This tutorial can be downloaded as part of the Wallaroo Tutorials repository.

Wallaroo Model Upload via the Wallaroo SDK: XGBoost RF Classification

The following tutorial demonstrates how to upload a XGBoost RF Classification model to a Wallaroo instance.

The following XGBoost model types are supported by Wallaroo. XGBoost models not supported by Wallaroo are supported via the Arbitrary Python models, also known as Bring Your Own Predict (BYOP).

• XGBRanker XGBoost models are currently supported via converting them to BYOP models.

Tutorial Goals

Demonstrate the following:

• Upload a XGBoost RF Classification model to a Wallaroo instance.
• Create a pipeline and add the model as a pipeline step.
• Perform a sample inference.

Prerequisites

• A Wallaroo version 2023.2.1 or above instance.

References

• Wallaroo MLOps API Essentials Guide: Model Upload and Registrations
• Wallaroo API Connection Guide
• DNS Integration Guide

Tutorial Steps

Import Libraries

The first step is to import the libraries we’ll be using. These are included by default in the Wallaroo instance’s JupyterHub service. See ./requirements.txt for a list of additional libraries used with this tutorial.

from sklearn.datasets import load_iris
from xgboost import XGBClassifier

import wallaroo
from wallaroo.pipeline import Pipeline
from wallaroo.deployment_config import DeploymentConfigBuilder
from wallaroo.object import EntityNotFoundError
from wallaroo.framework import Framework

import pyarrow as pa
import numpy as np
import pandas as pd

Open a Connection to Wallaroo

The next step is connect to Wallaroo through the Wallaroo client. The Python library is included in the Wallaroo install and available through the Jupyter Hub interface provided with your Wallaroo environment.

This is accomplished using the wallaroo.Client() command, which provides a URL to grant the SDK permission to your specific Wallaroo environment. When displayed, enter the URL into a browser and confirm permissions. Store the connection into a variable that can be referenced later.

If logging into the Wallaroo instance through the internal JupyterHub service, use wl = wallaroo.Client(). For more details on logging in through Wallaroo, see the Wallaroo SDK Essentials Guide: Client Connection.

wl = wallaroo.Client()

Set Variables

We’ll set the name of our workspace, pipeline, models and files. Workspace names must be unique across the Wallaroo workspace. For this, we’ll add in a randomly generated 4 characters to the workspace name to prevent collisions with other users’ workspaces. If running this tutorial, we recommend hard coding the workspace name so it will function in the same workspace each time it’s run.

workspace_name = f'xgboost-rf-classification'
pipeline_name = f'xgboost-rf-classification'

model_name = 'xgboost-rf-classification'
model_file_name = './models/xgb_rf_classifier.pkl'

Create Workspace and Pipeline

We will now create the Wallaroo workspace to store our model and set it as the current workspace. Future commands will default to this workspace for pipeline creation, model uploads, etc. We’ll create our Wallaroo pipeline to deploy our model.

workspace = wl.get_workspace(name=workspace_name, create_if_not_exist=True)
wl.set_current_workspace(workspace)

pipeline = wl.build_pipeline(pipeline_name)

Configure Data Schemas

XGBoost models are uploaded to Wallaroo through the Wallaroo Client upload_model method.

Upload XGBoost Model Parameters

The following parameters are available for XGBoost models.

Once the upload process starts, the model is containerized by the Wallaroo instance. This process may take up to 10 minutes.

XGBoost Schema Inputs

XGBoost schema follows a different format than other models. To prevent inputs from being out of order, the inputs should be submitted in a single row in the order the model is trained to accept, with all of the data types being the same. If a model is originally trained to accept inputs of different data types, it will need to be retrained to only accept one data type for each column - typically pa.float64() is a good choice.

For example, the following DataFrame has 4 columns, each column a float.

For submission to an XGBoost model, the data input schema will be a single array with 4 float values.

input_schema = pa.schema([
    pa.field('inputs', pa.list_(pa.float64(), list_size=4))
])

When submitting as an inference, the DataFrame is converted to rows with the column data expressed as a single array. The data must be in the same order as the model expects, which is why the data is submitted as a single array rather than JSON labeled columns: this insures that the data is submitted in the exact order as the model is trained to accept.

Original DataFrame:

Converted DataFrame:

XGBoost Schema Outputs

Outputs for XGBoost are labeled based on the trained model outputs. For this example, the output is simply a single output listed as output. In the Wallaroo inference result, it is grouped with the metadata out as out.output.

output_schema = pa.schema([
    pa.field('output', pa.int32())
])

pipeline.infer(dataframe)

input_schema = pa.schema([
    pa.field('inputs', pa.list_(pa.float32(), list_size=4))
])

output_schema = pa.schema([
    pa.field('predictions', pa.int64()),
    pa.field('probabilities', pa.list_(pa.float32(), list_size=3))
])

Upload Model

The model will be uploaded with the framework set as Framework.XGBOOST.

model = wl.upload_model(model_name, 
                        model_file_name, 
                        framework=Framework.XGBOOST, 
                        input_schema=input_schema, 
                        output_schema=output_schema)
model

Waiting for model loading - this will take up to 10.0min.
Model is pending loading to a native runtime.
Model is attempting loading to a native runtime...incompatible

Model is pending loading to a container runtime..
Model is attempting loading to a container runtime...successful

Ready

Deploy Pipeline

The model is uploaded and ready for use. We’ll add it as a step in our pipeline, then deploy the pipeline. For this example we’re allocated 0.25 cpu and 4 Gi RAM to the pipeline through the pipeline’s deployment configuration.

deployment_config = DeploymentConfigBuilder() \
    .cpus(0.25).memory('1Gi') \
    .build()

# clear the pipeline if it was used before
pipeline.undeploy()
pipeline.clear()

pipeline.add_model_step(model)

pipeline.deploy(deployment_config=deployment_config)

Run Inference

A sample inference will be run. First the pandas DataFrame used for the inference is created, then the inference run through the pipeline’s infer method.

data = load_iris(as_frame=True)

X = data['data'].values[:100]
y = data['target'][:100]

dataframe = pd.DataFrame({"inputs": X[:100].tolist()})
dataframe

100 rows × 1 columns

pipeline.infer(dataframe)

100 rows × 5 columns

Undeploy Pipelines

With the tutorial complete, the pipeline is undeployed to return the resources back to the cluster.

pipeline.undeploy()