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feat(pipeline): Add inference and lineage step types - #6224
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Add 4 pipeline step classes: - EndpointConfigStep, EndpointStep (SageMaker inference deployment) - InferenceComponentStep (multi-model endpoint support) - LineageStep (ML governance tracking) Design: each step accepts an 'arguments: Dict[str, Any]' forwarded to the pipeline service. Top-level argument keys are validated client-side against the corresponding public AWS API input shape (botocore service model) at construction and at serialization; fields the service is known to reject fail fast with actionable errors (EndpointConfig: DataCaptureConfig, ExplainerConfig; Endpoint: DeploymentConfig). Values are not validated -- they may be pipeline variables resolved at compile time. Full schema validation remains server-side. If the installed botocore does not know an operation, shape validation is skipped and the service remains the authority. Retryability: only EndpointConfigStep is retryable. Cacheability: EndpointConfigStep and EndpointStep are structurally cacheable via cache_config. Includes 23 unit tests and a LineageStep end-to-end integration test. --- X-AI-Prompt: Add the inference and lineage pipeline step types to the Python SDK with client-side argument validation X-AI-Tool: kiro-cli
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| return _SHAPE_CACHE[cache_key] | ||
| def validate_step_arguments( |
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why do we need all of this additional validation here? Other steps do not have this explicit validation. How are these steps different from other steps?
| def __init__( | ||
| self, | ||
| name: str, | ||
| arguments: Dict[str, Any], |
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This is not the right implementation for any of these steps. It will be very difficult to construct these arguments manually. We need to use the existing pysdk constructs and pass them as arguments. Please see how Training/Model steps are implemented and follow that pattern here. You must use step_args from PipelineSession instead of raw arguments: dict
SDK primitives exists for all four steps, and it eliminates the entire _argument_validation.py machinery
| return get_execution_role() | ||
| def test_lineage_step_execute_end_to_end(sagemaker_session, pipeline_session, role): |
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please add integ tests for other steps as well
…steps Address review feedback: EndpointConfigStep, EndpointStep, and InferenceComponentStep now take step_args captured under a PipelineSession, following the convention used by TrainingStep and ModelStep, instead of a raw arguments dict. - Session.endpoint_from_production_variants, Session.create_endpoint, and Session.create_inference_component now route their service calls through _intercept_create_request. Under a plain Session the behavior is unchanged (the intercept is a pass-through); under a PipelineSession the request is captured and returned as step arguments, and no service call is made. - Each step validates the provenance of its step_args via validate_step_args_input (wrong producer or a raw dict is rejected). - The _argument_validation module is removed: requests are now built by the session methods rather than hand-authored, so client-side key validation is no longer needed. - Adds an integration test chaining EndpointConfigStep -> EndpointStep -> InferenceComponentStep in a single pipeline execution, with full resource cleanup. LineageStep is unchanged pending a design decision on multi-entity step arguments. --- X-AI-Prompt: Rework the inference step types to use step_args captured via PipelineSession per review feedback X-AI-Tool: kiro-cli
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Add 4 pipeline step classes:
Design: each step accepts an 'arguments: Dict[str, Any]' forwarded to the pipeline service. Top-level argument keys are validated client-side against the corresponding public AWS API input shape (botocore service model) at construction and at serialization; fields the service is known to reject fail fast with actionable errors (EndpointConfig: DataCaptureConfig, ExplainerConfig; Endpoint: DeploymentConfig). Values are not validated -- they may be pipeline variables resolved at compile time. Full schema validation remains server-side. If the installed botocore does not know an operation, shape validation is skipped and the service remains the authority.
Retryability: only EndpointConfigStep is retryable. Cacheability: EndpointConfigStep and EndpointStep are structurally cacheable via cache_config.
Includes 23 unit tests and a LineageStep end-to-end integration test. ---
X-AI-Prompt: Add the inference and lineage pipeline step types to the Python SDK with client-side argument validation
X-AI-Tool: kiro-cli
Issue #, if available:
Description of changes:
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