This repository contains an example resource driver for use with the Dynamic Resource Allocation (DRA) feature of Kubernetes.
It is intended to demonstrate best-practices for how to construct a DRA resource driver and wrap it in a helm chart. It can be used as a starting point for implementing a driver for your own set of resources.
Before diving into the details of how this example driver is constructed, it's useful to run through a quick demo of it in action.
The driver itself provides access to a set of mock GPU devices, and this demo walks through the process of building and installing the driver followed by running a set of workloads that consume these GPUs.
The procedure below has been tested and verified on both Linux and Mac.
We start by first cloning this repository and cding into its demo
subdirectory. All of the scripts and example Pod specs used in this demo are
contained here, so take a moment to browse through the various files and see
what's available:
git clone https://github.com/kubernetes-sigs/dra-example-driver.git
cd dra-example-driver/demo
From here we will build the image for the example resource driver:
./build-driver.shAnd create a kind cluster to run it in:
./create-cluster.shOnce the cluster has been created successfully, double check everything is coming up as expected:
$ kubectl get pod -A
NAMESPACE NAME READY STATUS RESTARTS AGE
kube-system coredns-5d78c9869d-6jrx9 1/1 Running 0 1m
kube-system coredns-5d78c9869d-dpr8p 1/1 Running 0 1m
kube-system etcd-dra-example-driver-cluster-control-plane 1/1 Running 0 1m
kube-system kindnet-g88bv 1/1 Running 0 1m
kube-system kindnet-msp95 1/1 Running 0 1m
kube-system kube-apiserver-dra-example-driver-cluster-control-plane 1/1 Running 0 1m
kube-system kube-controller-manager-dra-example-driver-cluster-control-plane 1/1 Running 0 1m
kube-system kube-proxy-kgz4z 1/1 Running 0 1m
kube-system kube-proxy-x6fnd 1/1 Running 0 1m
kube-system kube-scheduler-dra-example-driver-cluster-control-plane 1/1 Running 0 1m
local-path-storage local-path-provisioner-7dbf974f64-9jmc7 1/1 Running 0 1mAnd then install the example resource driver via helm:
helm upgrade -i \
--create-namespace \
--namespace dra-example-driver \
dra-example-driver \
../deployments/helm/dra-example-driverDouble check the driver components have come up successfully:
$ kubectl get pod -n dra-example-driver
NAME READY STATUS RESTARTS AGE
dra-example-driver-controller-7555d488db-nbd52 1/1 Running 0 1m
dra-example-driver-kubeletplugin-qwmbl 1/1 Running 0 1mAnd show the initial state of available GPU devices on the worker node:
$ kubectl describe -n dra-example-driver nas/dra-example-driver-cluster-worker
...
Spec:
Allocatable Devices:
Gpu:
Product Name: LATEST-GPU-MODEL
Uuid: GPU-9ede7e32-5825-a11b-fa3d-bab6d47e0243
Gpu:
Product Name: LATEST-GPU-MODEL
Uuid: GPU-e7b42cb1-4fd8-91b2-bc77-352a0c1f5747
Gpu:
Product Name: LATEST-GPU-MODEL
Uuid: GPU-f11773a1-5bfb-e48b-3d98-1beb5baaf08e
Gpu:
Product Name: LATEST-GPU-MODEL
Uuid: GPU-0159f35e-99ee-b2b5-74f1-9d18df3f22ac
Gpu:
Product Name: LATEST-GPU-MODEL
Uuid: GPU-657bd2e7-f5c2-a7f2-fbaa-0d1cdc32f81b
Gpu:
Product Name: LATEST-GPU-MODEL
Uuid: GPU-18db0e85-99e9-c746-8531-ffeb86328b39
Gpu:
Product Name: LATEST-GPU-MODEL
Uuid: GPU-93d37703-997c-c46f-a531-755e3e0dc2ac
Gpu:
Product Name: LATEST-GPU-MODEL
Uuid: GPU-ee3e4b55-fcda-44b8-0605-64b7a9967744
...Next, deploy four example apps that demonstrate how ResourceClaims,
ResourceClaimTemplates, and custom ClaimParameter objects can be used to
request access to resources in various ways:
kubectl apply --filename=gpu-test{1,2,3,4}.yamlAnd verify that they are coming up successfully:
$ kubectl get pod -A
NAMESPACE NAME READY STATUS RESTARTS AGE
...
gpu-test1 pod1 0/1 Pending 0 2s
gpu-test1 pod2 0/1 Pending 0 2s
gpu-test2 pod 0/2 Pending 0 2s
gpu-test3 pod1 0/1 ContainerCreating 0 2s
gpu-test3 pod2 0/1 ContainerCreating 0 2s
gpu-test4 pod 0/1 Pending 0 2s
...Use your favorite editor to look through each of the gpu-test{1,2,3,4}.yaml
files and see what they are doing. The semantics of each match the figure
below:
Then dump the logs of each app to verify that GPUs were allocated to them according to these semantics:
for example in $(seq 1 4); do \
echo "gpu-test${example}:"
for pod in $(kubectl get pod -n gpu-test${example} --output=jsonpath='{.items[*].metadata.name}'); do \
for ctr in $(kubectl get pod -n gpu-test${example} ${pod} -o jsonpath='{.spec.containers[*].name}'); do \
echo "${pod} ${ctr}:"
kubectl logs -n gpu-test${example} ${pod} -c ${ctr}| grep GPU_DEVICE
done
done
echo ""
doneThis should produce output similar to the following:
gpu-test1:
pod1 ctr:
declare -x GPU_DEVICE_0="GPU-657bd2e7-f5c2-a7f2-fbaa-0d1cdc32f81b"
pod2 ctr:
declare -x GPU_DEVICE_0="GPU-ee3e4b55-fcda-44b8-0605-64b7a9967744"
gpu-test2:
pod ctr0:
declare -x GPU_DEVICE_0="GPU-9ede7e32-5825-a11b-fa3d-bab6d47e0243"
pod ctr1:
declare -x GPU_DEVICE_0="GPU-9ede7e32-5825-a11b-fa3d-bab6d47e0243"
gpu-test3:
pod1 ctr:
declare -x GPU_DEVICE_0="GPU-93d37703-997c-c46f-a531-755e3e0dc2ac"
pod2 ctr:
declare -x GPU_DEVICE_0="GPU-93d37703-997c-c46f-a531-755e3e0dc2ac"
gpu-test4:
pod ctr:
declare -x GPU_DEVICE_0="GPU-18db0e85-99e9-c746-8531-ffeb86328b39"
declare -x GPU_DEVICE_1="GPU-e7b42cb1-4fd8-91b2-bc77-352a0c1f5747"
declare -x GPU_DEVICE_2="GPU-f11773a1-5bfb-e48b-3d98-1beb5baaf08e"
declare -x GPU_DEVICE_3="GPU-0159f35e-99ee-b2b5-74f1-9d18df3f22ac"In this example resource driver, no "actual" GPUs are made available to any containers. Instead, a set of environment variables are set in each container to indicate which GPUs would have been injected into them by a real resource driver.
You can use the UUIDs of the GPUs set in these environment variables to verify that they were handed out in a way consistent with the semantics shown in the figure above.
Likewise, looking at the ClaimAllocations section of the
NodeAllocationState object on the worker node will show which GPUs have been
allocated to a given ResourceClaim by the resource driver:
$ kubectl describe -n dra-example-driver nas/dra-example-driver-cluster-worker
...
Spec:
...
Claim Allocations:
1c8364bc-b45e-4b26-b74b-d24aea518319:
Gpu:
Productname: LATEST-GPU-MODEL
Uuid: GPU-ee3e4b55-fcda-44b8-0605-64b7a9967744
Gpu:
Productname: LATEST-GPU-MODEL
Uuid: GPU-9ede7e32-5825-a11b-fa3d-bab6d47e0243
Gpu:
Productname: LATEST-GPU-MODEL
Uuid: GPU-e7b42cb1-4fd8-91b2-bc77-352a0c1f5747
Gpu:
Productname: LATEST-GPU-MODEL
Uuid: GPU-657bd2e7-f5c2-a7f2-fbaa-0d1cdc32f81b
30a54813-002f-45f0-bac3-06ed401d31ad:
Gpu:
Productname: LATEST-GPU-MODEL
Uuid: GPU-0159f35e-99ee-b2b5-74f1-9d18df3f22ac
37871101-7230-4b83-bda7-3d9e581ba515:
Gpu:
Productname: LATEST-GPU-MODEL
Uuid: GPU-f11773a1-5bfb-e48b-3d98-1beb5baaf08e
7c6ca074-26ca-40b7-b71a-e06903ca13b7:
Gpu:
Productname: LATEST-GPU-MODEL
Uuid: GPU-93d37703-997c-c46f-a531-755e3e0dc2ac
e16500f2-21c8-45e0-a6dc-280acfbcab4f:
Gpu:
Productname: LATEST-GPU-MODEL
Uuid: GPU-18db0e85-99e9-c746-8531-ffeb86328b39Once you have verified everything is running correctly, delete all of the example apps:
kubectl delete --wait=false --filename=gpu-test{1,2,3,4}.yamlWait for them to terminate:
$ kubectl get pod -A
NAMESPACE NAME READY STATUS RESTARTS AGE
...
gpu-test1 pod1 1/1 Terminating 0 31m
gpu-test1 pod2 1/1 Terminating 0 31m
gpu-test2 pod 2/2 Terminating 0 31m
gpu-test3 pod1 1/1 Terminating 0 31m
gpu-test3 pod2 1/1 Terminating 0 31m
gpu-test4 pod 1/1 Terminating 0 31m
...And show that the ClaimAllocations section of the NodeAllocationState
object on the worker node is now back to its initial state:
$ kubectl describe -n dra-example-driver nas/dra-example-driver-cluster-worker
...
Spec:
Allocatable Devices:
Gpu:
Product Name: LATEST-GPU-MODEL
Uuid: GPU-657bd2e7-f5c2-a7f2-fbaa-0d1cdc32f81b
Gpu:
Product Name: LATEST-GPU-MODEL
Uuid: GPU-18db0e85-99e9-c746-8531-ffeb86328b39
Gpu:
Product Name: LATEST-GPU-MODEL
Uuid: GPU-93d37703-997c-c46f-a531-755e3e0dc2ac
Gpu:
Product Name: LATEST-GPU-MODEL
Uuid: GPU-ee3e4b55-fcda-44b8-0605-64b7a9967744
Gpu:
Product Name: LATEST-GPU-MODEL
Uuid: GPU-9ede7e32-5825-a11b-fa3d-bab6d47e0243
Gpu:
Product Name: LATEST-GPU-MODEL
Uuid: GPU-e7b42cb1-4fd8-91b2-bc77-352a0c1f5747
Gpu:
Product Name: LATEST-GPU-MODEL
Uuid: GPU-f11773a1-5bfb-e48b-3d98-1beb5baaf08e
Gpu:
Product Name: LATEST-GPU-MODEL
Uuid: GPU-0159f35e-99ee-b2b5-74f1-9d18df3f22ac
...Finally, you can run the following to cleanup your environment and delete the
kind cluster started previously:
./delete-cluster.sh
...TBD
TBD
TBD
For more information on the DRA Kubernetes feature and developing custom resource drivers, see the following resources:
Learn how to engage with the Kubernetes community on the community page.
You can reach the maintainers of this project at:
Participation in the Kubernetes community is governed by the Kubernetes Code of Conduct.