ml-jobs

You can run local Python scripts on Snowflake compute pools by converting them into Snowflake ML Jobs. This process handles dependency detection, stage preparation, and generates a submission template for remote execution.

Yes, Snowflake ML Jobs support GPU training workloads alongside distributed training and run-on-pool configurations. The conversion process automatically determines your compute requirements to match the workload.

To prepare Python dependencies for remote execution, the ML Jobs conversion process automatically detects required packages and manages prerequisite handling. It also handles stage preparation to ensure the compute pool environment is ready.

The best way to offload heavy ML training to Snowflake is converting local scripts into ML Jobs. This enables scalable remote execution on dedicated compute pools without requiring manual deployment steps.

You need an existing Snowflake compute pool to execute ML Jobs. During conversion, you supply the script path and your preferred compute pool configuration to generate the appropriate job submission template.

Stage preparation is required for Snowflake ML Jobs to ensure your local Python script and its dependencies are available in the Snowflake environment. This step enables seamless remote execution on the designated compute pool.