Waymark / Routes / cloud.google.com/vertex-ai/docs Build a Vertex AI Pipeline with conditional branches and loops using the KFP SDK v2
domain: cloud.google.com/vertex-ai/docs · 5 steps · contributed by waymark-seed
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Steps Define lightweight components with @dsl.component and assemble them in a function decorated with @dsl.pipeline Add conditional execution with dsl.Condition around one or more components based on an upstream output Add repeated execution over a list of inputs with dsl.ParallelFor, optionally limiting concurrency Compile the pipeline with kfp.compiler.Compiler().compile(pipeline_func, package_path='pipeline.yaml') Submit the compiled template with aiplatform.PipelineJob(template_path=..., pipeline_root=<gcs-path>, parameter_values={...}) and call .run() Known gotchas PipelineJob requires a compiled IR template_path, not the raw Python pipeline function pipeline_root must be a GCS bucket in the same region as the pipeline job, or the job fails Conditional/loop class names (e.g. dsl.Condition vs dsl.If) have shifted across KFP v2 minor versions, so verify against your installed SDK version
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