Implementation:Kubeflow Pipelines Load Component From URL
| Sources | Kubeflow Pipelines, KFP Components |
|---|---|
| Domains | ML_Pipelines, Component_Reuse |
| Last Updated | 2026-02-13 |
Overview
Concrete tool for loading reusable pipeline components from remote YAML URLs provided by the KFP components module.
Description
kfp.components.load_component_from_url() fetches a YAML component specification from a URL and returns a callable component factory function. This is used at module level to define component operators that can be called within pipeline functions. URLs typically point to specific Git commit SHAs for reproducibility.
Usage
Call at module level to load remote components before pipeline definition. The returned factory is called within @dsl.pipeline functions.
Code Reference
Source Location: Repository: kubeflow/pipelines, File: samples/core/XGBoost/xgboost_sample.py (L4-21)
Signature:
def load_component_from_url(
url: str,
auth: Optional[Tuple[str, str]] = None,
) -> Callable:
"""Loads a component from a URL pointing to a component YAML file."""
Import:
from kfp import components
I/O Contract
| Direction | Name | Type | Required | Description |
|---|---|---|---|---|
| Input | url | str | Yes | URL to component YAML file |
| Output | return | Callable | — | A component factory function with parameters matching the YAML spec's inputs |
Usage Examples
Loading XGBoost components (xgboost_sample.py)
from kfp import components
chicago_taxi_dataset_op = components.load_component_from_url(
'https://raw.githubusercontent.com/kubeflow/pipelines/e3337b8bdcd63636934954e592d4b32c95b49129/components/datasets/Chicago%20Taxi/component.yaml'
)
xgboost_train_on_csv_op = components.load_component_from_url(
'https://raw.githubusercontent.com/kubeflow/pipelines/567c04c51ff00a1ee525b3458425b17adbe3df61/components/XGBoost/Train/component.yaml'
)
xgboost_predict_on_csv_op = components.load_component_from_url(
'https://raw.githubusercontent.com/kubeflow/pipelines/31939086d66d633732f75300ce69eb60e9fb0269/components/XGBoost/Predict/component.yaml'
)