Deploy a logged XGBoost model



This feature is in the Beta stage - the input parameters and Python APIs can change.
We also have plans to add more model formats and frameworks, model stores and easy inference and testing tools.

We would love to hear from you if you have any feedback or run into any issues.


What you'll learn

  • Logging a xgboost model via mlfoundry
  • Deploying our logged model via servicefoundry and ModelDeployment

This is a guide to deploy a xgboost model via Model Deployment without writing any service code.

After you complete the guide, you will have a successfully deployed model. Your deployed service will look like this:

We have divided this guide into two sections:

  • Train and log your model.
  • Deploy your model.



We have added sample training code for completeness' sake. If you already have a trained model skip to the deployment part

Train and log your Model

To use Model Deployment and deploy your model without writing any service code, first we need to log our model to Truefoundry's Model Registry. For this, we are going to be using mlfoundry.

For this guide we will be training our model in our local environment, but we can also use Truefoundry Jobs to train this model in the cloud

Project structure

To complete this section of the guide, you are going to create the following files:

  • requirements.txt: contains the list of dependencies we would need.
  • contains our training code.

Your final file structure is going to look like this:

β”œβ”€β”€ requirements.txt

Step 1: Install all dependencies.

First, we will create the requirements.txt file.



# For logging the trained model

# For deploying it as a Service

Now, we can install these dependencies in a Python environment using pip install -r requirements.txt.
This is necessary so that we can run our training script locally.

Step 2: Training and log our model.

Next, let's create our file, which will first train our model, and then log the model to the Truefoundry's Model Registry.

import mlfoundry
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
import xgboost as xgb

X, y = load_iris(as_frame=True, return_X_y=True)
X = X.rename(columns={
    "sepal length (cm)": "sepal_length",
    "sepal width (cm)": "sepal_width",
    "petal length (cm)": "petal_length",
    "petal width (cm)": "petal_width",

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)
clf = xgb.XGBClassifier(), y_train)

client = mlfoundry.get_client()
run = client.create_run(project_name="iris-clf", run_name="iris-xgb")

# Optionally, we can log hyperparameters using run.log_params(...)
# Optionally, we can log metrics using run.log_metrics(...)

model_version = run.log_model(name="iris-xgb", model=clf, framework="xgboost", step=0)
print("Model Logged as:", model_version.fqn)

Now that we have implemented the training code, you can run this script via running the following command in your terminal.


Step 3: Get our models FQN.

Once you run the above command, you will get the following output:

[mlfoundry] 2022-12-30T14:08:50+0530 INFO Logged in to '' as 'user-truefoundry' ([email protected])
Link to the dashboard for the run:
[mlfoundry] 2022-12-30T14:08:55+0530 INFO Run 'truefoundry/user-truefoundry/iris-clf/iris-xgb-14' has started.
[mlfoundry] 2022-12-30T14:08:55+0530 INFO Logging model and additional files, this might take a while ...
[mlfoundry] 2022-12-30T14:08:55+0530 INFO Serializing model files to model version contents
[mlfoundry] 2022-12-30T14:08:58+0530 INFO Packaging and uploading files to remote ...
[mlfoundry] 2022-12-30T14:09:10+0530 INFO Logged model successfully with fqn 'model:truefoundry/user-truefoundry/iris-clf/iris-xgb:2'
Model Logged as: model:truefoundry/user-truefoundry/iris-clf/iris-xgb:2
[mlfoundry] 2022-12-30T14:09:17+0530 INFO Setting run status of 'truefoundry/user-truefoundry/iris-clf/iris-xgb-14' to 'FINISHED'
Finished run: 'truefoundry/user-truefoundry/iris-clf/iris-xgb-14', Dashboard:

In this output, you will find the model FQN at line 8:

Model Logged as: model:truefoundry/user-truefoundry/iris-clf/iris-xgb:2

Copy the model:truefoundry/user-truefoundry/iris-clf/iris-xgb:2 part.



If you have already logged your model, You can do the following to get your models FQN:

  • Go to the Experiments tab.
  • Search for your Project's name, and click on it.
  • Click on the Run which contains the model you want to deploy.
  • Click on the Model tab.
  • Copy the Model FQN.

Model Deployment

Project structure

To complete this guide, you are going to create the following files:

  • contains our deployment code / deployment configuration file

Your final file structure is going to look like this:

└── / deploy.yaml

Step 1: Deploy the model

You can deploy the model on TrueFoundry programmatically either using our Python SDK, or via a YAML file.

Via Python SDK

File Structure




In the code below, ensure to replace "<YOUR_WORKSPACE_FQN>" with your Workspace FQN

Also replace "<YOUR_MODEL_VERSION_FQN>" with the model FQN you copied before.

import logging
from servicefoundry import ModelDeployment, TruefoundryModelRegistry, Resources

logging.basicConfig(level=logging.INFO, format=logging.BASIC_FORMAT)

# Replace these with your values
MODEL_VERSION_FQN = "<YOUR_MODEL_VERSION_FQN>" # E.g. model:truefoundry/user-truefoundry/iris-clf/iris-xgb:2
WORKSPACE = "<YOUR_WORKSPACE_FQN>" # E.g. tfy-ctl-euwe1:model-deployment-test

model_deployment = ModelDeployment(
    resources=Resources(cpu_request=0.2, cpu_limit=0.5, memory_request=500, memory_limit=1000)
deployment = model_deployment.deploy(workspace_fqn=WORKSPACE)

To deploy the job using Python API use:


Via YAML file

File Structure

└── deploy.yaml




In the file below, ensure to replace "<YOUR_MODEL_VERSION_FQN>" with the model FQN you copied before.

name: iris-xgb
type: model-deployment
  type: tfy-model-registry
  model_version_fqn: <YOUR_MODEL_VERSION_FQN>
  cpu_request: 0.2
  cpu_limit: 0.5
  memory_request: 500
  memory_limit: 1000

To deploy, run with your Workspace FQN:

sfy deploy --workspace-fqn <YOUR_WORKSPACE_FQN> --file deploy.yaml

Interact with the Service

After you run the command given above, you will get a link at the end of the output. The link will take you to your application's dashboard.

Once the build is complete you should get the endpoint for your service :

Endpoint URL is available on the Model Deployment Details Page

Endpoint URL is available on the Model Deployment Details Page

Copy the following endpoint URL, we will need this to make the requests.

We will now send the following examples for prediction.


Here is an example Python code snippet to send a request with the above data.



Behind the scenes, our model is being deployed using MLServer and Kserve's V2 Dataplane protocol

The general format of the Inference URL thus looks like




In the file below, ensure to replace "<YOUR_ENDPOINT_URL>" with the Endpoint url you copied before.

import json
from urllib.parse import urljoin

import requests

# Replace this with the value of your endpoint 
MODEL_NAME = "iris-xgb"

response =
    urljoin(ENDPOINT_URL, f'v2/models/{MODEL_NAME}/infer'),
        "inputs": [
            {"data": [7.0, 5.0], "datatype": "FP32", "name": "sepal_length", "shape": [2]},
            {"data": [3.2, 3.6], "datatype": "FP32", "name": "sepal_width", "shape": [2]},
            {"data": [4.7, 1.4], "datatype": "FP32", "name": "petal_length", "shape": [2]},
            {"data": [1.4, 0.2], "datatype": "FP32", "name": "petal_width", "shape": [2]}
        "parameters": {
            "content_type": "pd"

result = response.json()
print(json.dumps(result, indent=4))
print("Predicted Classes:", result["outputs"][0]["data"])
# Replace this with the values

curl -X POST \
     -H 'Content-Type: application/json' \
     -d '{"inputs":[{"data":[7,5],"datatype":"FP32","name":"sepal_length","shape":[2]},{"data":[3.2,3.6],"datatype":"FP32","name":"sepal_width","shape":[2]},{"data":[4.7,1.4],"datatype":"FP32","name":"petal_length","shape":[2]},{"data":[1.4,0.2],"datatype":"FP32","name":"petal_width","shape":[2]}],"parameters":{"content_type":"pd"}}' \

This should return something like

    "model_name": "iris-xgb",
    "model_version": "2",
    "id": "f4494159-5c65-4652-b29d-855b68dc001d",
    "parameters": {
        "content_type": null,
        "headers": null
    "outputs": [
            "name": "output-1",
            "shape": [
            "datatype": "INT64",
            "parameters": null,
            "data": [
Predicted Classes: [1, 0]

Note on content_type

Notice in the request we have

    "inputs": ...,
    "parameters": {
        "content_type": "pd"

We added this because when training the model we used pandas DataFrame. Our model expects a DataFrame with correct feature names when calling predict.

Alternatively, if we had not used a DataFrame and simply a numpy array or list during training, our request body would look something like

  "inputs": [
      "name": "input-0",
      "shape": [2, 4],
      "datatype": "FP32",
      "data": [
          [7.0, 3.2, 4.7, 1.4],
          [5.0, 3.6, 1.4, 0.2],

OpenAPI Spec

To make the process of making the requests easier you can go to the OpenAPI tab

Now, we are going to click on the Infer tab.

Scrolling down we will see the following card on the right.

The card on the right gives you the format to give a request using a specific language or certain way.

You can click on the dropdown and you will get a list of ways request can be made: