How To Attach A Model Schema#

Introduction#

Deprecated

ModelSchema is deprecated and will be removed in a future release. A model registered with create_model(feature_view=...) gets its input and output schema from the feature view's training dataset, and a deployment describes its requests with the deployment schema. The default predictor still reads a legacy model schema to select the model's input columns; a DefaultPredict subclass that overrides model_predict replaces that. For a model without a feature view, name its input columns with passed_features= on deploy().

In this guide you will learn how to attach a model schema to your model. A model schema, describes the type and shape of inputs and outputs (predictions) for your model. Attaching a model schema to your model will give other users a better understanding of what data it expects.

Model schema and deployment schema

A model registered with feature_view= gets its model schema inferred from the feature view's training dataset schema when it is saved. The default predictor checks at pod start that every model input column is served by the feature view, and the deployment schema describes what clients send. See the Deployment Schema Guide.

Code#

Step 1: Connect to Hopsworks#

Python

import hopsworks


project = hopsworks.login()

# get Hopsworks Model Registry handle
mr = project.get_model_registry()

Step 2: Create ModelSchema#

Create a ModelSchema for your inputs and outputs by passing in an example that your model is trained on and a valid prediction. Currently, we support pandas.DataFrame, pandas.Series, numpy.ndarray, list.

Python

# Import a Schema and ModelSchema definition
from hsml.model_schema import ModelSchema
from hsml.schema import Schema


# Model inputs for MNIST dataset
inputs = [
    {
        "type": "uint8",
        "shape": [28, 28, 1],
        "description": "grayscale representation of 28x28 MNIST images",
    }
]

# Build the input schema
input_schema = Schema(inputs)

# Model outputs
outputs = [{"type": "float32", "shape": [10]}]

# Build the output schema
output_schema = Schema(outputs)

# Create ModelSchema object
model_schema = ModelSchema(input_schema=input_schema, output_schema=output_schema)

Step 3: Set model_schema parameter#

Set the model_schema parameter in the create_model function and call save() to attaching it to the model and register it in the registry.

Python

model = mr.tensorflow.create_model(name="mnist", model_schema=model_schema)
model.save("./model")