DSLab Runner
The DS Lab Runner component is used to manage and execute data science experiments that were created in the Data Science Lab (DSLab) module and imported into a pipeline. It supports running both trained models and exported scripts as part of a data pipeline workflow.
The DS Lab Runner processes input data from one event and sends the transformed output to another event. It can operate in batch or real-time mode, depending on the selected configuration.
Requirements
Before using the DS Lab Runner, ensure that:
You have created and saved a model or script in the DS Lab module.
Input and output events are available in the pipeline canvas.
Compute resources (CPU/GPU) are configured for model execution.
Using the DS Lab Runner in a Pipeline Workflow
Drag the DS Lab Runner component to the Pipeline Workflow canvas.
Create two events (input and output) and place them on the canvas.
Input data can originate from ingestion components, readers, shared events, or scripts from the DS Lab module.
Connect the input event → DS Lab Runner → output event.
Select the DS Lab Runner component to open its configuration tabs.
Configure the fields in the Basic Information and Meta Information tabs.
Save the component.
Once configured, the DS Lab Runner reads input data, runs the model or script, and writes the processed data (including predicted columns, if applicable) to the output event.
Configuration
All configurations are grouped into the following sections:
Basic Information
Meta Information
Resource Configuration
Basic Information Tab
The Basic Information tab defines general properties and execution settings.
Invocation Type
Select execution mode: Batch or Real-Time.
Yes
Grace Period (sec)
Appears only for Batch mode. Defines the time before the component shuts down gracefully.
Conditional
Deployment Type
Pre-selected field showing the deployment type of the component.
Yes
Batch Size
Maximum number of records processed per cycle. Minimum: 10.
Yes
Failover Event
Select an event to handle failover scenarios.
Optional
Container Image Version
Displays the Docker image version used. Pre-selected.
Yes
Description
Optional description of the component.
No
Meta Information Tab
The Meta Information tab determines the execution type:
Model Runner (run a registered model from DSLab).
Script Runner (run an exported script from DSLab).
DS Lab Runner as Model Runner
Use this mode to execute a model created in the DS Lab module.
Project Name
Name of the project containing the model.
Yes
Model Name
Name of the saved model in the project.
Yes
DS Lab Runner as Script Runner
Use this mode to execute a Python script exported from the DSLab module.
Function Input Type
Select the input type: DataFrame or List of Dictionary.
Yes
Project Name
Name of the project containing the script.
Yes
Script Name
Name of the exported script from DSLab notebooks. Must be written inside a function.
Yes
External Library
Comma-separated list of external libraries used in the script.
Optional
Start Function
The function name defined in the script.
Yes
Script
Displays the exported script content.
Yes
Input Data
Key-value pairs for function parameters.
Optional
For details on exporting a script, see: Exporting a Script from DSLab.
Saving the DS Lab Runner
After completing the configuration, click Save Component (Storage icon).
A success message confirms that the component has been saved.
Example Workflow
Ingest customer transaction data through a Reader component.
Pass the data to a DS Lab Runner (Model Runner) that applies a fraud detection model.
Write the enriched dataset (with predicted fraud risk scores) to an output Event.