CostManagement Docs

How to Define a pGPU Cost Model

TOC

Prerequisites

In the GPU cluster:

  • Alauda Build of NVIDIA GPU Device Plugin installed
  • The Cost Management Agent installed

About Alauda Build of NVIDIA GPU Device Plugin

The NVIDIA device plugin for Kubernetes is a Daemonset that allows you to automatically:

  • Expose the number of GPUs on each nodes of your cluster
  • Keep track of the health of your GPUs
  • Run GPU enabled containers in your Kubernetes cluster.
Note
Because Alauda Build of NVIDIA GPU Device Plugin releases on a different cadence from Alauda Container Platform, the Alauda Build of NVIDIA GPU Device Plugin documentation is now available as a separate documentation set at .

Procedure

Create PrometheusRule for generate needed metrics

Create a PrometheusRule in the GPU cluster.

apiVersion: monitoring.coreos.com/v1
kind: PrometheusRule
metadata:
  labels:
    prometheus: kube-prometheus
  name: pgpu-labels
  namespace: kube-system
spec:
  groups:
  - name: gpu.rules
    interval: 30s
    rules:
    - record: gpu_count
      expr: |
        count by (UUID, label_modelName, namespace) (
          label_replace(
            DCGM_FI_DEV_GPU_UTIL{namespace!="kube-system"},
            "label_modelName",
            "$0",
            "modelName",
            ".*"
          )
        )

Add Collection Config (Cost Management Agent)

Create a ConfigMap in the GPU cluster where the Cost Management Agent runs to declare what to collect.

apiVersion: v1
data:
  config: >
    - kind: pGPU
      category: pGPUCount
      item: vGPUCountQuota
      period: Hourly
      labels:
        query: "gpu_count"
        mappers:
          name: UUID
          namespace: namespace
          cluster: ""
          project: ""
      usage:
        query: gpu_count
        step: 5m
        mappers:
          name: UUID
          namespace: namespace
          cluster: ""
          project: ""
kind: ConfigMap
metadata:
  labels:
    cpaas.io/slark.collection.config: "true"
  name: slark-agent-pgpu-namespace-config
  namespace: cpaas-system
---
apiVersion: v1
data:
  config: >
    - kind: Project
      category: pGPUCount
      item: vGPUCountsProjectQuota
      period: Hourly
      usage:
        query: avg by (project, cluster) (avg_over_time(cpaas_project_resourcequota{resource="requests.nvidia.com/gpu", type="project-hard"}[5m]))
        step: 5m
        mappers:
          name: project
          namespace: ""
          cluster: cluster
          project: project
kind: ConfigMap
metadata:
  labels:
    cpaas.io/slark.collection.config: "true"
  name: slark-agent-project-config-vgpu
  namespace: cpaas-system

After adding the yaml , you need to restart the Agent Pod to reload the configurations.

kubectl delete pods -n cpaas-system -l service_name=slark-agent

Add Display/Storage Config (Cost Management Server)

Create a ConfigMap in the cluster where the Cost Management Server runs to declare billing items, methods, units, and display names. This tells the server what and how to bill.

apiVersion: v1
data:
  config: |
    - name: pGPUCount
      displayname:
        zh: "pGPU"
        en: "pGPU"
      methods:
        - name: Request
          displayname:
            zh: "请求量"
            en: "Request Usage"
          item: vGPUCountQuota
          divisor: 1
          unit:
            zh: "count-hours"
            en: "count-hours"
        - name: ProjectQuota
          displayname:
            zh: "项目配额"
            en: "Project Quota"
          item: vGPUCountsProjectQuota
          unit:
            zh: "count-hours"
            en: "count-hours"
          divisor: 1
kind: ConfigMap
metadata:
  labels:
    cpaas.io/slark.display.config: "true"
  name: slark-display-config-for-pgpu
  namespace: kube-public

After adding the yaml , you need to restart the Server Pod to reload the configurations.

kubectl delete pods -n cpaas-system -l service_name=slark

Add Price For a pGPU Cost Model

Billing Method Description

Billing ItemBilling MethodBilling RulesDescription
pGPURequest (Count-hours)Calculated on an hourly basis using the POD's Request over the past hour, multiplied by the actual duration of the POD (counted as 5 minutes if less than 5 minutes).Based on pGPU resource requests
pGPUProject Quota (Count-hours)Calculated on an hourly basis using the project's allocated CPU quota limit, multiplied by time duration. Segmented calculation when quota changes.Based on project-level resource quotas

If the GPU cluster does not have a Cost model, you need to create a new cost model. Then you can add price for the cost model of the GPU cluster:

  1. Select pGPUin Billing Items.
  2. Select Request Usage (count-hours) or Project Quota (count-hours) in Method.
  3. Set Default Price.
  4. Config Price By Label (optional). Example: key: modelName value: "Tesla P100-PCIE-16GB" or "Tesla T4" or "NVIDIA A30"(Got it by run nvidia-smi)

Cost Details and Cost Statistics

Finally, after waiting for 1 or more hours, you can see the cost details in the Cost Details with namespace and card uuid dimensions. And you can see the total costs based on cluster, project, and namespace in the Cost Statistics.