We provide here examples of FATE jobs, including FATE-Pipeline scripts, DSL conf files, and modeling quality comparison tasks
We suggest that user use example-runner tool FATE-Test.
Below lists included types of examples.
To enhance usability of FATE, starting at FATE-v1.5, FATE provides python APIs. User may develop federated learning models conveniently with FATE-Pipeline. We provide a host of Pipeline examples for each FATE module and a quick start guide for Pipeline here
DSL is the first method FATE provides for constructing federated modeling jobs. For more information, please refer this guide on DSL.
Upgraded DSL(DSL v2) by FATE-v1.5 comes with the following major features:
- Predictions DSL may now be configured through FATE-Flow cli. Please note that new DSL no longer supports automatic formation of prediction DSL; user needs to first form DSL manually with FATE-Flow cli before running prediction task.
- New components may now be added to prediction DSL;
for instance,
evaluationmodule may be added to prediction task. - Standardize style of
role_parameterandalgorithm_parameter.
For DSL v2 examples, please refer dsl/v2. For examples of the older version, please refer dsl/v1. Please note that starting at version 1.6, FATE may no longer support DSL v1.
Starting at FATE-v1.5, FATE provides modeling quality verifier for comparing modeling quality of centralized training and FATE federated modeling. As of v1.5, we have provided quality comparison scripts for the following common models:
- heterogeneous scenario: LogisticRegression(benchmark_quality/hetero_lr), 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.
To run the comparison, please refer to the guide on benchmark_quality.
FATE provides a collection of publicly available data at data directory, along with a utility script for uploading all data sets. User may use the provided script to upload all pre-given data, or modify the corresponding configuration file for uploading arbitrary data. Please refer scripts for details.
FATE provides simple toy job for quick experiment when user developing FATE modules or testing for deployment. For details, please refer toy_example.
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.