Serving XGBoost models

Out of the box, mlserver supports the deployment and serving of xgboost 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

The first step will be to train a simple xgboost model. For that, we will use the mushrooms example from the xgboost Getting Started guide.

# Original code and extra details can be found in:
# https://xgboost.readthedocs.io/en/latest/get_started.html#python

import os
import xgboost as xgb
import requests

from urllib.parse import urlparse
from sklearn.datasets import load_svmlight_file


TRAIN_DATASET_URL = 'https://raw.githubusercontent.com/dmlc/xgboost/master/demo/data/agaricus.txt.train'
TEST_DATASET_URL = 'https://raw.githubusercontent.com/dmlc/xgboost/master/demo/data/agaricus.txt.test'


def _download_file(url: str) -> str:
    parsed = urlparse(url)
    file_name = os.path.basename(parsed.path)
    file_path = os.path.join(os.getcwd(), file_name)
    
    res = requests.get(url)
    
    with open(file_path, 'wb') as file:
        file.write(res.content)
    
    return file_path

train_dataset_path = _download_file(TRAIN_DATASET_URL)
test_dataset_path = _download_file(TEST_DATASET_URL)

# NOTE: Workaround to load SVMLight files from the XGBoost example
X_train, y_train = load_svmlight_file(train_dataset_path)
X_test, y_test = load_svmlight_file(test_dataset_path)

# read in data
dtrain = xgb.DMatrix(data=X_train, label=y_train)

# specify parameters via map
param = {'max_depth':2, 'eta':1, 'objective':'binary:logistic' }
num_round = 2
bst = xgb.train(param, dtrain, num_round)

bst

Saving our trained model

To save our trained model, we will serialise it using bst.save_model() and the JSON format. This is the approach by the XGBoost project.

Our model will be persisted as a file named mushroom-xgboost.json.

model_file_name = 'mushroom-xgboost.json'
bst.save_model(model_file_name)

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": "mushroom-xgboost",
    "implementation": "mlserver_xgboost.XGBoostModel",
    "parameters": {
        "uri": "./mushroom-xgboost.json",
        "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",
          "shape": x_0.shape,
          "datatype": "FP32",
          "data": x_0.toarray().tolist()
        }
    ]
}

endpoint = "http://localhost:8080/v2/models/mushroom-xgboost/versions/v0.1.0/infer"
response = requests.post(endpoint, json=inference_request)

response.json()

As we can see above, the model predicted the input as close to 0, which matches what’s on the test set.

y_test[0]