```
./bin/spark-submit --class org.apache.spark.examples.SparkPi \
    --master yarn-client \
    --num-executors 1 \
    --driver-memory 512m \
    --executor-memory 512m \
    --executor-cores 1 \
    examples/jars/spark-examples*.jar 10
```

The job should produce an output as shown below. Note the value of pi in the output.

```

	}

	cache := store.lock()
	defer store.unlock()

	// We should only update the in-memory cache if there were no changes
	// to the in-memory cache since the disk loading began. If there
	// were changes to the in-memory cache we should wait for the next
	// cycle until we can safely update the in-memory cache.
	//
	// An in-memory cache must be replaced only if we know for sure that the

                },
                "memory": {
                  "type": "string"
                }
              }
            },
            "requests": {
              "type": "object",
              "properties": {
                "cpu": {
                  "type": "string"
                },
                "memory": {
                  "type": "string"
                }

    ## Whether to automatically allocate NodePorts (only for LoadBalancers).
    # allocateLoadBalancerNodePorts: false

  resources:
    requests:
      cpu: 100m
      memory: 128Mi
    limits:
      cpu: 2000m
      memory: 1024Mi

  autoscaling:
    enabled: true
    minReplicas: 1
    maxReplicas: 5
    targetCPUUtilizationPercentage: 80
    targetMemoryUtilizationPercentage: {}

podAnnotations: {}

# Additional pod labels
podLabels: {}

## Configure resource requests and limits
## ref: http://kubernetes.io/docs/user-guide/compute-resources/
##
resources:
  requests:
    memory: 16Gi

## List of policies to be created after minio install
##
## In addition to default policies [readonly|readwrite|writeonly|consoleAdmin|diagnostics]

   - joker-eph
   - sanjoy
# filesystem path
filesystem_path:
   - tensorflow/c/experimental/filesystem
# security path
security_path:
   - tensorflow/security
# words checklist
segfault_memory:
   - segfault
   - memory leaks
# assignees
filesystem_security_assignee:
   - mihaimaruseac
   
# Cuda Comment
cuda_comment: >
   From the template it looks like you are installing **TensorFlow** (TF) prebuilt binaries:

  // Create a tensor instance from the given data buffer and description.
  // `memory_releaser` will be called on destruction, and it's responsible for
  // cleaning up the underlying buffer.
  virtual AbstractTensorInterface* CreateTensor(
      DataType dtype, const int64_t* dims, int num_dims, void* data, size_t len,
      MemoryReleaser memory_releaser, void* memory_releaser_arg) = 0;

  // Create a handle to wrap and manage a Tensor

    # block in the relevant section below and setting the desired resources values.
    defaultResources:
      requests:
        cpu: 10m
      #   memory: 128Mi
      # limits:
      #   cpu: 100m
      #   memory: 128Mi
    # Default hub for Istio images.
    # Releases are published to docker hub under 'istio' project.
    # Dev builds from prow are on gcr.io
    hub: gcr.io/istio-testing

    Fire<V> fire = new Fire<>(result);
    result.timer = scheduledExecutor.schedule(fire, time, unit);
    delegate.addListener(fire, directExecutor());
    return result;
  }

  /*
   * Memory visibility of these fields. There are two cases to consider.
   *
   * 1. visibility of the writes to these fields to Fire.run:
   *
   * The initial write to delegateRef is made definitely visible via the semantics of

// `enable_xla_compilation` is non-zero, and OFF otherwise.
// b) ConfigProto.gpu_options.allow_growth is set to `gpu_memory_allow_growth`.
// c) ConfigProto.device_count is set to `num_cpu_devices`.
TF_CAPI_EXPORT extern TF_Buffer* TF_CreateConfig(
    unsigned char enable_xla_compilation, unsigned char gpu_memory_allow_growth,
    unsigned int num_cpu_devices);

// Create a serialized tensorflow.RunOptions proto, where RunOptions.trace_level