Wallaroo SDK Upload Tutorials: Arbitrary Python

The following tutorials cover how to upload sample arbitrary python models into a Wallaroo instance.

Arbitrary Python models, also known as Bring Your Own Predict (BYOP) allow for custom model deployments with supporting scripts and artifacts. These are used with pre-trained models (PyTorch, Tensorflow, etc) along with whatever supporting artifacts they require. Supporting artifacts can include other Python modules, model files, etc. These are zipped with all scripts, artifacts, and a requirements.txt file that indicates what other Python models need to be imported that are outside of the typical Wallaroo platform.

Contrast this with Wallaroo Python models - aka “Python steps”. These are standalone python scripts that use the python libraries natively supported by the Wallaroo platform. These are used for either simple model deployment (such as ARIMA Statsmodels), or data formatting such as the postprocessing steps. A Wallaroo Python model will be composed of one Python script that matches the Wallaroo requirements.

Arbitrary Python File Requirements

Arbitrary Python (BYOP) models are uploaded to Wallaroo via a ZIP file with the following components:

For example, the if the arbitrary python model will be known as vgg_clustering, the contents may be in the following structure, with vgg_clustering as the storage directory:

vgg_clustering\
    feature_extractor.h5
    kmeans.pkl
    custom_inference.py
    requirements.txt

Note the inclusion of the custom_inference.py file. This file name is not required - any Python script or scripts that extend the classes listed above are sufficient. This Python script could have been named vgg_custom_model.py or any other name as long as it includes the extension of the classes listed above.

The sample arbitrary python model file is created with the command zip -r vgg_clustering.zip vgg_clustering/.

Wallaroo Arbitrary Python uses the Wallaroo SDK mac module, included in the Wallaroo SDK 2023.2.1 and above. See the Wallaroo SDK Install Guides for instructions on installing the Wallaroo SDK.

Arbitrary Python Script Requirements

The entry point of the arbitrary python model is any python script that extends the following classes. These are included with the Wallaroo SDK. The required methods that must be overridden are specified in each section below.

• mac.inference.Inference interface serves model inferences based on submitted input some input. Its purpose is to serve inferences for any supported arbitrary model framework (e.g. scikit, keras etc.).

  classDiagram
      class Inference {
          <<Abstract>>
          +model Optional[Any]
          +expected_model_types()* Set
          +predict(input_data: InferenceData)*  InferenceData
          -raise_error_if_model_is_not_assigned() None
          -raise_error_if_model_is_wrong_type() None
      }

• mac.inference.creation.InferenceBuilder builds a concrete Inference, i.e. instantiates an Inference object, loads the appropriate model and assigns the model to to the Inference object.

  classDiagram
      class InferenceBuilder {
          +create(config InferenceConfig) * Inference
          -inference()* Any
      }

mac.inference.Inference

mac.inference.Inference Objects

mac.inference.Inference Methods

preds = self.model.predict(data)
preds = preds.numpy()
rows, _ = preds.shape
preds = preds.reshape((rows,))

return {"prediction": preds} # a Dictionary of numpy arrays.

The example, the expected_model_types can be defined for the KMeans model.

from sklearn.cluster import KMeans

class SampleClass(mac.inference.Inference):
    @property
    def expected_model_types(self) -> Set[Any]:
        return {KMeans}

mac.inference.creation.InferenceBuilder

InferenceBuilder builds a concrete Inference, i.e. instantiates an Inference object, loads the appropriate model and assigns the model to the Inference.

classDiagram
    class InferenceBuilder {
        +create(config InferenceConfig) * Inference
        -inference()* Any
    }

Each model that is included requires its own InferenceBuilder. InferenceBuilder loads one model, then submits it to the Inference class when created. The Inference class checks this class against its expected_model_types() Set.

mac.inference.creation.InferenceBuilder Methods

Arbitrary Python Runtime

Arbitrary Python always run in the containerized model runtime.