Platform For AI:Work with the public resource group

The public resource group allows you to deploy model services by specifying the node resources or node type. The billing starts after a service is deployed and enters the Running state. For more information, see Billing of EAS.

If you use the EASCMD client to deploy a service, you can specify a system disk capacity. For more information, see Parameters of model services. Each node of the public resource group has 30 GB free system disk capacity. You are billed for the storage capacity that exceeds the free quota on a pay-as-you-go basis, and the billing starts immediately after the system disk is created. For more information, see Billing of EAS.

On the EAS-Online Model Services page, find the model service that you want to stop on the Inference Service tab and click Stop in the Actions column. Then, the model service is stopped, and the system stops billing the resources used by the model service. For more information, see Model service deployment by using the PAI console and Machine Learning Designer.

Use one of the following methods to deploy services to the public resource group.

• Use the console

  Deploy a model service on the EAS-Online Model Services page and set Resource Group Type to Public Resource Group. For more information, see Model service deployment by using the PAI console and Machine Learning Designer.

• Use EASCMD

  Use the EASCMD client to deploy a model service. For more information, see Deploy model services by using EASCMD or DSW.

  Deploy the model service by specifying the node resources or node type.

  • Deploy the model service by specifying the node resources:

    {
        "metadata": {
            "instance": 2,
            "cpu": 1,
            "memory": 2000
        },
        "cloud": {
            "computing": {}
        },
        "name": "test",
        "model_path": "http://examplebucket.oss-cn-shanghai.aliyuncs.com/models/model.tar.gz",
        "processor": "tensorflow_cpu_1.12"
    }

  • To deploy the model service by specifying the node type, you must add the cloud.computing.instance_type parameter to the service configuration file to specify an Elastic Compute Service (ECS) instance type:

    {
      "name": "tf_serving_test",
      "model_path": "http://examplebucket.oss-cn-shanghai.aliyuncs.com/models/model.tar.gz",
      "processor": "tensorflow_gpu_1.12",
      "cloud":{
          "computing":{
              "instance_type":"ecs.gn6i-c24g1.6xlarge"
          }
      },
      "metadata": {
        "instance": 1,
        "cuda": "9.0",
        "memory": 7000,
        "gpu": 1,
        "cpu": 4
      }
    }

    The following table describes the ECS instance types supported for the instance_type parameter.

    Instance Type	Specification
    ecs.c5.6xlarge	c5 (24vCPU+48GB)
    ecs.c6.2xlarge	c6 (8vCPU+16GB)
    ecs.c6.4xlarge	c6 (16vCPU+32GB)
    ecs.c6.6xlarge	c6 (24vCPU+48GB)
    ecs.c6.8xlarge	c6 (32vCPU+64GB)
    ecs.g5.6xlarge	g5 (24vCPU+96GB)
    ecs.g6.2xlarge	g6 (8vCPU+32GB)
    ecs.g6.4xlarge	g6 (16vCPU+64GB)
    ecs.g6.6xlarge	g6 (24vCPU+96GB)
    ecs.g6.8xlarge	g6 (32vCPU+128GB)
    ecs.gn5-c28g1.7xlarge	28vCPU+112GB+1*P100
    ecs.gn5-c4g1.xlarge	4vCPU+30GB+1*P100
    ecs.gn5-c8g1.2xlarge	8vCPU+60GB+1*P100
    ecs.gn5-c8g1.4xlarge	16vCPU+120GB+2*P100
    ecs.gn5i-c4g1.xlarge	4vCPU+16GB+1*P4
    ecs.gn5i-c8g1.2xlarge	8vCPU+32GB+1*P4
    ecs.gn6i-c16g1.4xlarge	16vCPU+62GB+1*T4
    ecs.gn6i-c24g1.12xlarge	48vCPU+186GB+2*T4
    ecs.gn6i-c24g1.6xlarge	48vCPU+186GB+2*T4
    ecs.gn6i-c4g1.xlarge	4vCPU+15GB+1*T4
    ecs.gn6i-c8g1.2xlarge	8vCPU+31GB+1*T4
    ecs.gn6v-c8g1.2xlarge	8vCPU+32GB+1*V100
    ecs.r6.2xlarge	r6 (8vCPU+64GB)
    ecs.r6.4xlarge	r6 (16vCPU+128GB)
    ecs.r6.6xlarge	r6 (24vCPU+192GB)
    ecs.r6.8xlarge	r6 (32vCPU+256GB)
    ecs.g7.2xlarge	g7 (8vCPU+32GB)
    ecs.g7.4xlarge	g7 (16vCPU+64GB)
    ecs.g7.6xlarge	g7 (24vCPU+96GB)
    ecs.g7.8xlarge	g7 (32vCPU+128GB)
    ecs.c7.2xlarge	c7 (8vCPU+16GB)
    ecs.c7.4xlarge	c7 (16vCPU+32GB)
    ecs.c7.6xlarge	c7 (24vCPU+48GB)
    ecs.c7.8xlarge	c7 (32vCPU+64GB)
    ecs.r7.2xlarge	r7 (8vCPU+64GB)
    ecs.r7.4xlarge	r7 (16vCPU+128GB)
    ecs.r7.6xlarge	r7 (24vCPU+192GB)
    ecs.r7.8xlarge	r7 (32vCPU+256GB)
    ecs.g7.16xlarge	g7 (64vCPU+256GB)
    ecs.c7.16xlarge	c7 (64vCPU+128GB)
    ecs.r7.16xlarge	r7 (64vCPU+512GB)
    ecs.gn7i-c8g1.2xlarge	8vCPU+30GB+1*A10
    ecs.gn7i-c16g1.4xlarge	16vCPU+60GB+1*A10
    ecs.gn7i-c32g1.8xlarge	32vCPU+188GB+1*A10
    ecs.gn6e-c12g1.3xlarge	12vCPU+92GB+1*V100
    ecs.g6.xlarge	g6 (4vCPU+16GB)
    ecs.c6.xlarge	c6 (4vCPU+8GB)
    ecs.r6.xlarge	r6 (4vCPU+32GB)
    ecs.g6.large	g6 (2vCPU+8GB)
    ecs.c6.large	c6 (2vCPU+4GB)
    ecs.r6.large	r6 (2vCPU+16GB)
    ecs.c7a.large	AMD (2vCPU+4GB)
    ecs.c7a.xlarge	AMD (4vCPU+8GB)
    ecs.c7a.2xlarge	AMD (8vCPU+16GB)
    ecs.c7a.4xlarge	AMD (16vCPU+32GB)
    ecs.c7a.8xlarge	AMD (32vCPU+64GB)
    ecs.c7a.16xlarge	AMD (64vCPU+128GB)
    ecs.g7a.large	AMD (2vCPU+8GB)
    ecs.g7a.xlarge	AMD (4vCPU+16GB)
    ecs.g7a.2xlarge	AMD (8vCPU+32GB)
    ecs.g7a.4xlarge	AMD (16vCPU+64GB)
    ecs.g7a.8xlarge	AMD (32vCPU+128GB)
    ecs.g7a.16xlarge	AMD (64vCPU+256GB)