# Serving LightGBM models¶

Out of the box, mlserver supports the deployment and serving of lightgbm models. By default, it will assume that these models have been serialised using the bst.save_model() method.

In this example, we will cover how we can train and serialise a simple model, to then serve it using mlserver.

## Training¶

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

import lightgbm as lgb
from sklearn.model_selection import train_test_split
import os

model_dir = "."
BST_FILE = "iris-lightgbm.bst"

y = iris['target']
X = iris['data']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1)
dtrain = lgb.Dataset(X_train, label=y_train)

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)


Our model will be persisted as a file named iris-lightgbm.bst.

## Serving¶

Now that we have trained and saved our model, the next step will be to serve it using mlserver. For that, we will need to create 2 configuration files:

• settings.json: holds the configuration of our server (e.g. ports, log level, etc.).

• model-settings.json: holds the configuration of our model (e.g. input type, runtime to use, etc.).

### settings.json¶

%%writefile settings.json
{
"debug": "true"
}


### model-settings.json¶

%%writefile model-settings.json
{
"name": "iris-lgb",
"implementation": "mlserver_lightgbm.LightGBMModel",
"parameters": {
"uri": "./iris-lightgbm.bst",
"version": "v0.1.0"
}
}


### Start serving our model¶

Now that we have our config in-place, we can start the server by running mlserver start .. This needs to either be ran from the same directory where our config files are or pointing to the folder where they are.

mlserver start .


Since this command will start the server and block the terminal, waiting for requests, this will need to be ran in the background on a separate terminal.

### Send test inference request¶

We now have our model being served by mlserver. To make sure that everything is working as expected, let’s send a request from our test set.

For that, we can use the Python types that mlserver provides out of box, or we can build our request manually.

import requests

x_0 = X_test[0:1]
inference_request = {
"inputs": [
{
"name": "predict-prob",
"shape": x_0.shape,
"datatype": "FP32",
"data": x_0.tolist()
}
]
}

endpoint = "http://localhost:8788/v2/models/iris-lgb/versions/v0.1.0/infer"
response = requests.post(endpoint, json=inference_request)

response.json()


As we can see above, the model predicted the probability for each class, and the probability of class 1 is the biggest, close to 0.99, which matches what’s on the test set.

y_test[0]