Contribute a schema

Consider the following schema that was created in the “Register a custom schema” section:

extended_schema = {
    "name": "sklearn__RandomForestClassifier__ext",
    "extends": "sklearn__RandomForestClassifier",

    "parameters": [
        {"name": "runtime_environment", "value_env": "RUNTIME_ENV"},

To contribute “sklearn__RandomForestClassifier__ext” to the rubicon_ml.schema registry, first write the dictionary out to a YAML file.

import yaml

schema_filename = "sklearn__RandomForestClassifier__ext.yaml"

with open(schema_filename, "w") as file:

Once “sklearn__RandomForestClassifier__ext.yaml” is created, follow the “Developer instructions” to fork the rubicon-ml GitHub repository and prepare to make a contribution.

From the root of the forked repository, copy the new schema into the library’s schema directory:

cp [PATH_TO]/sklearn__RandomForestClassifier__ext.yaml rubicon_ml/schema/schema/

Then update rubicon_ml/schema/, adding the new schema to the RUBICON_SCHEMA_REGISTRY:

    # other schema entries...
    "sklearn__RandomForestClassifier__ext": lambda: _load_schema(
        os.path.join("schema", "sklearn__RandomForestClassifier__ext.yaml")

Finally refer back to the “Contribute” section of the “Developer instructions” to push your changes to GitHub and open a pull request. Once the pull request is merged, “sklearn__RandomForestClassifier__ext” will be available in the next release of rubicon_ml.

Schema naming conventions

When naming a schema that extends a schema already made available by rubicon_ml.schema, simply append a double-underscore and a unique identifier. The “sklearn__RandomForestClassifier__ext” above is named following this convention.

When naming a schema that represents an object that is not yet present in schema, leverage the registry.get_schema_name function to generate a name. For example, if you are making a schema for an object my_obj of class Model from a module my_model, registry.get_schema_name(my_obj) will return the name “my_model__Model”.