Integrating GPU Telemetry into Kubernetes
Benefits of GPU Telemetry
Understanding GPU usage provides important insights for IT administrators managing a data center. Trends in GPU metrics correlate with workload behavior and make it possible to optimize resource allocation, diagnose anomalies, and increase overall data center efficiency. As GPUs become more mainstream in Kubernetes environments, users would like to get access to GPU metrics to monitor GPU resources, just like they do today for CPUs.
The purpose of this document is to enumerate an end-to-end (e2e) workflow for setting up and using DCGM within a Kubernetes environment.
For simplicity, the base environment being used in this guide is Ubuntu 18.04 LTS and a native installation of the NVIDIA drivers on the GPU enabled nodes (i.e. neither the NVIDIA GPU Operator nor containerized drivers are used in this document).
NVIDIA Drivers
This section provides a summary of the steps for installing the driver using the apt package manager on Ubuntu LTS.
Note
For complete instructions on setting up NVIDIA drivers, visit the quickstart guide at https://docs.nvidia.com/datacenter/tesla/tesla-installation-notes/index.html. The guide covers a number of pre-installation requirements and steps on supported Linux distributions for a successful install of the driver.
Install the kernel headers and development packages for the currently running kernel:
$ sudo apt-get install linux-headers-$(uname -r)
Setup the CUDA network repository and ensure packages on the CUDA network repository have priority over the Canonical repository:
$ distribution=$(. /etc/os-release;echo $ID$VERSION_ID | sed -e 's/\.//g') \
&& wget https://developer.download.nvidia.com/compute/cuda/repos/$distribution/x86_64/cuda-$distribution.pin \
&& sudo mv cuda-$distribution.pin /etc/apt/preferences.d/cuda-repository-pin-600
Install the CUDA repository GPG key:
$ sudo apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/$distribution/x86_64/7fa2af80.pub \
&& echo "deb http://developer.download.nvidia.com/compute/cuda/repos/$distribution/x86_64 /" | sudo tee /etc/apt/sources.list.d/cuda.list
Update the apt repository cache and install the driver using the cuda-drivers meta-package. Use the --no-install-recommends option for a lean driver install
without any dependencies on X packages. This is particularly useful for headless installations on cloud instances:
$ sudo apt-get update \
&& sudo apt-get -y install cuda-drivers
Install Docker
Use the official Docker script to install the latest release of Docker:
$ curl https://get.docker.com | sh
$ sudo systemctl --now enable docker
Install NVIDIA Container Toolkit
To run GPU accelerated containers in Docker, NVIDIA Container Toolkit for Docker is required.
Setup the stable repository and the GPG key:
$ distribution=$(. /etc/os-release;echo $ID$VERSION_ID) \
&& curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add - \
&& curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list
Install the NVIDIA runtime packages (and their dependencies) after updating the package listing:
$ sudo apt-get update \
&& sudo apt-get install -y nvidia-docker2
Since Kubernetes does not support the --gpus option with Docker yet, the nvidia runtime should be setup as the
default container runtime for Docker on the GPU node. This can be done by adding the default-runtime line into the Docker daemon
config file, which is usually located on the system at /etc/docker/daemon.json:
{
"default-runtime": "nvidia",
"runtimes": {
"nvidia": {
"path": "/usr/bin/nvidia-container-runtime",
"runtimeArgs": []
}
}
}
Restart the Docker daemon to complete the installation after setting the default runtime:
$ sudo systemctl restart docker
At this point, a working setup can be tested by running a base CUDA container:
$ sudo docker run --rm --gpus all nvidia/cuda:11.0-base nvidia-smi
You should observe an output as shown below:
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 450.51.06 Driver Version: 450.51.06 CUDA Version: 11.0 |
|-------------------------------+----------------------+----------------------+
| 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 T4 On | 00000000:00:1E.0 Off | 0 |
| N/A 34C P8 9W / 70W | 0MiB / 15109MiB | 0% Default |
| | | N/A |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=============================================================================|
| No running processes found |
+-----------------------------------------------------------------------------+
Install NVIDIA Device Plugin
To use GPUs in Kubernetes, the NVIDIA Device Plugin is required. The NVIDIA Device Plugin is a daemonset that automatically enumerates the number of GPUs on each node of the cluster and allows pods to be run on GPUs.
The preferred method to deploy the device plugin is as a daemonset using helm. First, install Helm:
$ curl -fsSL -o get_helm.sh https://raw.githubusercontent.com/helm/helm/master/scripts/get-helm-3 \
&& chmod 700 get_helm.sh \
&& ./get_helm.sh
Add the nvidia-device-plugin helm repository:
$ helm repo add nvdp https://nvidia.github.io/k8s-device-plugin \
&& helm repo update
Deploy the device plugin:
$ helm install --generate-name nvdp/nvidia-device-plugin
For more user configurable options while deploying the daemonset, refer to the documentation
At this point, all the pods should be deployed:
$ kubectl get pods -A
NAMESPACE NAME READY STATUS RESTARTS AGE
kube-system calico-kube-controllers-5fbfc9dfb6-2ttkk 1/1 Running 3 9d
kube-system calico-node-5vfcb 1/1 Running 3 9d
kube-system coredns-66bff467f8-jzblc 1/1 Running 4 9d
kube-system coredns-66bff467f8-l85sz 1/1 Running 3 9d
kube-system etcd-ip-172-31-81-185 1/1 Running 4 9d
kube-system kube-apiserver-ip-172-31-81-185 1/1 Running 3 9d
kube-system kube-controller-manager-ip-172-31-81-185 1/1 Running 3 9d
kube-system kube-proxy-86vlr 1/1 Running 3 9d
kube-system kube-scheduler-ip-172-31-81-185 1/1 Running 4 9d
kube-system nvidia-device-plugin-1595448322-42vgf 1/1 Running 2 9d
To test whether CUDA jobs can be deployed, run a sample CUDA vectorAdd application:
The pod spec is shown for reference below, which requests 1 GPU:
apiVersion: v1
kind: Pod
metadata:
name: gpu-operator-test
spec:
restartPolicy: OnFailure
containers:
- name: cuda-vector-add
image: "nvidia/samples:vectoradd-cuda10.2"
resources:
limits:
nvidia.com/gpu: 1
Save this podspec as gpu-pod.yaml. Now, deploy the application:
$ kubectl apply -f gpu-pod.yaml
Check the logs to ensure the app completed successfully:
$ kubectl get pods gpu-operator-test
NAME READY STATUS RESTARTS AGE
gpu-operator-test 0/1 Completed 0 9d
And check the logs of the gpu-operator-test pod:
$ kubectl logs gpu-operator-test
[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