Wallaroo SDK Upload Tutorials: SKLearn

The following tutorials cover how to upload sample SKLearn models.

Sci-kit Learn aka SKLearn.

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.

SKLearn Schema Inputs

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

For submission to an SKLearn 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:

SKLearn Schema Outputs

Outputs for SKLearn that are meant to be predictions or probabilities when output by the model are labeled in the output schema for the model when uploaded to Wallaroo. For example, a model that outputs either 1 or 0 as its output would have the output schema as follows:

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

When used in Wallaroo, the inference result is contained in the out metadata as out.predictions.

pipeline.infer(dataframe)