Resource Pools¶
ModelArts Resource Pools¶
When using ModelArts for full-process AI development, you can use two different resource pools.
Public Resource Pool: provides public large-scale computing clusters, which are allocated based on job parameter settings. Resources are isolated by job.
Dedicated Resource Pool: provides exclusive compute resources, which can be used for model deployment. It delivers higher efficiency and cannot be shared with other users.
Create a dedicated resource pool and select the dedicated resource pool during AI development. For details about the dedicated resource pool, see the following:
Creating a Dedicated Resource Pool
Dedicated Resource Pool¶
Dedicated resource pools can be used by notebook instances, training jobs, or for model deployment.
Dedicated resource pools are classified into two types: Dedicated for Development/Training and Dedicated for Service Deployment. The Dedicated for Development/Training type can be used only for notebook instances and training. The Dedicated for Service Deployment type can be used only for AI application deployment.
Dedicated resource pools are available only when they are in the Running state. If a dedicated resource pool is unavailable or abnormal, rectify the fault before using it.
Creating a Dedicated Resource Pool¶
Log in to the ModelArts management console and choose Dedicated Resource Pools on the left.
On the Dedicated Resource Pools page, select Dedicated for Development/Training or Dedicated for Service Deployment.
Click Create in the upper left corner. The page for creating a dedicated resource pool is displayed.
Set the parameters on the page. For details about how to set parameters, see Table 1 and Table 2.
Table 1 Parameters of the Dedicated for Development/Training type¶ Parameter
Description
Resource Type
The default value is and cannot be changed.
Name
Name of a dedicated resource pool.
The value can contain letters, digits, hyphens (-), and underscores (_).
Description
Brief description of a dedicated resource pool.
Nodes
Select the number of nodes in a dedicated resource pool. More nodes mean higher computing performance.
Specifications
Required specifications. The GPU delivers better performance, and the CPU is more cost-effective.
Table 2 Parameters of the Dedicated for Service Deployment type¶ Parameter
Description
Resource Type
The default value is Dedicated for Service Deployment and cannot be changed.
Name
Name of a dedicated resource pool.
Only lowercase letters, digits, and hyphens (-) are allowed. The value must start with a lowercase letter and cannot end with a hyphen (-).
Description
Brief description of a dedicated resource pool.
Custom Network Configuration
If you enable Custom Network Configuration, the service instance runs on the specified network and can communicate with other cloud service resource instances on the network. If you do not enable Custom Network Configuration, ModelArts allocates a dedicated network to each user and isolates users from each other.
If you enable Custom Network Configuration, set VPC, Subnet, and Security Group. If no network is available, go to the VPC service and create a network.
AZ
You can select Random, AZ 1, AZ 2, or AZ 3 based on site requirements. An AZ is a physical region where resources use independent power supplies and networks. AZs are physically isolated but interconnected through an internal network. To enhance workload availability, create nodes in different AZs.
Specifications
Select required flavors.
Nodes
Select the number of nodes in a dedicated resource pool. More nodes mean higher computing performance.
After confirming that the specifications are correct, create a dedicated resource pool as prompted. After a dedicated resource pool is created, its status changes to Running.
Scaling a Dedicated Resource Pool¶
After a dedicated resource pool is used for a period of time, you can scale out or in the capacity of the resource pool by increasing or decreasing the number of nodes.
The procedure for scaling is as follows:
Go to the dedicated resource pool management page, locate the row that contains the desired dedicated resource pool, and click Scale in the Operation column.
On the scaling page, increase or decrease the number of nodes. Increasing the node quantity scales out the resource pool whereas decreasing the node quantity scales in the resource pool. Scale the capacity based on service requirements.
During capacity expansion, increase the number of nodes based on your service needs.
During capacity reduction, delete the target nodes in the Operation column. To reduce one node, you need to switch off the node in Node List to delete the node.
Caution
Before reducing the capacity of a resource pool for real-time inference, ensure that there are no running instances on the nodes to be released. Otherwise, the real-time services will be interrupted. If you are not sure whether any instance is running on the node to be released, submit a consultation service ticket.
Click Submit. After the request is submitted, the dedicated resource pool management page is displayed.
Note
The node is not deleted immediately after the request is submitted. In this case, do not delete nodes from the dedicated resource pool list. Otherwise, the deletion may fail.
You can view the event list on the dedicated resource pool details page. "Begin to delete resource node %s" indicates that the node deletion starts. "Resource node %s deleted" indicates that the node has been deleted in the background.
Deleting a Dedicated Resource Pool¶
If a dedicated resource pool is no longer needed for AI service development, you can delete the resource pool to release resources.
Note
After a dedicated resource pool is deleted, the training jobs, notebook instances, real-time services, and batch services that depend on the resource pool will become unavailable. A dedicated resource pool cannot be restored after being deleted. Exercise caution when performing this operation.
Go to the dedicated resource pool management page and release resources.
In the dialog box that is displayed, click OK.