Skip to main content
Version: v1.3.0

Enable Metax GPU topology-aware scheduling

We now support metax.com/gpu by implementing topo-awareness among metax GPUs:

When multiple GPUs are configured on a single server, the GPU cards are connected to the same PCIe Switch or MetaXLink depending on whether they are connected , there is a near-far relationship. This forms a topology among all the cards on the server, as shown in the following figure:

img

A user job requests a certain number of metax-tech.com/gpu resources, Kubernetes schedule pods to the appropriate node. gpu-device further processes the logic of allocating the remaining resources on the resource node following criterias below:

  1. MetaXLink takes precedence over PCIe Switch in two way: – A connection is considered a MetaXLink connection when there is a MetaXLink connection and a PCIe Switch connection between the two cards. – When both the MetaXLink and the PCIe Switch can meet the job request Equipped with MetaXLink interconnected resources.

  2. When using node-scheduler-policy=spread , Allocate Metax resources to be under the same Metaxlink or Paiswich as much as possible, as the following figure shows:

img

  1. When using node-scheduler-policy=binpack, Assign GPU resources, so minimize the damage to MetaxXLink topology, as the following figure shows:

img

Important Notes​

  1. Device sharing is not supported yet.

  2. These features are tested on MXC500

Prerequisites​

  • Metax GPU extensions >= 0.8.0
  • Kubernetes >= 1.23

Enabling topo-awareness scheduling​

  • Deploy Metax GPU Extensions on metax nodes (Please consult your device provider to aquire its package and document)

  • Deploy HAMi according to README.md

Running Metax jobs​

Mthreads GPUs can now be requested by a container using the metax-tech.com/gpu resource type:

apiVersion: v1
kind: Pod
metadata:
  name: gpu-pod1
  annotations: hami.io/node-scheduler-policy: "spread" # when this parameter is set to spread, the scheduler will try to find the best topology for this task.
spec:
  containers:
    - name: ubuntu-container
      image: cr.metax-tech.com/public-ai-release/c500/colossalai:2.24.0.5-py38-ubuntu20.04-amd64 
      imagePullPolicy: IfNotPresent
      command: ["sleep","infinity"]
      resources:
        limits:
          metax-tech.com/gpu: 1 # requesting 1 vGPUs

NOTICE2: *You can find more examples in examples folder