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Nezha: Kubernetes Native Big Data Accelerator for Machine Learning

Why?

ML/DL training performance using datasets stored at S3/GCS/Azure is subject to rate limiting and suboptimal downloading throughput.

Nezha automatically rewrites training jobs' Pod spec, reroutes S3/GCS/Azure requests to local cache, and thus accelerates overall performance and scalability.

Mechanism

The Pod spec is modified by a mutating webhook. The webhook inspects deployments' or jobs' app label and applies host aliases to Pod spec. Once the containers are up and running, S3/GCS/Azure requests are redirected to proxy's endpoint.

Instruction

Overview

Nezha just needs the host aliases that Nezha acts as a reverse proxy, the Kubernetes Job or Deployment labels that Nezha's Webhook checks before injecting the hostaliases.

In the demo, the hostaliases configmap is as the follow. kubeflow creates Jobs and labels them as app.kubernetes.io/deploy-manager: ksonnet. That is how the hostaliases configuration below sets this label.

apiVersion: v1
kind: ConfigMap
metadata:
  name: hostaliases-config
data:
  config: |
      - name: dataset
        app: app.kubernetes.io/deploy-manager
        label: ksonnet

The hostaliases are retrieved once the reverse proxy service is up. As seen in the setup.sh scrtip, this is done via

kubectl get svc -n ${NAMESPACE} proxy-cache -o jsonpath={.spec.clusterIP}

Last, the remote servers that are proxy'ed are extracted from nginx configuration via

    SERVERS=$(grep server_name nginx.conf |tr -d ';' |awk '{print $2}')

Putting together, the setup.sh script does the following:

  • create nginx service
  • create signed certificates that are used by Webhook's TLS enabled HTTP server
  • create Webhook
  • create a configmap to store configurations for hostaliaes and expected Jobs or Deployments' labels

Setup Reverse Proxy Cache Service and Webhook

cd examples/demo
setup.sh start

You should expect the following service and pod running:

  • Reverse Proxy Cache
# kubectl get svc -n nezha-demo
NAME          TYPE        CLUSTER-IP    EXTERNAL-IP   PORT(S)   AGE
proxy-cache   ClusterIP   10.99.81.48   <none>        80/TCP    16m
# kubectl get pod -n nezha-demo
NAME                           READY   STATUS    RESTARTS   AGE
proxy-cache-655ff74648-bhg6r   1/1     Running   0          17m
  • Webhook
# kubectl get pod -l app=hostaliases-injector
 NAME                                                       READY   STATUS    RESTARTS   AGE
 hostaliases-injector-webhook-deployment-7b66fddb9d-b5xlg   1/1     Running   0          19m

Run a test job that downloads a dataset

kubectl apply -f examples/demo/kubeflow-test.yaml

The first time to run the test, the log from pod is like:

# kubectl logs -n nezha-test nezha-job-test-284rb |tail
164700K .......... .......... .......... .......... .......... 99% 15.2M 0s
164750K .......... .......... .......... .......... .......... 99% 2.10M 0s
164800K .......... .......... .......... .......... .......... 99% 7.67M 0s
164850K .......... .......... .......... .......... .......... 99% 2.23M 0s
164900K .......... .......... .......... .......... .......... 99% 56.7M 0s
164950K .......... .......... .......... .......... .......... 99% 6.36M 0s
165000K .......... .......... .......... ..........           100% 1.89M=46s

2018-11-07 19:39:45 (3.49 MB/s) - 'cifar-100-python.tar.gz' saved [169001437/169001437]

The download rate is 3.49 MB/s.

Run the job again:

kubectl delete -f examples/demo/kubeflow-test.yaml
kubectl apply -f examples/demo/kubeflow-test.yaml

Then the file is cached and download is much faster:

# kubectl logs -n nezha-test nezha-job-test-zs9bv |tail
164700K .......... .......... .......... .......... .......... 99%  449M 0s
164750K .......... .......... .......... .......... .......... 99%  371M 0s
164800K .......... .......... .......... .......... .......... 99%  396M 0s
164850K .......... .......... .......... .......... .......... 99%  448M 0s
164900K .......... .......... .......... .......... .......... 99%  445M 0s
164950K .......... .......... .......... .......... .......... 99%  456M 0s
165000K .......... .......... .......... ..........           100%  366M=0.5s

2018-11-07 19:41:15 (323 MB/s) - 'cifar-100-python.tar.gz' saved [169001437/169001437]

Download rate is 323 MB/s in the second run.

As observed, once the data is cached, the subsequent download is much faster: 323MB/s vs 3.49MB/s.

In practice, the proxy can run on a PV that has all the needed dataset beforehand.

Clean up Reverse Proxy Cache Service and Webhook

cd examples/demo
setup.sh clean

Acknowledgement

Some initial implementation of initializer is based on https://github.com/kelseyhightower/kubernetes-initializer-tutorial Some initial webhook implementation is based on Kubernetes e2e tests.