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TKE Serverless Cluster specifies the maximum resources allocated to a pod using annotation specification or automatic Request and Limit calculation. You can select either method.

Specifying by Annotation

TKE Serverless Cluster can add template annotation in the YAML file of a workload to explicitly specify the pod resource specifications. For more information, see Annotation Description.

Automatically Calculating by Request and Limit

TKE Serverless Cluster can calculate the Request and Limit parameters set for a workload to determine the resources required for running pods. The calculation method varies depending on the pod resource type. For more information on how to automatically calculate specified resource specifications based on the Request and Limit parameters, see CPU specification calculation methods for pods and GPU specification calculation methods for pods.

Note：

• If template annotation is specified for a workload, the annotation configuration prevails and the Request and Limit parameters are not calculated.
• For more information about Request and Limit resource allocation, see the supported CPU and GPU specifications in Resource Specifications. If the set value varies greatly from the supported specifications, the allocation of a resource may exceed expectations, resulting in resource waste.
• Regardless of how Request and Limit are set, the final calculation result will match with that in Resource Specifications, and resources allocated to a pod will not exceed the allowed specifications.
• If Request and Limit are not set for a container in a pod, the Request and Limit values of the container are regarded as 0.
• If Request and Limit are not set for all containers in a pod, the default pod specifications are 1 core and 2 GiB.
• Initcontainer and Container are calculated based on the following methods, and the larger value is used.

CPU specifications calculation methods for pods

Step 1. Calculate the total CPU and memory value of a pod.

The total values are the total Request value of all containers in a pod and the maximum Limit value of containers in a pod, respectively.

Step 2. Match pod resource specifications based on the following table.

Sample

You can better understand the CPU specification calculation methods for pods based on the following examples.

resources:
  limits:
    cpu: "1"
    memory: 2Gi
  requests:
    cpu: "1"
    memory: 2Gi

## container1
resources:
  limits:
    cpu: "4"
    memory: 4Gi
  requests:
    cpu: "2"
    memory: 4Gi
## container2
resources:
  limits:
    cpu: "1"
    memory: 2Gi
  requests:
    cpu: "1"
    memory: 2Gi

GPU specification calculation methods for pods

Note：

• Typically, GPUs have the same nvidia.com/gpu parameter value as vGPUs, and the value must be an integer.
• vGPU can be regarded as an independent GPU type. For example, 1/4*V100 indicates that 1/4 the computing power of a V100 GPU card is virtualized to a complete card. During resource allocation, one GPU card is applied for, that is, nvidia.com/GPU is 1.

Step 1. Calculate the total GPU value of a pod.

The total GPU value is the total Request value of all containers in a pod.

Step 2. Match pod resource specifications based on the following table.

Sample

You can better understand the GPU specification calculation methods for pods based on the following examples.

## eks.tke.cloud.tencent.com/gpu-type: V100
resources:
   limits:
     cpu: "8"
     memory: 32Gi
     nvidia.com/gpu: "1"
   requests:
     cpu: "4"
     memory: 16Gi
     nvidia.com/gpu: "1"

## eks.tke.cloud.tencent.com/gpu-type: V100
## container1
resources:
   limits:
     cpu: "8"
     memory: 32Gi
     nvidia.com/gpu: "1"
   requests:
     cpu: "4"
     memory: 16Gi
     nvidia.com/gpu: "1"
## container2
resources:
   limits:
     cpu: "32"
     memory: 128Gi
     nvidia.com/gpu: "1"
   requests:
     cpu: "16"
     memory: 64Gi
     nvidia.com/gpu: "1"