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iluvatar-gpu-support.md

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Introduction

We now support iluvatar.ai/gpu(i.e MR-V100、BI-V150、BI-V100) by implementing most device-sharing features as nvidia-GPU, including:

GPU sharing: Each task can allocate a portion of GPU instead of a whole GPU card, thus GPU can be shared among multiple tasks.

Device Memory Control: GPUs can be allocated with certain device memory size and have made it that it does not exceed the boundary.

Device Core Control: GPUs can be allocated with limited compute cores and have made it that it does not exceed the boundary.

Device UUID Selection: You can specify which GPU devices to use or exclude using annotations.

Very Easy to use: You don't need to modify your task yaml to use our scheduler. All your GPU jobs will be automatically supported after installation.

Prerequisites

  • Iluvatar gpu-manager (please consult your device provider)
  • driver version > 3.1.0

Enabling GPU-sharing Support

  • Deploy gpu-manager on iluvatar nodes (Please consult your device provider to aquire its package and document)

NOTICE: Install only gpu-manager, don't install gpu-admission package.

  • Identify the resource name about core and memory usage(i.e 'iluvatar.ai/vcuda-core', 'iluvatar.ai/vcuda-memory')

  • set the 'iluvatarResourceMem' and 'iluvatarResourceCore' parameters when install hami

helm install hami hami-charts/hami --set scheduler.kubeScheduler.imageTag={your kubernetes version} --set iluvatarResourceMem=iluvatar.ai/vcuda-memory --set iluvatarResourceCore=iluvatar.ai/vcuda-core -n kube-system

NOTE: The default resource names are:

  • iluvatar.ai/vgpu for GPU count
  • iluvatar.ai/vcuda-memory for memory allocation
  • iluvatar.ai/vcuda-core for core allocation

You can customize these names using the parameters above.

Device Granularity

HAMi divides each Iluvatar GPU into 100 units for resource allocation. When you request a portion of a GPU, you're actually requesting a certain number of these units.

Memory Allocation

  • Each unit of iluvatar.ai/vcuda-memory represents 256MB of device memory
  • If you don't specify a memory request, the system will default to using 100% of the available memory
  • Memory allocation is enforced with hard limits to ensure tasks don't exceed their allocated memory

Core Allocation

  • Each unit of iluvatar.ai/vcuda-core represents 1% of the available compute cores
  • Core allocation is enforced with hard limits to ensure tasks don't exceed their allocated cores
  • When requesting multiple GPUs, the system will automatically set the core resources based on the number of GPUs requested

Running Iluvatar jobs

Iluvatar GPUs can now be requested by a container using the iluvatar.ai/vgpu, iluvatar.ai/vcuda-memory and iluvatar.ai/vcuda-core resource type:

apiVersion: v1
kind: Pod
metadata:
  name: poddemo
spec:
  restartPolicy: Never
  containers:
  - name: poddemo
    image: harbor.4pd.io/vgpu/corex_transformers@sha256:36a01ec452e6ee63c7aa08bfa1fa16d469ad19cc1e6000cf120ada83e4ceec1e
    command:
    - bash
    args:
    - -c
    - |
      set -ex
      echo "export LD_LIBRARY_PATH=/usr/local/corex/lib64:$LD_LIBRARY_PATH">> /root/.bashrc
      cp -f /usr/local/iluvatar/lib64/libcuda.* /usr/local/corex/lib64/
      cp -f /usr/local/iluvatar/lib64/libixml.* /usr/local/corex/lib64/
      source /root/.bashrc
      sleep 360000
    resources:
      requests:
        iluvatar.ai/vgpu: 1
        iluvatar.ai/vcuda-core: 50
        iluvatar.ai/vcuda-memory: 64
      limits:
        iluvatar.ai/vgpu: 1
        iluvatar.ai/vcuda-core: 50
        iluvatar.ai/vcuda-memory: 64

NOTICE1: Each unit of vcuda-memory indicates 256M device memory

NOTICE2: You can find more examples in examples/iluvatar folder

Device UUID Selection

You can specify which GPU devices to use or exclude using annotations:

apiVersion: v1
kind: Pod
metadata:
  name: poddemo
  annotations:
    # Use specific GPU devices (comma-separated list)
    iluvatar.ai/use-gpuuuid: "node1-iluvatar-0,node1-iluvatar-1"
    # Or exclude specific GPU devices (comma-separated list)
    iluvatar.ai/nouse-gpuuuid: "node1-iluvatar-2,node1-iluvatar-3"
spec:
  # ... rest of pod spec

NOTE: The device ID format is {node-name}-iluvatar-{index}. You can find the available device IDs in the node status.

Finding Device UUIDs

You can find the UUIDs of Iluvatar GPUs on a node using the following command:

kubectl get pod <pod-name> -o yaml | grep -A 10 "hami.io/<card-type>-devices-allocated"

Or by examining the node annotations:

kubectl get node <node-name> -o yaml | grep -A 10 "hami.io/node-register-<card-type>"

Look for annotations containing device information in the node status.

Notes

  1. You need to set the following prestart command in order for the device-share to work properly
      set -ex
      echo "export LD_LIBRARY_PATH=/usr/local/corex/lib64:$LD_LIBRARY_PATH">> /root/.bashrc
      cp -f /usr/local/iluvatar/lib64/libcuda.* /usr/local/corex/lib64/
      cp -f /usr/local/iluvatar/lib64/libixml.* /usr/local/corex/lib64/
      source /root/.bashrc

  1. Virtualization takes effect only for containers that apply for one GPU(i.e iluvatar.ai/vgpu=1 ). When requesting multiple GPUs, the system will automatically set the core resources based on the number of GPUs requested.

  2. The iluvatar.ai/vcuda-memory resource is only effective when iluvatar.ai/vgpu=1.

  3. Multi-device requests (iluvatar.ai/vgpu > 1) do not support vGPU mode.