2. Deploy with Docker

In this example, we deploy the model with Docker. For this example you need to install Docker, see Docker and XGBoost, see XGBoost.

Firstly, import mljet.

import mljet

Import xgboost.XGBClassifier as classifier and sklearn.datasets to load the iris dataset.

from sklearn.datasets import load_iris
from xgboost import XGBClassifier

Let’s load dataset and create and train a simple model.

X, y = load_iris(return_X_y=True)
clf = XGBClassifier()
clf.fit(X, y)

Now, we can deploy the model to docker with mljet.contrib.entrypoint.cook().

mljet.contrib.cook(
    model=clf,
    strategy="docker",
    tag="mljet-xgboost",
    port=5000,
    need_run=True,
    silent=True,
    verbose=False,
)

Let’s see on passed parameters.

• model - model to deploy - clf (XGBoost model)

• strategy - strategy to use - docker

• tag - tag for the docker image - mljet-xgboost

• port - port for the docker container - 8000

• backend - backend to use - sanic, see Sanic

• need_run - run service after build or not - True (only create container)

• silent - silent mode - True, non-blocking mode

• verbose - verbose mode - True, print DEBUG logs

After calling the mljet.contrib.entrypoint.cook() function You can see build folder in the current directory. And you can see the docker image and container with name mljet-xgboost.

Now we can send a request to the model. For this example, we use requests, see Requests. You can use any other tool, for example Postman. Firstly, import requests.

import time

import requests

Let’s sleep for 5 seconds and check the response.

time.sleep(5)

response = requests.post(
    "http://localhost:5000/predict",
    json={"data": X.tolist()},
)

print(response.json())