Snowpark-optimized warehouses¶

When to use a Snowpark-optimized warehouse¶

Snowpark workloads can be run on both Standard and Snowpark-optimized warehouses. Snowpark-optimized warehouses are recommended for workloads that have large memory requirements such as ML training use cases using a stored procedure on a single virtual warehouse node. Initial creation and resumption of a Snowpark-optimized virtual warehouse may take longer than standard warehouses. Additionally, Snowpark workloads, utilizing UDF or UDTF, may also benefit from Snowpark-optimized warehouses.

Creating a Snowpark-optimized warehouse¶

Use the warehouse_type property in the CREATE WAREHOUSE command to create a new Snowpark-optimized warehouse.

Create a new Snowpark-optimized warehouse snowpark_opt_wh:

CREATE OR REPLACE WAREHOUSE snowpark_opt_wh WITH
  WAREHOUSE_SIZE = 'MEDIUM'
  WAREHOUSE_TYPE = 'SNOWPARK-OPTIMIZED';

Modifying Snowpark-optimized warehouse properties¶

The ALTER WAREHOUSE command can be used to modify warehouse properties, including the warehouse type.

ALTER WAREHOUSE snowpark_opt_wh SUSPEND;

Using Snowpark Python Stored Procedures to run ML training workloads¶

For information on Machine Learning Models and Snowpark Python, see Training Machine Learning Models with Snowpark Python.

Billing for Snowpark-optimized warehouses¶

The following table shows the Snowflake credits charged per hour for a Snowpark-optimized virtual warehouse by warehouse size. Snowpark-optimized warehouses are not supported on X-SMALL or SMALL warehouse sizes.

For more details on virtual warehouse credit usage, see Virtual warehouse credit usage.

Region availability¶

Snowpark-optimized warehouses are available in all regions across AWS, Azure, and Google Cloud.