata:
  name: gpu-pod
spec:
  affinity:
    nodeAffinity:
      requiredDuringSchedulingIgnoredDuringExecution:
        nodeSelectorTerms:
        - matchExpressions:
          - key: alpha.kubernetes.io/nvidia-gpu-name
            operator: In
            values:
            - Tesla_K80
  containers:
  - name: gpu-container
    image: gcr.io/tensorflow/tensorflow:latest-gpu
    imagePullPolicy: Always
    command: ["python"]
    args:
      - -u
      - -c
      - |
        import tensorflow as tf;a = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[2, 3], name='a');b = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[3, 2], name='b');c = tf.matmul(a, b);sess = tf.Session(config=tf.ConfigProto(log_device_placement=True));print(sess.run(c))
    resources:
      requests:
        alpha.kubernetes.io/nvidia-gpu: 1
      limits:
        alpha.kubernetes.io/nvidia-gpu: 1
    volumeMounts:
    - name: nvidia-driver-375-66
      mountPath: /usr/local/nvidia
      readOnly: true
    - name: libcuda-so
      mountPath: /usr/lib/x86_64-linux-gnu/libcuda.so
    - name: libcuda-so-1
      mountPath: /usr/lib/x86_64-linux-gnu/libcuda.so.1
    - name: libcuda-so-375-66
      mountPath: /usr/lib/x86_64-linux-gnu/libcuda.so.375.66
  restartPolicy: Never
  volumes:
  - name: nvidia-driver-375-66
    hostPath:
      path: /opt/nvidia/current
  - name: libcuda-so
    hostPath:
      path: /opt/nvidia/current/lib/libcuda.so
  - name: libcuda-so-1
    hostPath:
      path: /opt/nvidia/current/lib/libcuda.so.1
  - name: libcuda-so-375-66
    hostPath:
      path: /opt/nvidia/current/lib/libcuda.so.375.66