Enable Mthreads GPU sharing

Introduction

We now support mthreads.com/vgpu 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 on certain type(i.e MTT S4000) and have made it that it does not exceed the boundary.

Device Core Control: GPUs can be allocated with limited compute cores on certain type(i.e MTT S4000) and have made it that it does not exceed the boundary.

Important Notes

1. Device sharing for multi-cards is not supported.

2. Only one mthreads device can be shared in a pod(even there are multiple containers).

3. Support allocating exclusive mthreads GPU by specifying mthreads.com/vgpu only.

4. These features are tested on MTT S4000

Prerequisites

• MT CloudNative Toolkits > 1.9.0
• driver version >= 1.2.0

Enabling GPU-sharing Support

• Deploy MT-CloudNative Toolkit on mthreads nodes (Please consult your device provider to acquire its package and document)

NOTICE: You can remove mt-mutating-webhook and mt-gpu-scheduler after installation(optional).

• set the 'devices.mthreads.enabled = true' when installing hami

helm install hami hami-charts/hami --set scheduler.kubeScheduler.imageTag={your kubernetes version} --set device.mthreads.enabled=true -n kube-system

Running Mthreads jobs

Mthreads GPUs can now be requested by a container using the mthreads.com/vgpu, mthreads.com/sgpu-memory and mthreads.com/sgpu-core resource type:

apiVersion: v1
kind: Pod
metadata:
  name: gpushare-pod-default
spec:
  restartPolicy: OnFailure
  containers:
    - image: core.harbor.zlidc.mthreads.com:30003/mt-ai/lm-qy2:v17-mpc 
      imagePullPolicy: IfNotPresent
      name: gpushare-pod-1
      command: ["sleep"]
      args: ["100000"]
      resources:
        limits:
          mthreads.com/vgpu: 1
          mthreads.com/sgpu-memory: 32
          mthreads.com/sgpu-core: 8

NOTICE1: Each unit of sgpu-memory indicates 512M device memory

NOTICE2: You can find more examples in examples folder