https://github.com/mesalock-linux/gbdt-rs
MesaTEE GBDT-RS : a fast and secure GBDT library, supporting TEEs such as Intel SGX and ARM TrustZone
https://github.com/mesalock-linux/gbdt-rs
Last synced: about 2 months ago
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MesaTEE GBDT-RS : a fast and secure GBDT library, supporting TEEs such as Intel SGX and ARM TrustZone
- Host: GitHub
- URL: https://github.com/mesalock-linux/gbdt-rs
- Owner: mesalock-linux
- License: apache-2.0
- Created: 2018-12-18T22:37:28.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2025-01-29T18:49:13.000Z (4 months ago)
- Last Synced: 2025-04-11T17:45:27.839Z (2 months ago)
- Language: Rust
- Homepage:
- Size: 896 KB
- Stars: 213
- Watchers: 6
- Forks: 27
- Open Issues: 7
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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- Awesome-SGX-Open-Source - https://github.com/mesalock-linux/gbdt-rs
- StarryDivineSky - mesalock-linux/gbdt-rs - RS：一个快速且安全的 GBDT 库，支持 Intel SGX 和 ARM TrustZone 等 TEE (梯度提升和树模型)
README
# MesaTEE GBDT-RS : a fast and secure GBDT library, supporting TEEs such as Intel SGX and ARM TrustZone
[](https://ci.mesalock-linux.org/mesalock-linux/gbdt-rs)
[](https://codecov.io/gh/mesalock-linux/gbdt-rs)
MesaTEE GBDT-RS is a gradient boost decision tree library written in Safe Rust. There is no unsafe rust code in the library.
MesaTEE GBDT-RS provides the training and inference capabilities. And it can use the models trained by [xgboost](https://xgboost.readthedocs.io/en/latest/) to do inference tasks.
New! The MesaTEE GBDT-RS [paper](gbdt.pdf) has been [accepted by IEEE S&P'19](https://www.ieee-security.org/TC/SP2019/program-posters.html)!
# Supported Task
## Supppoted task for both training and inference
1. Linear regression: use SquaredError and LAD loss types
2. Binary classification (labeled with 1 and -1): use LogLikelyhood loss type
## Compatibility with xgboost
At this time, MesaTEE GBDT-RS support to use model trained by xgboost to do inference. The model should be trained by xgboost with following configruation:
1. booster: gbtree
2. objective: "reg:linear", "reg:logistic", "binary:logistic", "binary:logitraw", "multi:softprob", "multi:softmax" or "rank:pairwise".
We have tested that MesaTEE GBDT-RS is compatible with xgboost 0.81 and 0.82
# Quick Start
## Training Steps
1. Set configuration
2. Load training data
3. Train the model
4. (optional) Save the model
## Inference Steps
1. Load the model
2. Load the test data
3. Inference the test data
## Example
``` rust
use gbdt::config::Config;
use gbdt::decision_tree::{DataVec, PredVec};
use gbdt::gradient_boost::GBDT;
use gbdt::input::{InputFormat, load};
let mut cfg = Config::new();
cfg.set_feature_size(22);
cfg.set_max_depth(3);
cfg.set_iterations(50);
cfg.set_shrinkage(0.1);
cfg.set_loss("LogLikelyhood");
cfg.set_debug(true);
cfg.set_data_sample_ratio(1.0);
cfg.set_feature_sample_ratio(1.0);
cfg.set_training_optimization_level(2);
// load data
let train_file = "dataset/agaricus-lepiota/train.txt";
let test_file = "dataset/agaricus-lepiota/test.txt";
let mut input_format = InputFormat::csv_format();
input_format.set_feature_size(22);
input_format.set_label_index(22);
let mut train_dv: DataVec = load(train_file, input_format).expect("failed to load training data");
let test_dv: DataVec = load(test_file, input_format).expect("failed to load test data");
// train and save model
let mut gbdt = GBDT::new(&cfg);
gbdt.fit(&mut train_dv);
gbdt.save_model("gbdt.model").expect("failed to save the model");
// load model and do inference
let model = GBDT::load_model("gbdt.model").expect("failed to load the model");
let predicted: PredVec = model.predict(&test_dv);
```
## Example code
* Linear regression: examples/iris.rs
* Binary classification: examples/agaricus-lepiota.rs
# Use models trained by xgboost
## Steps
1. Use xgboost to train a model
2. Use examples/convert_xgboost.py to convert the model
* Usage: python convert_xgboost.py xgboost_model_path objective output_path
* Note convert_xgboost.py depends on xgboost python libraries. The converted model can be used on machines without xgboost
3. In rust code, call GBDT::load_from_xgboost(model_path, objective) to load the model
4. Do inference
5. (optional) Call GBDT::save_model to save the model to MesaTEE GBDT-RS native format.
## Example code
* "reg:linear": examples/test-xgb-reg-linear.rs
* "reg:logistic": examples/test-xgb-reg-logistic.rs
* "binary:logistic": examples/test-xgb-binary-logistic.rs
* "binary:logitraw": examples/test-xgb-binary-logistic.rs
* "multi:softprob": examples/test-xgb-multi-softprob.rs
* "multi:softmax": examples/test-xgb-multi-softmax.rs
* "rank:pairwise": examples/test-xgb-rank-pairwise.rs
# Multi-threading
## Training:
At this time, training in MesaTEE GBDT-RS is single-threaded.
## Inference:
The related inference functions are single-threaded. But they are thread-safe. We provide an inference example using multi threads in example/test-multithreads.rs
# SGX usage
Because MesaTEE GBDT-RS is written in pure rust, with the help of [rust-sgx-sdk](https://github.com/baidu/rust-sgx-sdk), it can be used in sgx enclave easily as:
```
gbdt_sgx = { git = "https://github.com/mesalock-linux/gbdt-rs" }
```
This would import a crate named `gbdt_sgx`. If you prefer `gbdt` as normal:
```
gbdt = { package = "gbdt_sgx", git = "https://github.com/mesalock-linux/gbdt-rs" }
```
For more information and concret examples, please look at directory `sgx/gbdt-sgx-test`.
# License
Apache 2.0
# Authors
Tianyi Li @n0b0dyCN
Tongxin Li @litongxin1991
Yu Ding @dingelish
# Steering Committee
Tao Wei, Yulong Zhang
# Acknowledgment
Thanks to @qiyiping for his/her great previous work [gbdt](https://github.com/qiyiping/gbdt). We read his/her code before starting this project.