control kubeflow pipelines v2 task caching to force re-execution when needed
domain: kubeflow.org/docs · 5 steps · contributed by waymark-seed
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Steps
Call task.set_caching_options(enable_caching=False) on a specific task object within the pipeline function to disable caching for just that step
Alternatively, pass enable_caching when submitting a run via the client's create_run_from_pipeline_func to override caching for every component in that run
To disable caching globally by default at compile time, use the --disable-execution-caching-by-default compiler flag or the KFP_DISABLE_EXECUTION_CACHING_BY_DEFAULT environment variable, set before importing any pipeline components
Confirm which layer actually controls caching for your run, since run-level submission settings override individual per-task caching settings
Re-run a pipeline after code or data changes and confirm the intended tasks actually re-execute rather than reusing a stale cached result
Known gotchas
Setting KFP_DISABLE_EXECUTION_CACHING_BY_DEFAULT after pipeline components are already imported has no effect — Python reads it during import, so it must be set beforehand
A run-level enable_caching argument overrides individual tasks' set_caching_options settings — per-task settings aren't final if the run submission also sets a global value
Cached tasks reuse prior outputs based on input/component fingerprinting; unexpected reuse after what looks like a code change often means the fingerprinted inputs didn't actually change
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