secretflow

SecretFlow

SecretFlow is a unified framework for privacy-preserving data intelligence and machine learning. Use this skill when the task mentions the secretflow package, sf.init, PYU, SPU, HEU, TEEU, VDataFrame, HDataFrame, FedNdarray, component, preprocessing, statistics, classical ML, PSI, Kuscia, or TEE-style deployment.

Start here

• Use Python 3.10 for the most reliable runtime coverage.
• Install secretflow in the target environment and keep python -m pip check clean.
• Minimal import check: python -c "import secretflow as sf; print(sf.__version__)"
• If you are validating an installed environment, run scripts/check_secretflow_install.py.
• Read references/repo-provenance.md when checking whether this skill still matches the repository snapshot that produced it.
• Read references/troubleshooting.md when the install, import, CLI, backend, or deployment path is failing.

Route map

sub-skills/runtime-data/

Use this route for local startup, device creation, reveal/to/wait behavior, FedNdarray, HDataFrame, VDataFrame, MixDataFrame, CSV/ORC IO, and small local simulation flows.

Typical triggers:

• sf.init with local or a simple cluster_config
• PYU, SPU, HEU, TEEU
• reveal, to, wait
• read_csv, read_orc, partition
• device-mismatch or direct-to-SPU/HEU errors

sub-skills/component-cli/

Use this route for the component registry and the secretflow component command family, component evaluation payloads, model export, serving model inferencer, and plugin packaging.

Typical triggers:

• secretflow component ls|inspect|translate|get_translation|run
• comp_eval, Registry, NodeEvalParam, StorageConfig, DistData
• model export or serving package generation
• plugin entry-point or component registration questions

sub-skills/analytics/

Use this route for preprocessing, statistics, score-card helpers, and direct classical ML APIs.

Typical triggers:

• StandardScaler
• psi_eval, table_statistics, categorical_statistics, ScoreCard
• SSGLM, SSRegression, FlLogisticRegressionMix, FlLogisticRegressionVertical, HESSLogisticRegression
• KMeans, GNB, GPC, KNNClassifer, and related model workflows

sub-skills/privacy-orchestration/

Use this route for PSI, secure aggregation/comparison, Kuscia, TEEU simulation, and deployment-mode questions.

Typical triggers:

• psi_df, psi_eval, SPUAggregator, PlainComparator
• KusciaTaskConfig, get_sf_cluster_config, convert_domain_data_to_individual_table
• TEEU, auth_manager, Ray/Kuscia setup, simulation/production mode choice
• deployment and orchestration troubleshooting

Working style

• Read the nearest sub-skill first, then cross-reference the shared root troubleshooting guidance when something fails.
• Keep runtime instructions inside this skill tree; do not rely on the original repository checkout being present later.
• For backend-specific work, prefer the smallest environment that can truthfully cover the selected task. CPU coverage is enough for the core local runtime and component CLI paths in this skill; advanced GPU, Kuscia, or TEE paths are routed to their own guidance.

Companion files

• references/repo-provenance.md — source snapshot and refresh baseline.
• references/repo-routing-metadata.json — router metadata for repo-skills routing.
• references/troubleshooting.md — cross-cutting install/import/backend issues.
• scripts/check_secretflow_install.py — quick environment and API smoke check.