Ray on VKE场景下，可以通过采用RayJob方式提交Ray作业。使用RayJob时，KubeRay会自动创建一个RayCluster，并在集群就绪时提交Ray作业。同时也支持在Ray作业结束后自动删除RayCluster。
本章节详细介绍了Rayjob的使用示例，在该示例中执行一个简单的python代码段，以便更快的上手和使用。

1 准备条件

1. 部署EMR on VKE产品中Ray服务。
2. 通过kubectl命令运行RayJob作业，需要安装kubectl工具（参考安装和设置 kubectl），且配置火山引擎VKE集群的连接信息（参考连接VKE集群）。

2 使用指导

1. 准备Rayjob使用的yaml文件：rayjob.sample.yaml

   apiVersion: ray.io/v1
   kind: RayJob
   metadata:
     name: rayjob-sample
   spec:
     entrypoint: python /home/ray/samples/sample_code.py
         
     # rayClusterSpec specifies the RayCluster instance to be created by the RayJob controller.
     rayClusterSpec:
       rayVersion: '2.9.3' # should match the Ray version in the image of the containers
       # Ray head pod template
       headGroupSpec:
         rayStartParams:
           dashboard-host: '0.0.0.0'
         template:
           spec:
             containers:
               - name: ray-head
                 image: emr-vke-public-cn-beijing.cr.volces.com/emr/ray:2.9.3-py3.9-ubuntu20.04-1.2.0
                 ports:
                   - containerPort: 6379
                     name: gcs-server
                   - containerPort: 8265 # Ray dashboard
                     name: dashboard
                   - containerPort: 10001
                     name: client
                 resources:
                   limits:
                     cpu: "1"
                   requests:
                     cpu: "200m"
                 volumeMounts:
                   - mountPath: /home/ray/samples
                     name: code-sample
             volumes:
               - name: code-sample
                 configMap:
                   # Provide the name of the ConfigMap you want to mount.
                   name: ray-job-code-sample
                   # An array of keys from the ConfigMap to create as files
                   items:
                     - key: sample_code.py
                       path: sample_code.py
       workerGroupSpecs:
         # the pod replicas in this group typed worker
         - replicas: 1
           minReplicas: 1
           maxReplicas: 5
           # logical group name, for this called small-group, also can be functional
           groupName: small-group
           # The `rayStartParams` are used to configure the `ray start` command.
           # See https://github.com/ray-project/kuberay/blob/master/docs/guidance/rayStartParams.md for the default settings of `rayStartParams` in KubeRay.
           # See https://docs.ray.io/en/latest/cluster/cli.html#ray-start for all available options in `rayStartParams`.
           rayStartParams: {}
           #pod template
           template:
             spec:
               containers:
                 - name: ray-worker
                   image: emr-vke-public-cn-beijing.cr.volces.com/emr/ray:2.9.3-py3.9-ubuntu20.04-1.2.0
                   lifecycle: 
                     preStop:
                       exec:
                         command: [ "/bin/sh","-c","ray stop" ]
                   resources:
                     limits:
                       cpu: "1"
                     requests:
                       cpu: "200m"
   ---
   apiVersion: v1
   kind: ConfigMap
   metadata:
     name: ray-job-code-sample
   data:
     sample_code.py: |
       import ray
       import os
       import requests
   
       ray.init()
   
       @ray.remote
       class Counter:
           def __init__(self):
               self.name = "counter_name"
               self.counter = 0
   
           def inc(self):
               self.counter += 1
   
           def get_counter(self):
               return "{} got {}".format(self.name, self.counter)
   
       counter = Counter.remote()
   
       for _ in range(5):
           ray.get(counter.inc.remote())
           print(ray.get(counter.get_counter.remote()))
   

在该Yaml文件中：

• 定义RayJob名称名为“rayjob-sample”
• 指定了作业入口“python /home/ray/samples/sample_code.py ”
• HeadGroupSpec中，定义RayCluster中Head信息：使用image名称、端口，以及资源限制等，并指定挂载卷
• WorkerGroupSpecs中，定义RayCluster中Worker信息。
• 最后的python脚本中，
  • 定义了一个Counter类，它是一个Ray远程对象，包含增加计数器和获取当前计数的方法。
  • 创建了一个Counter的远程实例，并进行了5次远程增加操作，每次增加后都会打印出当前的计数。

2. 执行上述Yaml文件

   kubectl apply -f rayjob.sample.yaml -n <命名空间>
   

3. 查看RayJob执行情况
   在VKE集群中可以看到创建RayCluster集群和RayJod使用的Pod。

4. 删除RayJob

   kubectl delete -f rayjob.sample.yaml -n <命名空间>