The [pipeline_from_decorator.py](https://github.com/allegroai/clearml/blob/master/examples/pipeline/pipeline_from_decorator.py)
example demonstrates the creation of a pipeline in ClearML using the [`PipelineDecorator`](../../references/sdk/automation_controller_pipelinecontroller.md#class-automationcontrollerpipelinedecorator)
class.
This example creates a pipeline incorporating four tasks, each of which is created from a python function using a custom decorator:
*`executing_pipeline`- Implements the pipeline controller which defines the pipeline structure and execution logic.
*`step_one` - Downloads and processes data.
*`step_two` - Further processes the data from `step_one`.
*`step_three` - Uses the processed data from `step_two` to train a model.
decorator, which creates a ClearML pipeline step for each one when the pipeline is executed.
The logic that executes these steps and controls the interaction between them is implemented in the `executing_pipeline`
function. This function is wrapped with the [`@PipelineDecorator.pipeline`](../../references/sdk/automation_controller_pipelinecontroller.md#pipelinedecoratorpipeline)
decorator which creates the ClearML pipeline task when it is executed.
The sections below describe in more detail what happens in the pipeline controller and steps.
## Pipeline Controller
In this example, the pipeline controller is implemented by the `executing_pipeline` function.
Using the `@PipelineDecorator.pipeline` decorator creates a ClearML Controller Task from the function when it is executed.
For detailed information, see [`@PipelineDecorator.pipeline`](../../references/sdk/automation_controller_pipelinecontroller.md#pipelinedecoratorpipeline).
In the example script, the controller defines the interactions between the pipeline steps in the following way:
1. The controller function passes its argument, `pickle_url`, to the pipeline's first step (`step_one`)
1. The returned data from the first step, `data_frame`, is passed to `step_two`
the <spanclass="link-code"><ahref="../../references/sdk/automation_controller_pipelinecontroller#pipelinedecoratorrun_locally"><code>PipelineDecorator.run_locally</code></a></span>
method before calling the pipeline function. See pipeline execution options [here](../../pipelines/pipelines_sdk_function_decorators.md#running-the-pipeline).
Using the `@PipelineDecorator.component` decorator will make the function a pipeline component that can be called from the
pipeline controller, which implements the pipeline's execution logic. For detailed information, see [`@PipelineDecorator.component`](../../references/sdk/automation_controller_pipelinecontroller.md#pipelinedecoratorcomponent).
When the pipeline controller calls a pipeline step, a corresponding ClearML task will be created. For this reason, each
function which makes up a pipeline step needs to be self-contained. Notice that all package imports inside the function
will be automatically logged as required packages for the pipeline execution step.
By default, the pipeline controller and the pipeline steps are launched through ClearML [queues](../../fundamentals/agents_and_queues.md#what-is-a-queue).
Use the [`PipelineDecorator.set_default_execution_queue`](../../references/sdk/automation_controller_pipelinecontroller.md#pipelinedecoratorset_default_execution_queue)
