validate_rids
Validate dataset, asset, workflow, and execution RIDs before running experiments to catch missing references and version mismatches early.
Instructions
Validate that RIDs exist in the catalog before running experiments.
Performs batch validation of RIDs to catch configuration errors early with clear error messages. Use this before running experiments to ensure all referenced datasets, assets, and other entities actually exist.
Args: dataset_rids: List of dataset RIDs to validate. asset_rids: List of asset RIDs to validate (model weights, etc.). dataset_versions: Dictionary mapping dataset RID to required version string (e.g., {"1-ABC": "0.4.0"}). Validates version exists. workflow_rids: List of workflow RIDs to validate. execution_rids: List of execution RIDs to validate. warn_missing_descriptions: If True (default), include warnings for datasets missing descriptions.
Returns: JSON with: - is_valid: True if all validations passed - errors: List of error messages - warnings: List of warning messages - validated_rids: Dictionary of validated RID info
Example: validate_rids( dataset_rids=["1-ABC", "2-DEF"], dataset_versions={"1-ABC": "0.4.0"}, asset_rids=["3-GHI"] ) -> { "is_valid": true, "errors": [], "warnings": [], "validated_rids": {...} }
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| asset_rids | No | ||
| dataset_rids | No | ||
| workflow_rids | No | ||
| execution_rids | No | ||
| dataset_versions | No | ||
| warn_missing_descriptions | No |
Output Schema
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |