Machine learning with Vineyard on KubernetesΒΆ

In this demonstration, we will build a fraudulent transaction classifier for fraudulent transaction data. The process consists of the following three main steps:

  • prepare-data: Utilize Vineyard to read and store data in a distributed manner.

  • process-data: Employ Mars to process the data across multiple nodes.

  • train-data: Use Pytorch to train the model on the distributed data.

We have three tables: user table, product table, and transaction table. The user and product tables primarily contain user and product IDs, along with their respective Feature vectors. Each record in the transaction table indicates a user purchasing a product, with a Fraud label identifying whether the transaction is fraudulent. Additional features related to these transactions are also stored in the transaction table. You can find the three tables in the dataset repo. Follow the steps below to reproduce the demonstration. First, create a vineyard cluster with 3 worker nodes.

$ cd k8s && make -C k8s/test/e2e install-vineyard-cluster

Expected output

the kubeconfig path is /tmp/e2e-k8s.config
Creating the kind cluster with local registry
Creating cluster "kind" ...
βœ“ Ensuring node image (kindest/node:v1.24.0) πŸ–Ό
βœ“ Preparing nodes πŸ“¦ πŸ“¦ πŸ“¦ πŸ“¦
βœ“ Writing configuration πŸ“œ
βœ“ Starting control-plane πŸ•ΉοΈ
βœ“ Installing CNI πŸ”Œ
βœ“ Installing StorageClass πŸ’Ύ
βœ“ Joining worker nodes 🚜
Set kubectl context to "kind-kind"
You can now use your cluster with:

kubectl cluster-info --context kind-kind --kubeconfig /tmp/e2e-k8s.config

Thanks for using kind! 😊
configmap/local-registry-hosting created
Installing vineyard-operator...
The push refers to repository [localhost:5001/vineyard-operator]
c3a672704524: Pushed
b14a7037d2e7: Pushed
8d7366c22fd8: Pushed
latest: digest: sha256:ea06c833351f19c5db28163406c55e2108676c27fdafea7652500c55ce333b9d size: 946
make[1]: Entering directory '/opt/caoye/v6d/k8s'
go: creating new go.mod: module tmp
/home/gsbot/go/bin/controller-gen rbac:roleName=manager-role crd:maxDescLen=0 webhook paths="./..." output:crd:artifacts:config=config/crd/bases
cd config/manager && /usr/local/bin/kustomize edit set image controller=localhost:5001/vineyard-operator:latest
/usr/local/bin/kustomize build config/default | kubectl apply -f -
namespace/vineyard-system created created created created created created created created
serviceaccount/vineyard-manager created created created created created created created created created created created created created
service/vineyard-controller-manager-metrics-service created
service/vineyard-webhook-service created
deployment.apps/vineyard-controller-manager created created created
make[1]: Leaving directory '/opt/caoye/v6d/k8s'
deployment.apps/vineyard-controller-manager condition met
Vineyard-Operator Ready
Installing vineyard cluster... created condition met
Vineyard cluster Ready

Verify that all Vineyard pods are running.

$ KUBECONFIG=/tmp/e2e-k8s.config kubectl get pod -n vineyard-system

Expected output

NAME                                           READY   STATUS    RESTARTS   AGE
etcd0                                          1/1     Running   0          68s
etcd1                                          1/1     Running   0          68s
etcd2                                          1/1     Running   0          68s
vineyard-controller-manager-7f569b57c5-46tgq   2/2     Running   0          92s
vineyardd-sample-6ffcb96cbc-gs2v9              1/1     Running   0          67s
vineyardd-sample-6ffcb96cbc-n59gg              1/1     Running   0          67s
vineyardd-sample-6ffcb96cbc-xwpzd              1/1     Running   0          67s

First, let’s prepare the dataset and download it into the kind worker nodes as follows.

$ worker=($(docker ps | grep kind-worker | awk -F ' ' '{print $1}'))
$ for c in ${worker[@]}; do \
    docker exec $c sh -c "\
        mkdir -p /datasets; \
        cd /datasets/; \
        curl -OL{item,txn,user}.csv" \

The prepare-data job primarily reads the datasets and distributes them across different Vineyard nodes. For more information, please refer to the prepare data code. To apply the job, follow the steps below:


The prepare-data job needs to exec into the other pods. Therefore, you need to create a service account and bind it to the role under the namespace. Please make sure you can have permission to create the following role.

- apiGroups: [""]
  resources: ["pods", "pods/log", "pods/exec"]
  verbs: ["get", "patch", "delete", "create", "watch", "list"]
$ kubectl create ns vineyard-job && \
kubectl apply -f showcase/vineyard-mars-pytorch/prepare-data/resources && \
kubectl wait job -n vineyard-job -l app=prepare-data --for condition=complete --timeout=1200s

Expected output

namespace/vineyard-job created created created
job.batch/prepare-data created
serviceaccount/prepare-data created
job.batch/prepare-data condition met


The process-data job needs to create a new namespace and deploy several kubernetes resources in it. Please make sure you can have permission to create the following role.

- apiGroups: [""]
  resources: ["pods", "pods/exec", "pods/log", "endpoints", "services"]
  verbs: ["get", "patch", "delete", "create", "watch", "list"]
- apiGroups: [""]
  resources: ["namespaces"]
  verbs: ["get", "create", "delete"]
- apiGroups: [""]
  resources: ["nodes"]
  verbs: ["get", "list"]
- apiGroups: [""]
  resources: ["roles", "rolebindings"]
  verbs: ["patch", "get", "create", "delete"]
- apiGroups: ["apps"]
  resources: ["deployments"]
  verbs: ["create"]

Notice, the process-data job will require lots of permissions to deal kubernetes resources, so please check the image of process-data job if it is an official one.

The prepare-data job creates numerous dataframes in Vineyard. To combine these dataframes, we use the appropriate join method in mars. For more details, refer to the process data code. Apply the process-data job as follows:

$ kubectl apply -f showcase/vineyard-mars-pytorch/process-data/resources && \
  kubectl wait job -n vineyard-job -l app=process-data --for condition=complete --timeout=1200s

Finally, apply the train-data job to obtain the fraudulent transaction classifier. You can also view the train data code.

$ kubectl apply -f k8s/showcase/vineyard-mars-pytorch/train-data/resources && \
  kubectl wait pods -n vineyard-job -l app=train-data --for condition=Ready --timeout=1200s

If any of the above steps fail, please refer to the mars showcase e2e test for further guidance.