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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:
    model:
      modelFormat:
        name: lightgbm
      storageUri: "gs://kfserving-examples/models/lightgbm/iris"

apiVersion: "serving.kserve.io/v1beta1"
kind: "InferenceService"
metadata:
  name: "lightgbm-iris"
spec:
  predictor:
    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 Open Inference Protocol

Test the model locally

Once you've got your model serialized model.bst, we can then use KServe LightGBM Server to create a local model server.

Note

This step is optional and just meant for testing, feel free to jump straight to deploying with InferenceService.

Pre-requisites

Firstly, to use kserve lightgbm server locally, you will first need to install the lgbserver runtime package in your local environment.

  1. Clone the KServe repository and navigate into the directory. 
    git clone https://github.com/kserve/kserve
  2. Install lgbserver runtime. KServe uses Poetry as the dependency management tool. Make sure you have already installed poetry. 
    cd python/lgbserver
poetry install

Serving model locally

The lgbserver package takes three arguments.

  • --model_dir: The model directory path where the model is stored.
  • --model_name: The name of the model deployed in the model server, the default value is model. This is optional.
  • --nthread: Number of threads to use by LightGBM. This is optional and the default value is 1.

With the lgbserver runtime package installed locally, you should now be ready to start our server as:

python3 lgbserver --model_dir /path/to/model_dir --model_name lightgbm-v2-iris

Deploy InferenceService with REST endpoint

To deploy the LightGBM model with Open 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:
    model:
      modelFormat:
        name: lightgbm
      runtime: kserve-lgbserver
      protocolVersion: v2
      storageUri: "gs://kfserving-examples/models/lightgbm/v2/iris"

Note

For V2 protocol (open inference protocol) if runtime field is not provided then, by default mlserver runtime is used.

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. Create a file named iris-input-v2.json with the sample input.

{
  "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.

Note

Currently, KServe only supports exposing either HTTP or gRPC port. By default, HTTP port is exposed.

apiVersion: "serving.kserve.io/v1beta1"
kind: "InferenceService"
metadata:
  name: "lightgbm-v2-iris-grpc"
spec:
  predictor:
    model:
      modelFormat:
        name: lightgbm
      protocolVersion: v2
      runtime: kserve-lgbserver
      storageUri: "gs://kfserving-examples/models/lightgbm/v2/iris"
      ports:
        - name: h2c          # knative expects grpc port name to be 'h2c'
          protocol: TCP
          containerPort: 8081

apiVersion: "serving.kserve.io/v1beta1"
kind: "InferenceService"
metadata:
  name: "lightgbm-v2-iris-grpc"
spec:
  predictor:
    model:
      modelFormat:
        name: lightgbm
      protocolVersion: v2
      runtime: kserve-lgbserver
      storageUri: "gs://kfserving-examples/models/lightgbm/v2/iris"
      ports:
        - name: grpc-port      # Istio requires the port name to be in the format <protocol>[-<suffix>]
          protocol: TCP
          containerPort: 8081

Note

For V2 protocol (open inference protocol) if runtime field is not provided then, by default mlserver runtime is used.

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/open-inference-protocol/main/specification/protocol/open_inference_grpc.proto

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

Determine the ingress IP and port and set INGRESS_HOST and INGRESS_PORT. Now, you can use curl to send the inference requests. The gRPC APIs follow the KServe prediction V2 protocol / Open Inference 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
}

You can test the deployed model by sending a sample request with the below payload. Notice that the input format differs from the in the previous REST endpoint example. Prepare the inference input inside the file named iris-input-v2-grpc.json. 

{
  "model_name": "lightgbm-v2-iris-grpc",
  "inputs": [
    {
      "name": "input-0",
      "shape": [2, 4],
      "datatype": "FP32",
      "contents": {
        "fp32_contents": [6.8, 2.8, 4.8, 1.4, 6.0, 3.4, 4.5, 1.6]
      }
    }
  ]
}

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-grpc",
  "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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