fix: backport #254 — auto-infer is_evaluation when dataset_id, datapoint_id, and run_id are all defined#267
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…id are all defined When all three evaluation identifiers (run_id, dataset_id, datapoint_id) are present, automatically set is_evaluation=True so users don't need to pass it explicitly. This prevents session lifecycle issues where evaluation context was not propagated because the flag was missing. Co-Authored-By: unknown <>
Co-Authored-By: unknown <>
Ensures _setup_evaluation_context_dynamically reads the correct is_evaluation value from config after auto-inference. Co-Authored-By: unknown <>
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integration tests are already in broken state on base branch, ignoring the failure here... |
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Summary
Backport of #254 from
federated-sdk-release-candidatetolegacy-sdk.When users pass
dataset_id,datapoint_id, andrun_idto the tracer but forgetis_evaluation=True, evaluation context (baggage propagation, session metadata, source defaulting to"evaluation") is silently skipped. This causes session lifecycle issues where sessions are overwritten between experiment and tracer instances.This adds inference logic in
_initialize_core_attributes()that auto-setsis_evaluation=Truewhen all three evaluation identifiers are present. Bothself.is_evaluationandconfig["is_evaluation"]are updated together so that_setup_evaluation_context_dynamically(config)receives the correct value downstream.Review & Testing Checklist for Human
is_evaluation=Falseoverride: The check usesnot self.is_evaluation, which cannot distinguish between the Pydantic defaultFalseand an explicitis_evaluation=Falsefrom the user. If a user passes all three IDs alongside explicitis_evaluation=False, inference will override it toTrue. Confirm this edge case is acceptable onlegacy-sdk._setup_evaluation_context_dynamically(config)onlegacy-sdkreadsconfig["is_evaluation"]the same way as onfederated-sdk-release-candidate, so the config sync fix is actually needed and correct here.is_evaluation=True) and asserts evaluation mode is inferred.Notes