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README.md
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README.md

Creating your own model and testing the LightGBM server.

To test the LightGBM Server, first we need to generate a simple LightGBM model using Python.

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)

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)

Then, we can install and run the LightGBM Server using the generated model and test for prediction. Models can be on local filesystem, S3 compatible object storage, Azure Blob Storage, or Google Cloud Storage.

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

We can also do some simple predictions

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)

Predict on a InferenceService using LightGBM Server

Setup

  1. Your ~/.kube/config should point to a cluster with KFServing installed.
  2. Your cluster's Istio Ingress gateway must be network accessible.

Create the InferenceService

Apply the CRD

kubectl apply -f lightgbm.yaml

Expected Output

$ inferenceservice.serving.kubeflow.org/lightgbm-iris created

Run a prediction

The first step is to determine the ingress IP and ports and set INGRESS_HOST and INGRESS_PORT

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]]}

Run LightGBM InferenceService with your own image

Since the KFServing LightGBM image is built from a specific version of lightgbm pip package, sometimes it might not be compatible with the pickled model you saved from your training environment, however you can build your own lgbserver image following this instruction.

To use your lgbserver image:

        "lightgbm": {
            "image": "<your-dockerhub-id>/kfserving/lgbserver",
        },
  • Specify the runtimeVersion on InferenceService spec
apiVersion: "serving.kubeflow.org/v1beta1"
kind: "InferenceService"
metadata:
  name: "lightgbm-iris"
spec:
  predictor:
    lightgbm:
      storageUri: "gs://kfserving-examples/models/lightgbm/iris"
      runtimeVersion: X.X.X