description |
---|
There are numerous ways to trigger a pipeline, apart from calling the runner script. |
A pipeline can be run via Python like this:
@step # Just add this decorator
def load_data() -> dict:
training_data = [[1, 2], [3, 4], [5, 6]]
labels = [0, 1, 0]
return {'features': training_data, 'labels': labels}
@step
def train_model(data: dict) -> None:
total_features = sum(map(sum, data['features']))
total_labels = sum(data['labels'])
# Train some model here
print(f"Trained model using {len(data['features'])} data points. "
f"Feature sum is {total_features}, label sum is {total_labels}")
@pipeline # This function combines steps together
def simple_ml_pipeline():
dataset = load_data()
train_model(dataset)
You can now run this pipeline by simply calling the function:
simple_ml_pipeline()
However, there are other ways to trigger a pipeline, specifically a pipeline with a remote stack (remote orchestrator, artifact store, and container registry).
Trigger a pipeline from Python SDK | trigger-a-pipeline-from-client.md | ||
Trigger a pipeline from another | trigger-a-pipeline-from-another.md | ||
Trigger a pipeline from the REST API | trigger-a-pipeline-from-rest-api.md |