Using GPU workers

Charmed Kubernetes supports GPU-enabled instances for applications that can use them. The kubernetes-worker application will automatically detect NVIDIA hardware and enable the appropriate support. This page describes recommended deployment and verification steps when using GPU workers with Charmed Kubernetes.

Deploying Charmed Kubernetes with GPU workers

When deploying the Charmed Kubernetes bundle, you can use a YAML overlay file with constraints to ensure worker units are deployed on GPU-enabled machines. Because GPU support varies depending on the underlying cloud, this requires specifying a particular instance type.

For example, when deploying to AWS, you may decide to use a p3.2xlarge instance from the available AWS GPU-enabled instance types. Similarly, you could choose Azure’s Standard_NC6s_v3 instance from the available Azure GPU-enabled instance types.

NVIDIA updates its list of supported GPUs frequently, so be sure to cross reference the GPU included in a specific cloud instance against the Supported NVIDIA GPUs and Systems documentation.

Example overlay files that set GPU worker constraints:

# AWS gpu-overlay.yaml
applications:
  kubernetes-worker:
    constraints: instance-type=p3.2xlarge

# Azure gpu-overlay.yaml
applications:
  kubernetes-worker:
    constraints: instance-type=Standard_NC6s_v3

Deploy Charmed Kubernetes with an overlay like this:

juju deploy charmed-kubernetes --overlay ~/path/my-overlay.yaml --overlay ~/path/gpu-overlay.yaml

As demonstrated here, you can use multiple overlay files when deploying, so you can combine GPU support with an integrator charm or other custom configuration.

You may then want to test a GPU workload.

Adding GPU workers post deployment

It isn’t necessary for all worker units to have GPU support. You can add GPU-enabled workers to an existing cluster. The recommended way to do this is to first set a new constraint for the kubernetes-worker application:

juju set-constraints kubernetes-worker instance-type=p3.2xlarge

Then add as many new GPU worker units as required. For example, to add two new units:

juju add-unit kubernetes-worker -n2

Adding GPU workers with GCP

Google supports GPUs slightly differently to most clouds. There are no GPU variations included in the general instance templates, and therefore they have to be added manually.

To begin, add a new machine with Juju. Include any desired constraints for cpu cores, memory, etc:

juju add-machine --constraints 'cores=4 mem=16G'

The command will return with the unit number of the machine that was created - take note of this number.

Next you will need to use the gcloud tool or the GCP console to stop the newly created instance, edit its configuration and then restart the machine.

Once it is up and running, add the kubernetes-worker application to it:

juju add-unit kubernetes-worker --to 10

…replacing 10 in the above with the previously noted number. As the charm installs, the GPU will be detected and the relevant support will be installed.

Testing

As GPU instances can be costly, it is useful to test that they can actually be used. A simple test job can be created to run NVIDIA’s hardware reporting tool. Please note that you may need to replace the image tag in the following YAML with the latest supported one.

This can also be downloaded here.

apiVersion: batch/v1
kind: Job
metadata:
  name: nvidia-smi
spec:
  template:
    metadata:
      name: nvidia-smi
    spec:
      restartPolicy: Never
      containers:
      - image: nvidia/cuda:12.1.0-base-ubuntu22.04
        name: nvidia-smi
        args:
          - nvidia-smi
        resources:
          limits:
            nvidia.com/gpu: 1
          requests:
            nvidia.com/gpu: 1
        volumeMounts:
        - mountPath: /usr/bin/
          name: binaries
        - mountPath: /usr/lib/x86_64-linux-gnu
          name: libraries
      volumes:
      - name: binaries
        hostPath:
          path: /usr/bin/
      - name: libraries
        hostPath:
          path: /usr/lib/x86_64-linux-gnu

Download the file and run it with:

kubectl create -f nvidia-test.yaml

You can inspect the logs to find the hardware report.

kubectl logs job.batch/nvidia-smi

Tue Apr 11 22:46:04 2023
+---------------------------------------------------------------------------------------+
| NVIDIA-SMI 530.30.02              Driver Version: 530.30.02    CUDA Version: 12.1     |
|-----------------------------------------+----------------------+----------------------+
| GPU  Name                  Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf            Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|                                         |                      |               MIG M. |
|=========================================+======================+======================|
|   0  Tesla V100-SXM2-16GB            On | 00000000:00:1E.0 Off |                    0 |
| N/A   36C    P0               23W / 300W|      0MiB / 16384MiB |      0%      Default |
|                                         |                      |                  N/A |
+-----------------------------------------+----------------------+----------------------+

+---------------------------------------------------------------------------------------+
| Processes:                                                                            |
|  GPU   GI   CI        PID   Type   Process name                            GPU Memory |
|        ID   ID                                                             Usage      |
|=======================================================================================|
|  No running processes found                                                           |
+---------------------------------------------------------------------------------------+