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Deploy LightGBM model with InferenceService

Train a LightGBM model

To test the LightGBM Server, first you need to train a simple LightGBM model with following python code.

import lightgbm as lgb
from sklearn.datasets import load_iris
import os

model_dir = "."
BST_FILE = "model.bst"

iris = load_iris()
y = iris['target']
X = iris['data']
dtrain = lgb.Dataset(X, label=y, feature_names=iris['feature_names'])

params = {
    'objective':'multiclass', 
    'metric':'softmax',
    'num_class': 3
}
lgb_model = lgb.train(params=params, train_set=dtrain)
model_file = os.path.join(model_dir, BST_FILE)
lgb_model.save_model(model_file)

Deploy LightGBM model with V1 protocol

Test the model locally

Install and run the LightGBM Server using the trained model locally and test the prediction. 

python -m lgbserver --model_dir /path/to/model_dir --model_name lgb

After the LightGBM Server is up locally we can then test the model by sending an inference request.

import requests

request = {'sepal_width_(cm)': {0: 3.5}, 'petal_length_(cm)': {0: 1.4}, 'petal_width_(cm)': {0: 0.2},'sepal_length_(cm)': {0: 5.1} }
formData = {
    'inputs': [request]
}
res = requests.post('http://localhost:8080/v1/models/lgb:predict', json=formData)
print(res)
print(res.text)

Deploy with InferenceService

To deploy the model on Kubernetes you can create the InferenceService by specifying the modelFormat with lightgbm and storageUri. 

apiVersion: "serving.kserve.io/v1beta1"
kind: "InferenceService"
metadata:
  name: "lightgbm-iris"
spec:
  predictor:
    lightgbm:
      storageUri: "gs://kfserving-examples/models/lightgbm/iris"

apiVersion: "serving.kserve.io/v1beta1"
kind: "InferenceService"
metadata:
  name: "lightgbm-iris"
spec:
  predictor:
    model:
      modelFormat:
        name: lightgbm
      storageUri: "gs://kfserving-examples/models/lightgbm/iris"

Apply the above yaml to create the InferenceService

kubectl apply -f lightgbm.yaml

Expected Output

$ inferenceservice.serving.kserve.io/lightgbm-iris created

Test the deployed model

To test the deployed model the first step is to determine the ingress IP and ports and set INGRESS_HOST and INGRESS_PORT, then run the following curl command to send the inference request to the InferenceService.

MODEL_NAME=lightgbm-iris
INPUT_PATH=@./iris-input.json
SERVICE_HOSTNAME=$(kubectl get inferenceservice lightgbm-iris -o jsonpath='{.status.url}' | cut -d "/" -f 3)
curl -v -H "Host: ${SERVICE_HOSTNAME}" http://${INGRESS_HOST}:${INGRESS_PORT}/v1/models/$MODEL_NAME:predict -d $INPUT_PATH

Expected Output

*   Trying 169.63.251.68...
* TCP_NODELAY set
* Connected to 169.63.251.68 (169.63.251.68) port 80 (#0)
> POST /models/lightgbm-iris:predict HTTP/1.1
> Host: lightgbm-iris.default.svc.cluster.local
> User-Agent: curl/7.60.0
> Accept: */*
> Content-Length: 76
> Content-Type: application/x-www-form-urlencoded
>
* upload completely sent off: 76 out of 76 bytes
< HTTP/1.1 200 OK
< content-length: 27
< content-type: application/json; charset=UTF-8
< date: Tue, 21 May 2019 22:40:09 GMT
< server: istio-envoy
< x-envoy-upstream-service-time: 13032
<
* Connection #0 to host 169.63.251.68 left intact
{"predictions": [[0.9, 0.05, 0.05]]}

Deploy the model with V2 protocol

Test the model locally

Once you've got your model serialized model.bst, we can then use MLServer which implements the KServe V2 inference protocol to spin up a local server. For more details on MLServer, please check the LightGBM example doc.

To run MLServer locally, you first install the mlserver package in your local environment, as well as the LightGBM runtime.

pip install mlserver mlserver-lightgbm

The next step is to provide the model settings so that MLServer knows:

  • The inference runtime to serve your model (i.e. mlserver_lightgbm.LightGBMModel)
  • The model's name and version

These can be specified through environment variables or by creating a local model-settings.json file:

{
  "name": "lightgbm-iris",
  "version": "v1.0.0",
  "implementation": "mlserver_lightgbm.LightGBMModel"
}

With the mlserver package installed locally and a local model-settings.json file, you should now be ready to start our server as:

mlserver start .

Deploy InferenceService with REST endpoint

When you deploy your model with InferenceService KServe injects sensible defaults so that it runs out-of-the-box without any further configuration. However, you can still override these defaults by providing a model-settings.json file similar to your local one. You can even provide a set of model-settings.json files to load multiple models.

To deploy the LightGBM model with V2 inference protocol, you need to set the protocolVersion field to v2.

apiVersion: "serving.kserve.io/v1beta1"
kind: "InferenceService"
metadata:
  name: "lightgbm-v2-iris"
spec:
  predictor:
    lightgbm:
      protocolVersion: v2
      storageUri: "gs://kfserving-examples/models/lightgbm/v2/iris"

apiVersion: "serving.kserve.io/v1beta1"
kind: "InferenceService"
metadata:
  name: "lightgbm-v2-iris"
spec:
  predictor:
    model:
      modelFormat:
        name: lightgbm
      protocolVersion: v2
      storageUri: "gs://kfserving-examples/models/lightgbm/v2/iris"

Apply the InferenceService yaml to get the REST endpoint

kubectl apply -f lightgbm-v2.yaml

Expected Output

$ inferenceservice.serving.kserve.io/lightgbm-v2-iris created

Test the deployed model with curl

You can now test your deployed model by sending a sample request.

Note that this request needs to follow the V2 Dataplane protocol. You can see an example payload below:

{
  "inputs": [
    {
      "name": "input-0",
      "shape": [2, 4],
      "datatype": "FP32",
      "data": [
        [6.8, 2.8, 4.8, 1.4],
        [6.0, 3.4, 4.5, 1.6]
      ]
    }
  ]
}

Now, assuming that your ingress can be accessed at ${INGRESS_HOST}:${INGRESS_PORT} or you can follow this instruction to find out your ingress IP and port.

You can use curl to send the inference request as:

SERVICE_HOSTNAME=$(kubectl get inferenceservice lightgbm-v2-iris -o jsonpath='{.status.url}' | cut -d "/" -f 3)

curl -v \
  -H "Host: ${SERVICE_HOSTNAME}" \
  -H "Content-Type: application/json" \
  -d @./iris-input-v2.json \
  http://${INGRESS_HOST}:${INGRESS_PORT}/v2/models/lightgbm-v2-iris/infer

Expected Output

{
  "model_name":"lightgbm-v2-iris",
  "model_version":null,
  "id":"96253e27-83cf-4262-b279-1bd4b18d7922",
  "parameters":null,
  "outputs":[
    {
      "name":"predict",
      "shape":[2,3],
      "datatype":"FP64",
      "parameters":null,
      "data":
        [8.796664107010673e-06,0.9992300031041593,0.0007612002317336916,4.974786820804187e-06,0.9999919650711493,3.0601420299625077e-06]
    }
  ]
}

Create the InferenceService with gRPC endpoint

Create the inference service yaml and expose the gRPC port, currently only one port is allowed to expose either HTTP or gRPC port and by default HTTP port is exposed.

apiVersion: "serving.kserve.io/v1beta1"
kind: "InferenceService"
metadata:
  name: "lightgbm-v2-iris"
spec:
  predictor:
    lightgbm:
      protocolVersion: v2
      storageUri: "gs://kfserving-examples/models/lightgbm/v2/iris"
      ports:
      - name: h2c
        protocol: TCP
        containerPort: 9000

apiVersion: "serving.kserve.io/v1beta1"
kind: "InferenceService"
metadata:
  name: "lightgbm-v2-iris"
spec:
  predictor:
    model:
      modelFormat:
        name: lightgbm
      protocolVersion: v2
      storageUri: "gs://kfserving-examples/models/lightgbm/v2/iris"
      ports:
      - name: h2c
        protocol: TCP
        containerPort: 9000

Apply the InferenceService yaml to get the gRPC endpoint

kubectl apply -f lightgbm-v2-grpc.yaml

Test the deployed model with grpcurl

After the gRPC InferenceService becomes ready, grpcurl, can be used to send gRPC requests to the InferenceService.

# download the proto file
curl -O https://raw.githubusercontent.com/kserve/kserve/master/docs/predict-api/v2/grpc_predict_v2.proto

INPUT_PATH=iris-input-v2-grpc.json
PROTO_FILE=grpc_predict_v2.proto
SERVICE_HOSTNAME=$(kubectl get inferenceservice lightgbm-v2-iris -o jsonpath='{.status.url}' | cut -d "/" -f 3)

The gRPC APIs follow the KServe prediction V2 protocol.

For example, ServerReady API can be used to check if the server is ready:

grpcurl \
  -plaintext \
  -proto ${PROTO_FILE} \
  -authority ${SERVICE_HOSTNAME}" \
  ${INGRESS_HOST}:${INGRESS_PORT} \
  inference.GRPCInferenceService.ServerReady

Expected Output

{
  "ready": true
}

ModelInfer API takes input following the ModelInferRequest schema defined in the grpc_predict_v2.proto file. Notice that the input file differs from that used in the previous curl example.

grpcurl \
  -vv \
  -plaintext \
  -proto ${PROTO_FILE} \
  -authority ${SERVICE_HOSTNAME} \
  -d @ \
  ${INGRESS_HOST}:${INGRESS_PORT} \
  inference.GRPCInferenceService.ModelInfer \
  <<< $(cat "$INPUT_PATH")

Expected Output

Resolved method descriptor:
// The ModelInfer API performs inference using the specified model. Errors are
// indicated by the google.rpc.Status returned for the request. The OK code
// indicates success and other codes indicate failure.
rpc ModelInfer ( .inference.ModelInferRequest ) returns ( .inference.ModelInferResponse );

Request metadata to send:
(empty)

Response headers received:
accept-encoding: identity,gzip
content-type: application/grpc
date: Sun, 25 Sep 2022 10:25:05 GMT
grpc-accept-encoding: identity,deflate,gzip
server: istio-envoy
x-envoy-upstream-service-time: 99

Estimated response size: 91 bytes

Response contents:
{
  "modelName": "lightgbm-v2-iris",
  "outputs": [
    {
      "name": "predict",
      "datatype": "FP64",
      "shape": [
        "2",
        "3"
      ],
      "contents": {
        "fp64Contents": [
          8.796664107010673e-06,
          0.9992300031041593,
          0.0007612002317336916,
          4.974786820804187e-06,
          0.9999919650711493,
          3.0601420299625077e-06
        ]
      }
    }
  ]
}
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