FateWithShellmiaoComment/Fate/examples

Example Usage Guide

中文

We provide here examples of FATE jobs, including FATE-Pipeline scripts, DSL conf files, and modeling quality comparison tasks

For auto-test of FATE, FATE provides auto-test tools FATE-Test.

To make it easy to submit FL modeling tasks using Pipeline or DSL, we recommend that users install FATE-Client.

To quickly start to use pipeline or dsl, please refer to tutorial

Below lists included types of examples.

FATE-Pipeline

To enhance usability of FATE, starting at FATE-v1.5, FATE provides Pipeline Module. User may develop federated learning models conveniently using python API. Please refer this: FATE-Pipeline. We provide many Pipeline examples for each FATE module.

DSL

DSL is language of building federated modeling tasks based on configuration file for FATE. For more information, please refer this dsl setting guide on DSL.

Upgraded DSL(DSL v2) by FATE-v1.5 comes with the following major features:

1. Predictions DSL may now be configured through FATE-Flow cli. Please note that with new DSL training job will no longer automatically form prediction DSL; user needs to first form DSL manually with FATE-Flow cli before running prediction task.
2. New components may now be added to prediction DSL; for instance, evaluation module may be added to prediction task.
3. Standardize style of role_parameter and algorithm_parameter.

For DSL v2 examples, please refer dsl/v2. Please note that starting at version 1.7, FATE may no longer support DSL/v1 and remove related examples. However, tools will be provided to transform DSL/ V1 built models into DSL/ V2 to facilitate model migration to DSL/ V2.

Benchmark Quality

Starting at FATE-v1.5, FATE provides modeling quality verifier for comparing modeling quality of centralized training and FATE federated modeling. We have provided quality comparison scripts for the following common models:

• heterogeneous scenario:
  • LogisticRegression(benchmark_quality/hetero_lr)
  • LinearRegression(benchmark_quality/hetero_linear_regression)
  • SecureBoost(benchmark_quality/hetero_sbt)
  • FastSecureBoost(benchmark_quality/hetero_fast_sbt)
  • NN(benchmark_quality/hetero_nn)
• homogeneous scenario:
  • LogisticRegression(benchmark_quality/homo_lr)
  • SecureBoost(benchmark_quality/homo_sbt)
  • NN(benchmark_quality/homo_nn

Starting at v1.6, benchmark quality supports matching metrics from the same script. For more details, please refer to the benchmark-quality guide.

Benchmark Performance

FATE-Test also provides performance benchmark for FATE federated modeling. We include benchmark test suites for the following common models:

• Hetero Logistic Regression(benchmark_performance/hetero_lr),
• Hetero SecureBoost(benchmark_performance/hetero_sbt),
• Hetero SSHE LR(benchmark_performance/hetero_fast_sbt),
• Hetero Intersect:
  • benchmark_performance/intersect_single
  • benchmark_performance/intersect_multi
    

For more details, please refer to the benchmark-performance guide.

Upload Default Data

user may use FATE-Test for uploading data.

Toy Example

FATE provides simple toy example for quick testing the network connectivity between sites Such as

flow test toy -gid 9999 -hid 10000

Min-test

Min-test is used for deployment testing and quick modeling demo. Min-test includes tasks of hetero Logistic Regression and hetero SecureBoost. User only needs to configure few simple parameters to run a full modeling job with FATE. Please refer min_test_task for instructions.