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Quick Start Guide

To quickly get started using ModelMesh Serving, here is a brief guide.

Prerequisites

  • A Kubernetes cluster v 1.16+ with cluster administrative privileges
  • kubectl and kustomize (v4.0.0+)
  • At least 4 vCPU and 8 GB memory. For more details, please see here.

1. Install ModelMesh Serving

Get the latest release

RELEASE=release-0.9
git clone -b $RELEASE --depth 1 --single-branch https://github.com/kserve/modelmesh-serving.git
cd modelmesh-serving

Run install script

kubectl create namespace modelmesh-serving
./scripts/install.sh --namespace modelmesh-serving --quickstart

This will install ModelMesh serving in the modelmesh-serving namespace, along with an etcd and MinIO instances. Eventually after running this script, you should see a Successfully installed ModelMesh Serving! message.

Note: These etcd and MinIO deployments are intended for development/experimentation and not for production.

To see more details about installation, click here.

Verify installation

Check that the pods are running:

kubectl get pods

NAME                                        READY   STATUS    RESTARTS   AGE
pod/etcd                                    1/1     Running   0          5m
pod/minio                                   1/1     Running   0          5m
pod/modelmesh-controller-547bfb64dc-mrgrq   1/1     Running   0          5m

Check that the ServingRuntimes are available:

kubectl get servingruntimes

NAME           DISABLED   MODELTYPE    CONTAINERS   AGE
mlserver-0.x              sklearn      mlserver     5m
triton-2.x                tensorflow   triton       5m

ServingRuntimes are automatically provisioned based on the framework of the model deployed. Two ServingRuntimes are included with ModelMesh Serving by default. The current mappings for these are:

ServingRuntime Supported Frameworks
triton-2.x tensorflow, pytorch, onnx, tensorrt
mlserver-0.x sklearn, xgboost, lightgbm

2. Deploy a model

With ModelMesh Serving now installed, try deploying a model using the KServe InferenceService CRD.

Note: While both the KServe controller and ModelMesh controller will reconcile InferenceService resources, the ModelMesh controller will only handle those InferenceServices with the serving.kserve.io/deploymentMode: ModelMesh annotation. Otherwise, the KServe controller will handle reconciliation. Likewise, the KServe controller will not reconcile an InferenceService with the serving.kserve.io/deploymentMode: ModelMesh annotation, and will defer under the assumption that the ModelMesh controller will handle it.

Here, we deploy an SKLearn MNIST model which is served from the local MinIO container:

kubectl apply -f - <<EOF
apiVersion: serving.kserve.io/v1beta1
kind: InferenceService
metadata:
  name: example-sklearn-isvc
  annotations:
    serving.kserve.io/deploymentMode: ModelMesh
spec:
  predictor:
    model:
      modelFormat:
        name: sklearn
      storage:
        key: localMinIO
        path: sklearn/mnist-svm.joblib
EOF

Note: the above YAML uses the InferenceService predictor storage spec. You can also continue using the storageUri field in lieu of the storage spec:

kubectl apply -f - <<EOF
apiVersion: serving.kserve.io/v1beta1
kind: InferenceService
metadata:
  name: example-sklearn-isvc
  annotations:
    serving.kserve.io/deploymentMode: ModelMesh
    serving.kserve.io/secretKey: localMinIO
spec:
  predictor:
    model:
      modelFormat:
        name: sklearn
      storageUri: s3://modelmesh-example-models/sklearn/mnist-svm.joblib
EOF

After applying this InferenceService, you should see that it is likely not yet ready.

kubectl get isvc

NAME                    URL   READY   PREV   LATEST   PREVROLLEDOUTREVISION   LATESTREADYREVISION   AGE
example-sklearn-isvc          False                                                                 3s

Eventually, you should see the ServingRuntime pods that will hold the SKLearn model become Running.

kubectl get pods

...
modelmesh-serving-mlserver-0.x-7db675f677-twrwd   3/3     Running   0          2m
modelmesh-serving-mlserver-0.x-7db675f677-xvd8q   3/3     Running   0          2m

Then, checking on the InferenceServices again, you should see that the one we deployed is now ready with a provided URL:

kubectl get isvc

NAME                    URL                                               READY   PREV   LATEST   PREVROLLEDOUTREVISION   LATESTREADYREVISION   AGE
example-sklearn-isvc    grpc://modelmesh-serving.modelmesh-serving:8033   True                                                                  97s

You can describe the InferenceService to get more status information:

kubectl describe isvc example-sklearn-isvc

Name:         example-sklearn-isvc
...
Status:
  Components:
    Predictor:
      Grpc URL:  grpc://modelmesh-serving.modelmesh-serving:8033
      Rest URL:  http://modelmesh-serving.modelmesh-serving:8008
      URL:       grpc://modelmesh-serving.modelmesh-serving:8033
  Conditions:
    Last Transition Time:  2022-07-18T18:01:54Z
    Status:                True
    Type:                  PredictorReady
    Last Transition Time:  2022-07-18T18:01:54Z
    Status:                True
    Type:                  Ready
  Model Status:
    Copies:
      Failed Copies:  0
      Total Copies:   2
    States:
      Active Model State:  Loaded
      Target Model State:
    Transition Status:     UpToDate
  URL:                     grpc://modelmesh-serving.modelmesh-serving:8033
...

To see more detailed instructions and information, click here.

3. Perform an inference request

Now that a model is loaded and available, you can then perform inference. Currently, only gRPC inference requests are supported by ModelMesh, but REST support is enabled via a REST proxy container. By default, ModelMesh Serving uses a headless Service since a normal Service has issues load balancing gRPC requests. See more info here.

gRPC request

To test out gRPC inference requests, you can port-forward the headless service in a separate terminal window:

kubectl port-forward --address 0.0.0.0 service/modelmesh-serving  8033 -n modelmesh-serving

Then a gRPC client generated from the KServe grpc_predict_v2.proto file can be used with localhost:8033. A ready-to-use Python example of this can be found here.

Alternatively, you can test inference with grpcurl. This can easily be installed with brew install grpcurl if on macOS.

With grpcurl, a request can be sent to the SKLearn MNIST model like the following. Make sure that the MODEL_NAME variable below is set to the name of your InferenceService.

MODEL_NAME=example-sklearn-isvc
grpcurl \
  -plaintext \
  -proto fvt/proto/kfs_inference_v2.proto \
  -d '{ "model_name": "'"${MODEL_NAME}"'", "inputs": [{ "name": "predict", "shape": [1, 64], "datatype": "FP32", "contents": { "fp32_contents": [0.0, 0.0, 1.0, 11.0, 14.0, 15.0, 3.0, 0.0, 0.0, 1.0, 13.0, 16.0, 12.0, 16.0, 8.0, 0.0, 0.0, 8.0, 16.0, 4.0, 6.0, 16.0, 5.0, 0.0, 0.0, 5.0, 15.0, 11.0, 13.0, 14.0, 0.0, 0.0, 0.0, 0.0, 2.0, 12.0, 16.0, 13.0, 0.0, 0.0, 0.0, 0.0, 0.0, 13.0, 16.0, 16.0, 6.0, 0.0, 0.0, 0.0, 0.0, 16.0, 16.0, 16.0, 7.0, 0.0, 0.0, 0.0, 0.0, 11.0, 13.0, 12.0, 1.0, 0.0] }}]}' \
  localhost:8033 \
  inference.GRPCInferenceService.ModelInfer

This should give you output like the following:

{
  "modelName": "example-sklearn-isvc__isvc-3642375d03",
  "outputs": [
    {
      "name": "predict",
      "datatype": "INT64",
      "shape": ["1"],
      "contents": {
        "int64Contents": ["8"]
      }
    }
  ]
}

REST request

Note: The REST proxy is currently in an alpha state and may still have issues with certain usage scenarios.

You will need to port-forward a different port for REST.

kubectl port-forward --address 0.0.0.0 service/modelmesh-serving 8008 -n modelmesh-serving

With curl, a request can be sent to the SKLearn MNIST model like the following. Make sure that the MODEL_NAME variable below is set to the name of your InferenceService.

MODEL_NAME=example-sklearn-isvc
curl -X POST -k http://localhost:8008/v2/models/${MODEL_NAME}/infer -d '{"inputs": [{ "name": "predict", "shape": [1, 64], "datatype": "FP32", "data": [0.0, 0.0, 1.0, 11.0, 14.0, 15.0, 3.0, 0.0, 0.0, 1.0, 13.0, 16.0, 12.0, 16.0, 8.0, 0.0, 0.0, 8.0, 16.0, 4.0, 6.0, 16.0, 5.0, 0.0, 0.0, 5.0, 15.0, 11.0, 13.0, 14.0, 0.0, 0.0, 0.0, 0.0, 2.0, 12.0, 16.0, 13.0, 0.0, 0.0, 0.0, 0.0, 0.0, 13.0, 16.0, 16.0, 6.0, 0.0, 0.0, 0.0, 0.0, 16.0, 16.0, 16.0, 7.0, 0.0, 0.0, 0.0, 0.0, 11.0, 13.0, 12.0, 1.0, 0.0]}]}'

This should give you a response like the following:

{
  "model_name": "example-sklearn-isvc__ksp-7702c1b55a",
  "outputs": [
    {
      "name": "predict",
      "datatype": "FP32",
      "shape": [1],
      "data": [8]
    }
  ]
}

To see more detailed instructions and information, click here.

4. (Optional) Deleting your ModelMesh Serving installation

To delete all ModelMesh Serving resources that were installed, run the following from the root of the project:

./scripts/delete.sh --namespace modelmesh-serving