Wallaroo SDK Upload Tutorials: XGBoost

The following tutorials cover how to upload sample XGBoost models.

During the model upload process, the Wallaroo instance will attempt to convert the model to a Native Wallaroo Runtime. If unsuccessful based , it will create a Wallaroo Containerized Runtime for the model. See the model deployment section for details on how to configure pipeline resources based on the model’s runtime.

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)