End to end inference service example with Minio and Kafka¶
This example shows an end to end inference pipeline which processes an kafka event and invoke the inference service to get the prediction with provided pre/post processing code.
Deploy Kafka¶
If you do not have an existing kafka cluster, you can run the following commands to install in-cluster kafka using helm3 with persistence turned off.
helm repo add confluentinc https://confluentinc.github.io/cp-helm-charts/
helm repo update
helm install my-kafka -f values.yaml --set cp-schema-registry.enabled=false,cp-kafka-rest.enabled=false,cp-kafka-connect.enabled=false confluentinc/cp-helm-charts
after successful install you are expected to see the running kafka cluster
NAME READY STATUS RESTARTS AGE
my-kafka-cp-kafka-0 2/2 Running 0 126m
my-kafka-cp-kafka-1 2/2 Running 1 126m
my-kafka-cp-kafka-2 2/2 Running 0 126m
my-kafka-cp-zookeeper-0 2/2 Running 0 127m
Install Knative Eventing and Kafka Event Source¶
- Install Knative Eventing Core >= 0.18
kubectl apply -f https://github.com/knative/eventing/releases/download/v0.25.0/eventing-crds.yaml kubectl apply -f https://github.com/knative/eventing/releases/download/v0.25.0/eventing-core.yaml
- Install Kafka Event Source.
kubectl apply -f https://github.com/knative-sandbox/eventing-kafka/releases/download/v0.25.3/source.yaml
- Install
InferenceService
addressable cluster rolekubectl apply -f addressable-resolver.yaml
Deploy Minio¶
-
If you do not have Minio setup in your cluster, you can run following command to install Minio test instance.
kubectl apply -f minio.yaml
-
Install Minio client mc
# Run port forwarding command in a different terminal kubectl port-forward $(kubectl get pod --selector="app=minio" --output jsonpath='{.items[0].metadata.name}') 9000:9000 mc config host add myminio http://127.0.0.1:9000 minio minio123
-
Create buckets
mnist
for uploading images anddigit-[0-9]
for classification.mc mb myminio/mnist mc mb myminio/digit-[0-9]
-
Setup event notification to publish events to kafka.
# Setup bucket event notification with kafka mc admin config set myminio notify_kafka:1 tls_skip_verify="off" queue_dir="" queue_limit="0" sasl="off" sasl_password="" sasl_username="" tls_client_auth="0" tls="off" client_tls_cert="" client_tls_key="" brokers="my-kafka-cp-kafka-headless:9092" topic="mnist" version="" # Restart minio mc admin service restart myminio # Setup event notification when putting images to the bucket mc event add myminio/mnist arn:minio:sqs::1:kafka -p --event put --suffix .png
Upload the mnist model to Minio¶
gsutil cp -r gs://kfserving-examples/models/tensorflow/mnist .
mc cp -r mnist myminio/
Create S3 Secret for Minio and attach to Service Account¶
KServe
gets the secrets from your service account, you need to add the created or existing secret to your service account's secret list.
By default KServe
uses default
service account, user can use own service account and overwrite on InferenceService
CRD.
Apply the secret and attach the secret to the service account.
kubectl apply -f s3-secret.yaml
Build mnist transformer image¶
The transformation image implements the preprocess handler to process the minio notification event to download the image from minio and transform image bytes to tensors. The postprocess handler processes the prediction and upload the image to the classified minio bucket digit-[0-9]
.
docker build -t $USER/mnist-transformer:latest -f ./transformer.Dockerfile . --rm
docker push $USER/mnist-transformer:latest
Create the InferenceService¶
Specify the built image on Transformer
spec and apply the inference service CRD.
kubectl apply -f mnist-kafka.yaml
This creates transformer and predictor pods, the request goes to transformer first where it invokes the preprocess handler, transformer then calls out to predictor to get the prediction response which in turn invokes the postprocess handler.
kubectl get pods -l serving.kserve.io/inferenceservice=mnist
mnist-predictor-default-9t5ms-deployment-74f5cd7767-khthf 2/2 Running 0 10s
mnist-transformer-default-jmf98-deployment-8585cbc748-ftfhd 2/2 Running 0 14m
Create kafka event source¶
Apply kafka event source which creates the kafka consumer pod to pull the events from kafka and deliver to inference service.
kubectl apply -f kafka-source.yaml
This creates the kafka source pod which consumers the events from mnist
topic
kafkasource-kafka-source-3d809fe2-1267-11ea-99d0-42010af00zbn5h 1/1 Running 0 8h
Upload a digit image to Minio mnist bucket¶
The last step is to upload the image images/0.png
, image then should be moved to the classified bucket based on the prediction response!
mc cp images/0.png myminio/mnist
mnist
after uploading an image in mnist
bucket
{
"EventType":"s3:ObjectCreated:Put",
"Key":"mnist/0.png",
"Records":[
{"eventVersion":"2.0",
"eventSource":"minio:s3",
"awsRegion":"",
"eventTime":"2019-11-17T19:08:08Z",
"eventName":"s3:ObjectCreated:Put",
"userIdentity":{"principalId":"minio"},
"requestParameters":{"sourceIPAddress":"127.0.0.1:37830"},
"responseElements":{"x-amz-request-id":"15D808BF706E0994",
"x-minio-origin-endpoint":"http://10.244.0.71:9000"},
"s3":{
"s3SchemaVersion":"1.0",
"configurationId":"Config",
"bucket":{
"name":"mnist",
"ownerIdentity":{"principalId":"minio"},
"arn":"arn:aws:s3:::mnist"},
"object":{"key":"0.png","size":324,"eTag":"ebed21f6f77b0a64673a3c96b0c623ba","contentType":"image/png","userMetadata":{"content-type":"image/png"},"versionId":"1","sequencer":"15D808BF706E0994"}},
"source":{"host":"","port":"","userAgent":""}}
],
"level":"info",
"msg":"",
"time":"2019-11-17T19:08:08Z"
}
Check the transformer log, you should expect a prediction response and put the image to the corresponding bucket
kubectl logs mnist-transformer-default-rctjm-deployment-54d59c849c-2dq98 kserve-container
[I 201128 22:32:27 kfserver:88] Registering model: mnist
[I 201128 22:32:27 kfserver:77] Listening on port 8080
[I 201128 22:32:27 kfserver:79] Will fork 0 workers
[I 201128 22:32:27 process:123] Starting 6 processes
[I 201128 22:32:44 connectionpool:203] Starting new HTTP connection (1): minio-service
[I 201128 22:32:58 image_transformer:51] {'predictions': [{'predictions': [0.0247901566, 1.37231364e-05, 0.0202635303, 0.39037028, 0.000513458275, 0.435112566, 0.000607515569, 0.00041125578, 0.127784252, 0.000133168287], 'classes': 5}]}
[I 201128 22:32:58 image_transformer:53] digit:5