Google Kubernetes Engine#
RAPIDS can be deployed on Google Cloud via the Google Kubernetes Engine (GKE).
To run RAPIDS you’ll need a Kubernetes cluster with GPUs available.
Prerequisites#
First you’ll need to have the gcloud CLI tool installed along with kubectl, helm, etc for managing Kubernetes.
Ensure you are logged into the gcloud CLI.
$ gcloud init
Create the Kubernetes cluster#
Now we can launch a GPU enabled GKE cluster.
gcloud container clusters create rapids-gpu-kubeflow \
--accelerator type=nvidia-tesla-a100,count=2 --machine-type a2-highgpu-2g \
--zone us-central1-c --release-channel stable
With this command, you’ve launched a GKE cluster called rapids-gpu-kubeflow. You’ve specified that it should use nodes of type a2-highgpu-2g, each with two A100 GPUs.
Install drivers#
Next, install the NVIDIA drivers onto each node.
$ kubectl apply -f https://raw.githubusercontent.com/GoogleCloudPlatform/container-engine-accelerators/master/nvidia-driver-installer/cos/daemonset-preloaded-latest.yaml
daemonset.apps/nvidia-driver-installer created
Verify that the NVIDIA drivers are successfully installed.
$ kubectl get po -A --watch | grep nvidia
kube-system nvidia-driver-installer-6zwcn 1/1 Running 0 8m47s
kube-system nvidia-driver-installer-8zmmn 1/1 Running 0 8m47s
kube-system nvidia-driver-installer-mjkb8 1/1 Running 0 8m47s
kube-system nvidia-gpu-device-plugin-5ffkm 1/1 Running 0 13m
kube-system nvidia-gpu-device-plugin-d599s 1/1 Running 0 13m
kube-system nvidia-gpu-device-plugin-jrgjh 1/1 Running 0 13m
After your drivers are installed, you are ready to test your cluster.
Let’s create a sample pod that uses some GPU compute to make sure that everything is working as expected.
cat << EOF | kubectl create -f -
apiVersion: v1
kind: Pod
metadata:
name: cuda-vectoradd
spec:
restartPolicy: OnFailure
containers:
- name: cuda-vectoradd
image: "nvidia/samples:vectoradd-cuda11.6.0-ubuntu18.04"
resources:
limits:
nvidia.com/gpu: 1
EOF
$ kubectl logs pod/cuda-vectoradd
[Vector addition of 50000 elements]
Copy input data from the host memory to the CUDA device
CUDA kernel launch with 196 blocks of 256 threads
Copy output data from the CUDA device to the host memory
Test PASSED
Done
If you see Test PASSED in the output, you can be confident that your Kubernetes cluster has GPU compute set up correctly.
Next, clean up that pod.
$ kubectl delete pod cuda-vectoradd
pod "cuda-vectoradd" deleted
Install RAPIDS#
Now that you have a GPU enables Kubernetes cluster on GKE you can install RAPIDS with any of the supported methods.
Clean up#
You can also delete the GKE cluster to stop billing with the following command.
$ gcloud container clusters delete rapids-gpu-kubeflow --zone us-central1-c
Deleting cluster rapids...⠼