---

name: local-launchers description: "Run and author service-free local FATE launchers with fate.arch.launchers, dataframe readers, and direct fate.ml module APIs." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Local Launchers

Use this sub-skill when the task is to run, inspect, or author service-free local FATE launchers for direct fate.ml modules. These launchers simulate federated parties with local multiprocessing and do not require FateFlow, Pipeline, Docker, or a running FATE service.

Route map

• Read references/launcher-recipes.md for local launcher structure, party/data rules, and recipes for SSHE LR/LinR, SecureBoost, Pearson, Hetero/Homo NN, FedPass, and SMPC-style proc launchers.
• Read references/module-api.md when selecting imports, checking constructor signatures, choosing CSVReader/PandasReader/TableReader, or wiring create_context, launch, HfArgumentParser, and fate.ml trainers.
• Run scripts/check_launcher_imports.py for cheap import/signature checks before any training. It is safe by default and will not spawn launcher training unless an explicit heavy-run flag is supplied.
• Read references/troubleshooting.md for missing fate_utils/setuptools, bad party tuples, missing CSVs, local-vs-cluster context mistakes, GPU-only assumptions, and SMPC import issues.

Use this instead of service-backed routes when

• The user says “local launcher”, “without FateFlow”, “no Pipeline service”, “direct fate.ml API”, “multiprocess launcher”, or “local federated simulation”.
• The user has local CSV/Pandas data and wants to test a FATE algorithm module without uploading tables through FateFlow.
• The task is to adapt launcher patterns around launch(run_fn), create_context(...), CSVReader, PandasReader, TableReader, or local TrainingArguments.

Route elsewhere

• For installing PyPI packages, starting/stopping FateFlow services, Docker/Compose, cluster service ports, or service smoke checks, use ../deployment/SKILL.md.
• For service-backed FateFlowPipeline workflows, upload/Reader/components, model deployment, or service prediction, use ../pipeline-workflows/SKILL.md.
• For python -m fate.components, component descriptors, component task-schema, or component CLI execution planning, use ../component-runtime/SKILL.md.

Safe default workflow

1. Confirm this is a local launcher task, not a FateFlow Pipeline task.
2. Select the party layout:
   • Hetero algorithms: usually guest:9999 host:10000.
   • Homo NN FedAVG: guest, host, and arbiter; the arbiter is the server.
   • SMPC-style proc wrappers: parties must match the target MPC module’s expectation.
3. Check imports and signatures before training:

   python scripts/check_launcher_imports.py --check-standard
   python scripts/check_launcher_imports.py \
     --module fate.ml.glm.hetero.sshe --object SSHELogisticRegression
   

4. Prepare tiny local data first. Guest-side hetero tabular recipes normally carry labels; host-side hetero recipes normally omit labels but share the match-id column.
5. Only after import/data checks pass, run launch(run_fn) or an explicit launcher command. Treat this as a heavy training run because it spawns one process per party and may run cryptographic or neural-network code.

Backend caveat

The verified construction baseline was CPU-only. Local launcher imports, dataframe readers, component-free algorithms, and CPU PyTorch patterns are in scope. GPU and DeepSpeed-on-Eggroll are documented optional/advanced paths, not required or verified defaults for this sub-skill.