execution.list_steps()

step_process = ProcessingStep(
    name="PreprocessAbaloneData",
    processor=sklearn_processor,
    inputs = [
        ProcessingInput(
            input_name='dataset',
            source=...,
            destination="/opt/ml/processing/code",
        )
    ],
    outputs=[
        ProcessingOutput(output_name="train", source="/opt/ml/processing/train", destination = processed_data_path),
        ProcessingOutput(output_name="validation", source="/opt/ml/processing/validation", destination = processed_data_path),
        ProcessingOutput(output_name="test", source="/opt/ml/processing/test", destination = processed_data_path),
    ],
    code=os.path.join(BASE_DIR, "process.py"), ## Code is passed through an argument
    cache_config = cache_config,
    job_arguments = ['--input', 'arg1']
)

sklearn_estimator = SKLearn(
    entry_point=os.path.join(BASE_DIR, "train.py"), ## Code is passed through the entry_point
    framework_version="0.23-1",
    instance_type=training_instance_type,
    role=role,
    output_path=model_path, # New
    sagemaker_session=sagemaker_session, # New
    instance_count=1, # New
    base_job_name=f"{base_job_prefix}/pilot-train",
    metric_definitions=[
        {'Name': 'train:accuracy', 'Regex': 'accuracy_train=(.*?);'},
        {'Name': 'validation:accuracy', 'Regex': 'accuracy_validation=(.*?);'}
    ],
)