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MetaGPU Device Plugin for Kubernetes

The metagpu device plugin (mgdp) allows you to share one or more Nvidia GPUs between different K8s workloads.

Motivation

K8s doesn't provide a support for the GPU sharing. Meaning user must allocate entire GPU to his workload, even if the actual GPU usage is much bellow of 100%. This project will help to improve the GPU utilization by allowing GPU sharing between multiple K8s workloads.

How it works

The mgdp is based on Nvidia Container Runtime and on go-nvml One for the features the nvidia container runtime providers, is an ability to specify the visible GPU devices Ids by using env vars NVIDIA_VISIBLE_DEVICES.

The most short & simple explanation of the mgdp logic is:

  1. mgdp detects all the GPU devices Ids
  2. From the real GPU deices Ids, it's generates a meta-devices Ids
  3. mgdp advertise these meta-devices Ids to the K8s
  4. Once a user requests for a gpu fraction, for example 0.5 GPU, mgdp will allocate 50 meta-devices IDs
  5. The 50 meta-gpus are bounded to 1 real device id, this real device ID will be injected to the container

In addition, each metagpu container will have mgctl binary. The mgctl is an alternative for nvidia-smi. The mgctl improves security and provides better K8s integration.

The sharing configurations

By default, mgdp will share each of your GPU devices to 100 meta-gpus. For example, if you've a machine with 2 GPUs, mgdp will generate 200 metagpus. Requesting for 50 metagpus, will give you 0.5 GPU, requesting 150 metagpus, will give you 1.5 metagpus.

MetaGPU demo from Cnvrg's MLCon 2.0

Deployment

  1. clone the repo
  2. use helm chart to install or dump manifest and install manually

Install with helm chart

# cd into cloned directory and run
# for openshift set ocp=true  
helm install chart --set ocp=false -ncnvrg

Install with raw K8s manifests

# cd into cloned directory and run
# for openshift set ocp=true  
helm template chart --set ocp=false -ncnvrg > meatgpu.yaml 
kubectl apply -f meatgpu.yaml

Test the Metagpu

cat <<EOF | kubectl apply -f -
apiVersion: v1
kind: Pod
metadata:
  name: metagpu-test
  namespace: cnvrg
spec:
  tolerations:
   - operator: "Exists"
  containers:
  - name: gpu-test-with-gpu
    image: tensorflow/tensorflow:latest-gpu
    command:
      - /usr/local/bin/python
      - -c
      - |
        import tensorflow as tf
        tf.get_logger().setLevel('INFO')
        gpus = tf.config.list_physical_devices('GPU')
        if gpus:
          # Restrict TensorFlow to only allocate 1GB of memory on the first GPU
          try:
            tf.config.set_logical_device_configuration(gpus[0],[tf.config.LogicalDeviceConfiguration(memory_limit=1024)])
            logical_gpus = tf.config.list_logical_devices('GPU')
            print(len(gpus), "Physical GPUs,", len(logical_gpus), "Logical GPUs")
          except RuntimeError as e:
            # Virtual devices must be set before GPUs have been initialized
            print(e)
        print("Num GPUs Available: ", len(tf.config.list_physical_devices('GPU')))
        while True:
          print(tf.reduce_sum(tf.random.normal([1000, 1000])))
    resources:
      limits:
        cnvrg.io/metagpu: "30"
EOF