Snowpark-optimized warehouses¶

Snowpark-optimized warehouses let you configure the available memory resources and CPU architecture on a single-node instance for your workloads.

When to use a Snowpark-optimized warehouse¶

While Snowpark workloads can be run on both standard and Snowpark-optimized warehouses, Snowpark-optimized warehouses are recommended for running code, and recommended workloads that have large memory requirements or dependencies on a specific CPU architecture. Example workloads include ML training use cases using a stored procedure on a single virtual warehouse node. Snowpark workloads, utilizing UDF or UDTF, might also benefit from Snowpark-optimized warehouses.

Note

Initial creation and resumption of a Snowpark-optimized virtual warehouse might take longer than standard warehouses.

Configuration options for Snowpark-optimized warehouses¶

The default configuration for a Snowpark-optimized warehouse provides 16x memory per node compared to a standard warehouse. You can optionally configure additional memory per node and specify CPU architecture using the resource_constraint property. The following options are available:

Memory (up to)	CPU architecture	Minimum warehouse size required
16GB	Default or x86	XSMALL
256GB	Default or x86	M
1TB [1]	Default or x86	L

Creating a Snowpark-optimized warehouse¶

To create a new Snowpark-optimized warehouse, you can set the warehouse type property in the following interfaces.

Set the WAREHOUSE_TYPE property to 'SNOWPARK-OPTIMIZED' when running the CREATE WAREHOUSE command. For example:

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

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Create a large Snowpark-optimized warehouse so_warehouse with 256 GB of memory by specifying the resource constraint MEMORY_16X_X86:

CREATE WAREHOUSE so_warehouse WITH
  WAREHOUSE_SIZE = 'LARGE'
  WAREHOUSE_TYPE = 'SNOWPARK-OPTIMIZED'
  RESOURCE_CONSTRAINT = 'MEMORY_16X_X86';

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Note

The default resource constraint is MEMORY_16X.

Set the warehouse_type property to 'SNOWPARK-OPTIMIZED' when constructing a Warehouse object.

Then, pass this Warehouse object to the WarehouseCollection.create method to create the warehouse in Snowflake. For example:

from snowflake.core import CreateMode
from snowflake.core.warehouse import Warehouse

my_wh = Warehouse(
  name="snowpark_opt_wh",
  warehouse_size="MEDIUM",
  warehouse_type="SNOWPARK-OPTIMIZED"
)
root.warehouses.create(my_wh, mode=CreateMode.or_replace)

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Note

Resource constraints are currently not supported in the Snowflake Python APIs.

Modifying Snowpark-optimized warehouse properties¶

To modify warehouse properties including the warehouse type, you can use the following interfaces.

Note

Changing the warehouse type is only supported for a warehouse in the SUSPENDED state. To suspend a warehouse before changing the warehouse_type property, execute the following operation:

ALTER WAREHOUSE snowpark_opt_wh SUSPEND;

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root.warehouses["snowpark_opt_wh"].suspend()

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Use the ALTER WAREHOUSE command to modify the memory resources and CPU architecture for Snowpark-optimized warehouse so_warehouse:

ALTER WAREHOUSE so_warehouse SET
  RESOURCE_CONSTRAINT = 'MEMORY_1X_x86';

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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¶

For information on Snowpark-optimized warehouse credit consumption see Table 1(a): Snowflake Credit Table for Virtual Warehouse Services in the Snowflake Service Consumption Table.

Region availability¶

Snowpark-optimized warehouses are available in all regions across AWS, Azure, and Google Cloud [1].