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name: pipeline-workflows description: "Service-backed FATE-Client Pipeline workflows for data upload, Reader/PSI, preprocessing, model training, evaluation, deploy/predict, and upload-config validation." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

pipeline-workflows

Use this sub-skill when a user wants to author, adapt, or debug a service-backed FATE-Client Pipeline workflow: upload local data into FATE storage, construct Reader/PSI and downstream components, train/evaluate a model, dump/deploy it, run prediction, or validate example upload configuration shape before contacting a service.

Route here for

• Uploading CSV/example data with FateFlowPipeline.transform_local_file_to_dataframe(...) and mapping it to FATE table namespace/name pairs.
• Building pipeline DAGs with Reader, PSI, DataSplit, FeatureScale, HeteroFeatureBinning, HeteroFeatureSelection, HeteroSecureBoost, HomoLR, HomoNN, HeteroNN, SSHELR, SSHELinR, CoordinatedLR, CoordinatedLinR, Evaluation, Union, and example-backed statistics/sample/correlation components.
• Handling table naming, namespace conventions, guest/host/arbiter party ids, and multi-host reader.hosts[...] mapping.
• Saving and reusing trained pipelines with dump_model, load_model, deploy, get_deployed_pipeline, and predict.
• Safely checking upload-config YAML structure without contacting FateFlow.

Route elsewhere

• FateFlow service installation/startup, Docker/host deployment, port conflicts, or fate_flow/pipeline init setup: use ../deployment/SKILL.md.
• Service-free direct module execution with fate.arch.launchers or fate.ml: use ../local-launchers/SKILL.md.
• Low-level python -m fate.components CLI descriptors, task-schema, custom component internals, or component execution configs: use ../component-runtime/SKILL.md.

Operating constraints

• These workflows require a running FateFlow service and initialized pipeline client. The verified CLI surfaces are fate_flow init|restart|start|status|stop|version and pipeline init|show|site-info; fate_flow init accepts --ip, --port, --home, and pipeline init accepts --ip, --port, --path.
• The inspected package set was pyfate==2.2.0 (import fate), fate_client==2.2.0, fate_flow==2.2.0, and fate_utils==0.1.0. Treat GPU and DeepSpeed as optional and unverified here; the minimum verified drafting/inspection backend is CPU.
• Service-backed training and upload examples are normally not run during skill generation or environment preparation. Use them as recipes unless the user supplies a live FateFlow service, uploaded data, party ids, and an execution budget.
• Do not route deployment shell scripts or service mutation into this sub-skill. Keep runtime actions at the Python pipeline API level after service prerequisites are satisfied.

Fast workflow map

1. Service gate: confirm FateFlow is initialized/running and pipeline is initialized. If not, route to deployment.
2. Data gate: validate upload configs with scripts/validate_upload_config.py; then upload/transform local files using FateFlowPipeline().set_parties(local="0") and transform_local_file_to_dataframe(...).
3. Reader/PSI gate: construct Reader with exact namespace/name for each role; add PSI for hetero alignment when parties have overlapping match ids.
4. Feature/model DAG: choose preprocessing and model components from references/component-catalog.md, wire outputs by explicit artifact names, then add_tasks, compile, and fit.
5. Inspect/save: use get_task_info(task_name) for metrics/models and dump_model(file_path) for reuse.
6. Deploy/predict: reload with FateFlowPipeline.load_model(file_path), call deploy([...]) on the trained components that should be reused, attach a fresh Reader, then build a prediction pipeline and call predict().

Bundled references and helper

• references/pipeline-workflows.md — stepwise upload, Reader/PSI, train/evaluate, deploy/predict recipes.
• references/component-catalog.md — component cheat sheet with verified method/constructor call shapes and common use cases.
• references/data-formats.md — table naming, namespaces, role conventions, upload YAML fields, and sample/match id rules.
• references/troubleshooting.md — service, table/namespace, party, model, deploy, and predict diagnostics.
• scripts/validate_upload_config.py — local YAML parser/validator; it never contacts FateFlow.