Attention
The DEEP platform is sunsetting.
The DEEP-Hybrid-DataCloud project project has ended and its plaftform and software are being decomissioned during 2023, as they have been superseeded by the AI4EOSC platform and the AI4OS software stack.
Please refer to the following links for further information:
DEEP : Installing and testing GPU Node in Kubernetes - CentOS7
Introduction
The manual procedure for installation and configuration od a Kubernetes cluster is provided. The cluster is composed by a Master node and one Worker node
Mater | Worker node |
|---|---|
VM
| Baremetal
|
Cluster Status
Cluster Name: KubeDeep
Kubectl components
# kubectl get componentstatuses
NAME STATUS MESSAGE ERROR
controller-manager Healthy ok
scheduler Healthy ok
etcd-0 Healthy {"health": "true"
# kubectl get nodes
NAME STATUS ROLES AGE VERSION
Node1_GPU Ready <none> 13d v1.10.0
Worker GPU specifications
# nvidia-smi
Mon Apr 2 23:13:37 2018
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 390.30 Driver Version: 390.30 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |
|===============================+======================+======================|
| 0 Tesla K40m On | 00000000:02:00.0 Off | 0 |
| N/A 30C P8 20W / 235W | 0MiB / 11441MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
| 1 Tesla K40m On | 00000000:84:00.0 Off | 0 |
| N/A 32C P8 20W / 235W | 0MiB / 11441MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: GPU Memory |
| GPU PID Type Process name Usage |
|=============================================================================|
| No running processes found |
+-----------------------------------------------------------------------------+
Tests
Test #1 - Simple vector-add (CUDA8)
This CUDA Runtime API sample is a very basic sample that implements element by element vector addition. The examples uses CUDA8 driver.
#cat vector-add.yaml
apiVersion: v1
kind: Pod
metadata:
name: vector-add
spec:
restartPolicy: OnFailure
containers:
- name: cuda-vector-add
# https://github.com/kubernetes/kubernetes/blob/v1.7.11/test/images/nvidia-cuda/Dockerfile
image: "k8s.gcr.io/cuda-vector-add:v0.1"
resources:
limits:
nvidia.com/gpu: 1
# kubectl apply -f vector-add.yaml
pod "vector-add" created
# kubectl get pods --show-all
NAME READY STATUS RESTARTS AGE
vector-add 0/1 Completed 0 4s
Test #2 - Simple vector-add with different CUDA driver
This CUDA Runtime API sample is a very basic sample that implements element by element vector addition. The examples uses two Docker images with different version of CUDA driver. To complete the test, a new Docker image with CUDA driver version 9 has been built and uploaded in a private repo.
# cat cuda8-vector-add.yaml
apiVersion: v1
kind: Pod
metadata:
name: cuda8-vector-add
spec:
restartPolicy: OnFailure
containers:
- name: cuda-vector-add
# https://github.com/kubernetes/kubernetes/blob/v1.7.11/test/images/nvidia-cuda/Dockerfile
image: "k8s.gcr.io/cuda-vector-add:v0.1"
resources:
limits:
nvidia.com/gpu: 1
# cat cuda9-vector-add.yaml
apiVersion: v1
kind: Pod
metadata:
name: cuda9-vector-add
spec:
restartPolicy: OnFailure
containers:
- name: cuda-vector-add
image: <private repo>/deep/cuda-vector-add:v0.2
resources:
limits:
nvidia.com/gpu: 1
# kubectl apply -f cuda9-vector-add.yaml -f cuda8-vector-add.yaml
pod "cuda9-vector-add" created
pod "cuda8-vector-add" created
# kubectl get pods --show-all
NAME READY STATUS RESTARTS AGE
cuda8-vector-add 0/1 Completed 0 2s
cuda9-vector-add 0/1 Completed 0 2s
Test #3 - Simple BinomialOption with different CUDA driver
This sample evaluates fair call price for a given set of European options under binomial model. To complete the test, two new Docker images with CUDA8 and CUDA9 has been built and uploaded in a private repo.The test will take some seconds and GPU engage can be shown
# cat cuda8-binomialoption.yaml
apiVersion: v1
kind: Pod
metadata:
name: cuda8-binomialoption
spec:
restartPolicy: OnFailure
containers:
- name: cuda8-binomilaoption
image: <private_repo>/deep/cuda-binomialoption:v0.1
resources:
limits:
nvidia.com/gpu: 1
# cat cuda9-binomialoption.yaml
apiVersion: v1
kind: Pod
metadata:
name: cuda9-binomialoption
spec:
restartPolicy: OnFailure
containers:
- name: cuda9-binomialoption
image: <private_repo>/deep/cuda-binomialoption:v0.2
resources:
limits:
nvidia.com/gpu: 1
# kubectl apply -f cuda8-binomialoption.yaml -f cuda9-binomialoption.yaml
pod "cuda8-binomialoption" created
pod "cuda9-binomialoption" created
# kubectl get pods --show-all
NAME READY STATUS RESTARTS AGE
cuda8-binomialoption 1/1 Running 0 2s
cuda9-binomialoption 1/1 Running 0 2s
# kubectl get pods --show-all
NAME READY STATUS RESTARTS AGE
cuda8-binomialoption 1/1 Running 0 22s
cuda9-binomialoption 1/1 Running 0 22s
# kubectl get pods --show-all
NAME READY STATUS RESTARTS AGE
cuda8-binomialoption 0/1 Completed 0 1m
cuda9-binomialoption 0/1 Completed 0 1m
# nvidia-smi
Mon Apr 2 23:35:17 2018
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 390.30 Driver Version: 390.30 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |
|===============================+======================+======================|
| 0 Tesla K40m On | 00000000:02:00.0 Off | 0 |
| N/A 31C P0 63W / 235W | 80MiB / 11441MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
| 1 Tesla K40m On | 00000000:84:00.0 Off | 0 |
| N/A 33C P0 63W / 235W | 80MiB / 11441MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: GPU Memory |
| GPU PID Type Process name Usage |
|=============================================================================|
| 0 3385 C ./binomialOptions 69MiB |
| 1 3369 C ./binomialOptions 69MiB |
+-----------------------------------------------------------------------------+
Test #4 - Job Scheduling features
Tests highlithing the features of the Kubernetes scheduler. Default schedule policies are used (FIFO).
Submission of a bunch of different cuda jobs with different running time.
Parrec (1h)
Cuda8-binomialoption.yaml (5 min)
Cuda9-binomialoption.yaml (5 min)
Cuda8-vector-add.yaml (few sec)
Cuda9-vector-add.yaml (few sec)
The parrec job has been launched as first job. One GPU has been engaged by the job; the other is still available for other jobs.
# kubectl apply -f parrec.yaml
pod "parrec" created
# kubectl get pods -o wide
NAME READY STATUS RESTARTS AGE IP NODE
parrec 1/1 Running 0 22s 172.30.0.52 gpu-node-01
Other jobs have been submitted in the following order:
# kubectl apply -f cuda8-binomialoption.yaml -f cuda9-binomialoption.yaml -f cuda8-vector-add.yaml -f cuda9-vector-add.yaml
pod "cuda8-binomialoption" created
pod "cuda9-binomialoption" created
pod "cuda8-vector-add" created
pod "cuda9-vector-add" created
# kubectl get pods -o wide
NAME READY STATUS RESTARTS AGE IP NODE
cuda8-binomialoption 1/1 Running 0 4s 172.30.0.53 gpu-node-01
cuda8-vector-add 0/1 Pending 0 4s <none> <none>
cuda9-binomialoption 0/1 Pending 0 4s <none> <none>
cuda9-vector-add 0/1 Pending 0 4s <none> <none>
parrec 1/1 Running 0 1m 172.30.0.52 gpu-node-01
The “cuda8-binomialoption” is running, the other are in the FIFO queue in pending state. After completion, the other job will be running in the same order they have been submitted.
# kubectl get pods -o wide
NAME READY STATUS RESTARTS AGE IP NODE
cuda8-binomialoption 1/1 Running 0 31s 172.30.0.53 gpu-node-01
cuda8-vector-add 0/1 Pending 0 31s <none> <none>
cuda9-binomialoption 0/1 Pending 0 31s <none> <none>
cuda9-vector-add 0/1 Pending 0 31s <none> <none>
parrec 1/1 Running 0 2m 172.30.0.52 gpu-node-01
# kubectl get pods -o wide
NAME READY STATUS RESTARTS AGE IP NODE
cuda8-binomialoption 0/1 Completed 0 49s 172.30.0.53 gpu-node-01
cuda8-vector-add 0/1 Pending 0 49s <none> <none>
cuda9-binomialoption 0/1 Pending 0 49s <none> <none>
cuda9-vector-add 0/1 Pending 0 49s <none> <none>
parrec 1/1 Running 0 2m 172.30.0.52 gpu-node-01
# kubectl get pods -o wide
NAME READY STATUS RESTARTS AGE IP NODE
cuda8-binomialoption 0/1 Completed 0 1m 172.30.0.53 gpu-node-01
cuda8-vector-add 0/1 Pending 0 1m <none> <none>
cuda9-binomialoption 0/1 ContainerCreating 0 1m <none> gpu-node-01
cuda9-vector-add 0/1 Pending 0 1m <none> <none>
parrec 1/1 Running 0 2m 172.30.0.52 gpu-node-01
# kubectl get pods -o wide
NAME READY STATUS RESTARTS AGE IP NODE
cuda8-binomialoption 0/1 Completed 0 1m 172.30.0.53 gpu-node-01
cuda8-vector-add 0/1 Pending 0 1m <none> <none>
cuda9-binomialoption 1/1 Running 0 1m 172.30.0.54 gpu-node-01
cuda9-vector-add 0/1 Pending 0 1m <none> <none>
parrec 1/1 Running 0 2m 172.30.0.52 gpu-node-01
# kubectl get pods -o wide
NAME READY STATUS RESTARTS AGE IP NODE
cuda8-binomialoption 0/1 Completed 0 2m 172.30.0.53 gpu-node-01
cuda8-vector-add 0/1 Completed 0 2m 172.30.0.55 gpu-node-01
cuda9-binomialoption 0/1 Completed 0 2m 172.30.0.54 gpu-node-01
cuda9-vector-add 0/1 Pending 0 2m <none> <none>
parrec 1/1 Running 0 3m 172.30.0.52 gpu-node-01
# kubectl get pods -o wide
NAME READY STATUS RESTARTS AGE IP NODE
cuda8-binomialoption 0/1 Completed 0 2m 172.30.0.53 gpu-node-01
cuda8-vector-add 0/1 Completed 0 2m 172.30.0.55 gpu-node-01
cuda9-binomialoption 0/1 Completed 0 2m 172.30.0.54 gpu-node-01
cuda9-vector-add 0/1 Completed 0 2m 172.30.0.56 gpu-node-01
parrec 1/1 Running 0 4m 172.30.0.52 gpu-node-01
Access PODs from outside the cluster
To access PODs from outside the cluster it can be possible following different procedures that strictly depend on the usecase and (cloud) providers.
NodePort, hostNetwork, hostPort, LoadBalancer and Ingress features of Kubernetes can be adopted as described in the following Reference:
http://alesnosek.com/blog/2017/02/14/accessing-kubernetes-pods-from-outside-of-the-cluster/
For example puroposes, the Test #5 example will describe and use the NodePort procedure as the cluster is defined as 1 Master and 1 Worker both with routable IPs.
Test #5 - Long running service with NodePort
Prerequisites
Kubernetes Node with routable IP
Port range dynamically selected as from the “kubernetes-apiservice.service” configuration file
Nginx replica 2; V. 1.13.12 - latest (as from the YAML files)
Yaml files related to nginx deployment and nginx service
# cat ngnix.deploy.yaml
apiVersion: extensions/v1beta1
kind: Deployment
metadata:
name: nginx-example
namespace: default
labels:
app: nginx
spec:
replicas: 2
strategy:
type: RollingUpdate
rollingUpdate:
maxSurge: 1
maxUnavailable: 0
template:
metadata:
labels:
app: nginx
name: nginx
spec:
containers:
- image: nginx:latest
name: ingress-example
ports:
- name: http
containerPort: 80
readinessProbe:
httpGet:
path: /
port: 80
scheme: HTTP
livenessProbe:
httpGet:
path: /
port: 80
scheme: HTTP
initialDelaySeconds: 5
timeoutSeconds: 1
# cat ngnix.svc.yaml
apiVersion: v1
kind: Service
metadata:
name: nginx
namespace: default
spec:
type: NodePort
ports:
- name: http
port: 80
targetPort: 80
protocol: TCP
selector:
app: nginx
Creation of nginx POD and nginx service. The following commands will return the Node hostname and the port associated to the nginx.
# kubectl apply -f ngnix.deploy.yaml -f ngnix.svc.yaml
deployment.extensions "nginx-example" created
service "nginx" created
# kubectl get pods
NAME READY STATUS RESTARTS AGE
nginx-example-78847794b7-8nm8t 0/1 Running 0 11s
nginx-example-78847794b7-n8nxs 0/1 Running 0 11s
# kubectl get pods
NAME READY STATUS RESTARTS AGE
nginx-example-78847794b7-8nm8t 1/1 Running 0 30s
nginx-example-78847794b7-n8nxs 1/1 Running 0 30s
# kubectl get svc
NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE
nginx NodePort 192.168.0.130 <none> 80:30916/TCP 51s
Test of nginx
# curl http://gpu-node-01:30916
<!DOCTYPE html>
<html>
<head>
<title>Welcome to nginx!</title>
<style>
…
Delete one of the two PODs from nginx
# kubectl delete pod nginx-example-78847794b7-8nm8t
pod "nginx-example-78847794b7-8nm8t" deleted
A new POD is creating while the old POD is getting deleted. No service downtime is registered from the user
# kubectl get pods
NAME READY STATUS RESTARTS AGE
nginx-example-78847794b7-6gvnn 0/1 Running 0 4s
nginx-example-78847794b7-8nm8t 0/1 Terminating 0 12m
nginx-example-78847794b7-n8nxs 1/1 Running 0 12m
# kubectl get pods
NAME READY STATUS RESTARTS AGE
nginx-example-78847794b7-6gvnn 0/1 Running 0 12s
nginx-example-78847794b7-n8nxs 1/1 Running 0 12m
# kubectl get pods -o wide
NAME READY STATUS RESTARTS AGE IP NODE
nginx-example-78847794b7-6gvnn 1/1 Running 0 28s 172.30.0.61 gpu-node-01
nginx-example-78847794b7-n8nxs 1/1 Running 0 12m 172.30.0.59 gpu-node-01
Delete the seconf POD. The internal POD IP is changing, but the public endpoint is the same.
# kubectl delete pod nginx-example-78847794b7-n8nxs
pod "nginx-example-78847794b7-n8nxs" deleted
# kubectl get pods -o wide
NAME READY STATUS RESTARTS AGE IP NODE
nginx-example-78847794b7-2szlv 0/1 Running 0 4s 172.30.0.62 gpu-node-01
nginx-example-78847794b7-6gvnn 1/1 Running 0 50s 172.30.0.61 gpu-node-01
nginx-example-78847794b7-n8nxs 0/1 Terminating 0 13m 172.30.0.59 gpu-node-01
# kubectl get pods -o wide
NAME READY STATUS RESTARTS AGE IP NODE
nginx-example-78847794b7-2szlv 1/1 Running 0 13s 172.30.0.62 gpu-node-01
nginx-example-78847794b7-6gvnn 1/1 Running 0 59s 172.30.0.61 gpu-node-01
Changing the version of nginx with an older version (v. 1.12).
# cat ngnix.deploy.yaml
apiVersion: extensions/v1beta1
…
containers:
- image: nginx:1.12
Apply changes. New PODs are created while old PODs are deleted. No nginx downtime is registered from the user. Public endpoint and port remain unchanged.
# kubectl apply -f ngnix.deploy.yaml -f ngnix.svc.yaml
deployment.extensions "nginx-example" configured
service "nginx" unchanged
# kubectl get pods
NAME READY STATUS RESTARTS AGE
nginx-example-5d9764f848-8kc8b 0/1 Running 0 9s
nginx-example-78847794b7-2szlv 1/1 Running 0 2m
nginx-example-78847794b7-6gvnn 1/1 Running 0 2m
# kubectl get pods -o wide
NAME READY STATUS RESTARTS AGE IP NODE
nginx-example-5d9764f848-8kc8b 1/1 Running 0 23s 172.30.0.63 gpu-node-01
nginx-example-5d9764f848-xwr77 1/1 Running 0 7s 172.30.0.64 gpu-node-01
nginx-example-78847794b7-6gvnn 0/1 Terminating 0 3m 172.30.0.61 gpu-node-01
# kubectl get pods -o wide
NAME READY STATUS RESTARTS AGE IP NODE
nginx-example-5d9764f848-8kc8b 1/1 Running 0 54s 172.30.0.63 gpu-node-01
nginx-example-5d9764f848-xwr77 1/1 Running 0 38s 172.30.0.64 gpu-node-01
References
The following guides have been followed for the Installation and Configuration of Kubernetes cluster - a detailed step-by-step guide will be provided soon
Kubernetes Installation/Configuration docs
GPU Node - CentOS7.4
Install docker
https://docs.docker.com/install/linux/docker-ce/centos/#set-up-the-repository
Driver nvidia-install repo
Driver Nvidia-install driver
http://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html#post-installation-actions
Install NVIDIA-docker repo
Install-NVIDIA docker
https://github.com/NVIDIA/nvidia-docker/wiki/Installation-(version-2.0)#prerequisites