src/cloud/pipelines/image_classification/pipeline.py [152:180]:
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    preprocessor = SKLearnProcessor(
        framework_version="0.23-1",
        role=role,
        instance_type=processing_instance_type,
        instance_count=processing_instance_count,
        max_runtime_in_seconds=7200,
    )

    # A preprocessing report to store some information from the preprocessing step for next steps
    preprocessing_report = PropertyFile(
        name='PreprocessingReport',
        output_name='preprocessing_report',
        path='preprocessing_report.json'
    )
    
    # Preprocessing Step
    step_process = ProcessingStep(
        name="DefectDetectionPreprocessing",
        code=os.path.join(BASE_DIR, 'preprocessing.py'), ## this is the script defined above
        processor=preprocessor,
        inputs=[
            ProcessingInput(source=input_data, destination='/opt/ml/processing/input')
        ],
        outputs=[
            ProcessingOutput(output_name='train_data', source='/opt/ml/processing/train'),
            ProcessingOutput(output_name='test_data', source='/opt/ml/processing/test'),
            ProcessingOutput(output_name='val_data', source='/opt/ml/processing/val'),
            ProcessingOutput(output_name='preprocessing_report', source='/opt/ml/processing/report')
        ],
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src/cloud/pipelines/semantic_segmentation/pipeline.py [112:140]:
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    preprocessor = SKLearnProcessor(
        framework_version="0.23-1",
        role=role,
        instance_type=processing_instance_type,
        instance_count=processing_instance_count,
        max_runtime_in_seconds=7200,
    )

    # A preprocessing report to store some information from the preprocessing step for next steps
    preprocessing_report = PropertyFile(
        name='PreprocessingReport',
        output_name='preprocessing_report',
        path='preprocessing_report.json'
    )

    # Preprocessing Step
    step_process = ProcessingStep(
        name="DefectDetectionPreprocessing",
        code=os.path.join(BASE_DIR, 'preprocessing.py'), ## this is the script defined above
        processor=preprocessor,
        inputs=[
            ProcessingInput(source=input_data, destination='/opt/ml/processing/input')
        ],
        outputs=[
            ProcessingOutput(output_name='train_data', source='/opt/ml/processing/train'),
            ProcessingOutput(output_name='test_data', source='/opt/ml/processing/test'),
            ProcessingOutput(output_name='val_data', source='/opt/ml/processing/val'),
            ProcessingOutput(output_name='preprocessing_report', source='/opt/ml/processing/report')
        ],
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