代码拉取完成，页面将自动刷新
We provide two methods for users to start running with XFL: via docker or conda.
Python：3.9
OS：MacOS，Linux（most distributions supported, CentOS 7 in this tutorial）
CPU/Memory：8 cores and 16G memory is recommended as a minimum configuration
Redis
git clone https://github.com/paritybit-ai/XFL
cd XFL
# Get the redis image
docker pull redis:7.0.3
# start a redis server, and make sure that port 6379 is open
docker run --name my_redis -p 6379:6379 -d redis:7.0.3
Run the XFL image as a container
# pull the image
docker pull basebit/xfl:1.2.0
# start with XFL
docker run -it --entrypoint /bin/bash \
--network container:my_redis \
-v $PWD/demo:/opt/app/xfl/demo \
-v /opt/dataset:/opt/dataset \
-v /opt/config:/opt/config \
-v /opt/log:/opt/log \
-v /opt/checkpoints:/opt/checkpoints \
basebit/xfl:1.4.0
Running inside docker contrainer
cd demo/vertical/logistic_regression/2party
sh run.sh
It is recommended to use anaconda to create a virtual environment.
conda create -n xfl python=3.9.7
conda activate xfl
Install redis and other dependencies
# Ubuntu
apt install redis-server
# CentOS
yum install epel-release
yum install redis
# MacOS
brew install redis
brew install coreutils
install python dependencies
# update pip
pip install -U pip
# install dependencies
pip install -r requirements.txt
Running in standalone mode
# set permission
sudo chmod 755 /opt
# enter the project directory
cd demo/vertical/logistic_regression/2party
# activate the virtual environment
conda activate xfl
# start redis-server (skip if it is already run)
redis-server & # for Ubuntu and MacOS
systemctl start redis # for CentOS
# start running the demo
sh run.sh
We provide various examples in the demo directory of the project.
demo
├── horizontal
│ ├── logistic_regression
│ └── resnet
├── local
│ ├── normalization
│ └── standard_scaler
├── vertical
│ ├── binning_woe_iv
│ ├── feature_selection
│ ├── kmeans
│ ├── logistic_regression
│ ├── pearson
│ └── xgboost
└── ...
In each subdirectory, an executable script for demonstration is provided. For example, the following commands run the vertical logistic regression (two parties)
cd demo/vertical/logistic_regression/2party
sh run.sh
\opt directory are required when running with the default configuration. If the permission cannot be obtained, one should modify the configuration under the corresponding subdirectory.JOB_ID will be automatically assigned to the task after it is running. According to this JOB_ID, the output and log files of the task can be obtained.INFO: All Stage Successful. will be printed, indicating that all tasks were executed successfully.A successfully executed vertical logistic regression (two parties) produces the following files:
/opt
├── checkpoints # model path
│ ├── ...
│ └── 4 # [JOB_ID]
│ ├── node-1 # output directory for node-1 (model file included)
│ │ ├── vertical_logistic_regression_guest.pt
│ │ └── ...
│ └── node-2 # output directory for node-2 (model file included)
│ ├── vertical_logistic_regression_host.pt
│ └── ...
└── log
└── 4 # [JOB_ID]
└── xfl.log # log file
After the task is completed, one can clean up residual processes with following script.
# clean up residual processes
sh stop.sh
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