model_schema.md

description
Documentation on how to attach a model schema to a model.

How To Attach A Model Schema

Introduction

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.

Code

Step 1: Connect to Hopsworks

=== "Python" ```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" ```python # Import a Schema and ModelSchema definition from hsml.utils.model_schema import ModelSchema from hsml.utils.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" python model = mr.tensorflow.create_model(name="mnist", model_schema=model_schema) model.save("./model")