Inference in Snowflake Warehouses

The Snowflake Model Registry runs model methods in a warehouse by default. Running models in a warehouse is appropriate for small-to-medium size CPU-only models whose dependencies can be satisfied by packages available in the Snowflake conda channel.

Note

You can also run models on a Snowpark Container Services (SPCS) compute pool. This approach is more appropriate for large models that can benefit from distributed inference. See Model Serving in Snowpark Container Services for more information.

To get started, log the model in the model registry. Only models that have been logged in the registry are available for inference. Logging a model, or loading an existing model from the registry by means of registry.get_model(...).version(...), returns a ModelVersion object on which you can call the run method.

Inference from Python

To call a method of a model version, use mv.run, where mv is a ModelVersion object. Specify the name of the function to be called and pass a Snowpark or pandas DataFrame that contains the inference data, along with any required parameters. The method is executed in a Snowflake warehouse.

The return value of the method is a Snowpark or pandas DataFrame, matching the type of DataFrame passed in. Snowpark DataFrames are evaluated lazily, so the method is run only when the DataFrame’s collect, show, or to_pandas method is called.

Note

Invoking a method runs it in the warehouse specified in the session you’re using to connect to the registry. See Specifying a Warehouse.

The following example illustrates running the predict method of a model. This model’s predict method does not require any parameters besides the inference data (test_features here). If it did, they would be passed as additional arguments after the inference data.

remote_prediction = mv.run(test_features, function_name="predict")
remote_prediction.show()   # assuming test_features is Snowpark DataFrame
Copy

To see what methods can be called on a given model, call mv.show_functions. The return value of this method is a list of ModelFunctionInfo objects. Each of these objects includes the following attributes:

  • name: The name of the function that can be called from Python or SQL.

  • target_method: The name of the Python method in the original logged model.

Inference from SQL

Use the model_name!method_name(...) syntax to can call or invoke methods of a model. The methods available on a model are determined by the underlying Python model class. For example, many types of models use a method named predict for inference.

To call a method of the default model, use the following syntax. Include any method arguments within the parentheses and specify the table containing the inference data in the FROM clause.

SELECT <model_name>!<method_name>(...) FROM <table_name>;
Copy

To invoke a method from a specific version of a model, create an alias to the specific version of the model and call the method through the alias.

Use the following syntax to call a method from a specific version of a model.

WITH <model_version_alias> AS MODEL <model_name> VERSION <version_or_alias_name>
    SELECT <model_version_alias>!<method_name>(...) FROM <table_name>;
Copy

The following example uses the LAST alias to call the latest version of a model.

WITH latest AS MODEL my_model VERSION LAST
    SELECT latest!predict(...) FROM my_table;
Copy

Tip

See the full model SQL API reference here.

Language: English