create_execution
Initialize an ML workflow run by creating an execution record, linking input datasets and assets for provenance tracking. Supports dry-run mode for testing without catalog writes.
Instructions
Create a new execution to track an ML workflow run with provenance.
This is the first step in the execution lifecycle. Specify input datasets and assets to establish provenance - these will be recorded as inputs to this workflow run.
LIFECYCLE (follow in order):
create_execution() - You are here
start_execution() - Begin timing
[Run your ML workflow]
stop_execution() - End timing
Args: workflow_name: Descriptive name (e.g., "ResNet50 Training Run 3"). workflow_type: Type from Workflow_Type vocabulary (e.g., "Training", "Inference"). description: What this execution does and why. dataset_rids: Input dataset RIDs for provenance tracking. asset_rids: Input asset RIDs for provenance tracking. dry_run: If True, download input datasets/assets but skip creating execution records in the catalog and skip uploading results. Useful for testing data loading, configuration, and model initialization without writing to the catalog.
Returns: JSON with execution_rid, workflow_rid, dataset_count, asset_count, dry_run.
Example: create_execution("CIFAR Training", "Training", "Train ResNet on CIFAR-10", ["1-ABC"]) create_execution("Test Run", "Training", "Debug data loading", dry_run=True)
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| dry_run | No | ||
| asset_rids | No | ||
| description | No | ||
| dataset_rids | No | ||
| workflow_name | Yes | ||
| workflow_type | Yes |
Output Schema
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |