test: Add enhanced ML workflow pipeline for integration tests

backend/test/resources/enhanced_artifact_pipeline.py

@@ -0,0 +1,82 @@
+import kfp
+from kfp import dsl
+from kfp.components import create_component_from_func, OutputPath, InputPath
+
+# Component 1: Data Processing
+def process_data(input_text: str, processed_data: OutputPath()):
+    """Process input data and create training dataset"""
+    # Simulate data processing
+    processed_content = f"PROCESSED: {input_text.upper()}"
+
+    with open(processed_data, 'w') as f:
+        f.write(processed_content)
+
+    print(f"Processed data saved to: {processed_data}")
+
+# Component 2: Training (consumes processed data)
+def train_model(training_data: InputPath(), model_path: OutputPath()):
+    """Train a simple model using processed data"""
+    # Read processed data
+    with open(training_data, 'r') as f:
+        data = f.read()
+
+    # Simulate model training
+    model_content = f"MODEL_TRAINED_ON: {data}"
+
+    with open(model_path, 'w') as f:
+        f.write(model_content)
+
+    print(f"Model trained and saved to: {model_path}")
+
+# Component 3: Evaluation
+def evaluate_model(model: InputPath(), training_data: InputPath(), results: OutputPath()):
+    """Evaluate model and produce results"""
+    # Read inputs
+    with open(model, 'r') as f:
+        model_data = f.read()
+
+    with open(training_data, 'r') as f:
+        train_data = f.read()
+
+    # Simulate evaluation
+    evaluation_results = f"EVALUATION: Model '{model_data}' trained on '{train_data}' - Score: 95%"
+
+    with open(results, 'w') as f:
+        f.write(evaluation_results)
+
+    print(f"Evaluation results saved to: {results}")
+    return evaluation_results
+
+# Create components
+process_data_op = create_component_from_func(process_data)
+train_model_op = create_component_from_func(train_model)  
+evaluate_model_op = create_component_from_func(evaluate_model)
+
+@dsl.pipeline(name="enhanced-ml-workflow-test")
+def enhanced_artifact_pipeline(input_text: str = "kubeflow pipelines"):
+    """Enhanced pipeline with realistic ML workflow: Data → Train → Evaluate"""
+
+    # Data processing task
+    process_task = process_data_op(input_text)
+    process_task.set_display_name("Process Training Data")
+
+    # Model training task (depends on processed data)
+    train_task = train_model_op(process_task.outputs['processed_data'])
+    train_task.set_display_name("Train ML Model")
+
+    # Model evaluation task (depends on both model and training data)
+    eval_task = evaluate_model_op(
+        train_task.outputs['model_path'],
+        process_task.outputs['processed_data']
+    )
+    eval_task.set_display_name("Evaluate Model")
+
+    # Return final results
+    return eval_task.outputs
+
+if __name__ == "__main__":
+    kfp.compiler.Compiler().compile(
+        pipeline_func=enhanced_artifact_pipeline,
+        package_path="enhanced_artifact_pipeline.yaml"
+    )
+    print("Enhanced pipeline compiled!")

sdk/python/pyproject.toml

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+[build-system]
+requires = ["setuptools>=45", "wheel"]
+build-backend = "setuptools.build_meta"
+
+[project]
+name = "kfp"
+dynamic = ["version"]
+description = "Kubeflow Pipelines SDK"
+readme = {file = "README.md", content-type = "text/markdown"}
+requires-python = ">=3.9.0"
+license = {text = "Apache-2.0"}
+authors = [
+    {name = "The Kubeflow Authors"}
+]
+keywords = ["kubeflow", "pipelines", "machine-learning", "mlops"]
+classifiers = [
+    "Intended Audience :: Developers",
+    "Intended Audience :: Education", 
+    "Intended Audience :: Science/Research",
+    "License :: OSI Approved :: Apache Software License",
+    "Programming Language :: Python :: 3",
+    "Programming Language :: Python :: 3.9",
+    "Programming Language :: Python :: 3.10",
+    "Programming Language :: Python :: 3.11",
+    "Programming Language :: Python :: 3.12",
+    "Programming Language :: Python :: 3.13",
+    "Topic :: Scientific/Engineering",
+    "Topic :: Scientific/Engineering :: Artificial Intelligence",
+    "Topic :: Software Development",
+    "Topic :: Software Development :: Libraries",
+    "Topic :: Software Development :: Libraries :: Python Modules",
+]
+dependencies = [
+    "click>=8.1.8",
+    "click-option-group==0.5.7", 
+    "docstring-parser>=0.7.3,<1",
+    "google-api-core>=1.31.5,<3.0.0dev,!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.0",
+    "google-auth>=1.6.1,<3",
+    "google-cloud-storage>=2.2.1,<4",
+    "kfp-pipeline-spec>=2.14.3,<3",
+    "kfp-server-api>=2.14.3,<3", 
+    "kubernetes>=8.0.0,<31",
+    "protobuf>=6.31.1,<7.0",
+    "PyYAML>=5.3,<7",
+    "requests-toolbelt>=0.8.0,<2",
+    "tabulate>=0.8.6,<1",
+    "urllib3<3.0.0",
+    'typing-extensions>=3.7.4,<5; python_version<"3.9"'
+]
+
+
+[project.optional-dependencies]
+all = [
+    "docker",
+    "kfp-kubernetes",
+    "nbclient>=0.10,<1", 
+    "ipykernel>=6,<7",
+    "jupyter_client>=7,<9"
+]
+kubernetes = ["kfp-kubernetes"]
+notebooks = [
+    "nbclient>=0.10,<1",
+    "ipykernel>=6,<7", 
+    "jupyter_client>=7,<9"
+]
+
+[project.urls]
+Documentation = "https://kubeflow-pipelines.readthedocs.io/en/stable/"
+Bug-Tracker = "https://github.com/kubeflow/pipelines/issues"
+Source = "https://github.com/kubeflow/pipelines/tree/master/sdk"
+Changelog = "https://github.com/kubeflow/pipelines/blob/master/sdk/RELEASE.md"
+
+[project.scripts]
+dsl-compile = "kfp.cli.compile_:main"
+kfp = "kfp.cli.__main__:main"
+
+[tool.setuptools.dynamic]
+version = {attr = "kfp.version.__version__"}