# FATE
**Repository Path**: cjv/FATE
## Basic Information
- **Project Name**: FATE
- **Description**: An Industrial Level Federated Learning Framework
- **Primary Language**: Unknown
- **License**: Apache-2.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No
## Statistics
- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-02-15
- **Last Updated**: 2020-12-19
## Categories & Tags
**Categories**: Uncategorized
**Tags**: None
## README
[](https://opensource.org/licenses/Apache-2.0) [](https://checkstyle.sourceforge.io/google_style.html) [](https://github.com/mmyjona/FATE-Serving/pulls) [](https://checkstyle.sourceforge.io/google_style.html)
[DOC](./doc) | [Quick Start](./examples/federatedml-1.x-examples) | [中文](./README_zh.md)
FATE (Federated AI Technology Enabler) is an open-source project initiated by Webank's AI Department to provide a secure computing framework to support the federated AI ecosystem. It implements secure computation protocols based on homomorphic encryption and multi-party computation (MPC). It supports federated learning architectures and secure computation of various machine learning algorithms, including logistic regression, tree-based algorithms, deep learning and transfer learning.
## Getting Involved
* Join our maillist [Fate-FedAI Group IO](https://groups.io/g/Fate-FedAI). You can ask questions and participate in the development discussion.
* For any frequently asked questions, you can check in [FAQ](https://github.com/FederatedAI/FATE/wiki).
* Please report bugs by submitting [issues](https://github.com/FederatedAI/FATE/issues).
* Submit contributions using [pull requests](https://github.com/FederatedAI/FATE/pulls)
## Federated Learning Algorithms In FATE
FATE already supports a number of federated learning algorithms, including vertical federated learning, horizontal federated learning, and federated transfer learning. More details are available in [federatedml](./federatedml).
## Install
FATE can be installed on Linux or Mac. Now, FATE can support：
* Native installation: standalone and cluster deployments;
* KubeFATE installation:
- Multipal parties deployment by docker-compose, which for devolopment and test purpose;
- Cluster (multi-node) deployment by Kubernetes
### Native installation:
Software environment :jdk1.8+、Python3.6、python virtualenv、mysql5.6+、redis-5.0.2
##### Standalone
FATE provides Standalone runtime architecture for developers. It can help developers quickly test FATE. Standalone support two types of deployment: Docker version and Manual version. Please refer to Standalone deployment guide: [standalone-deploy](./standalone-deploy/)
##### Cluster
FATE also provides a distributed runtime architecture for Big Data scenario. Migration from standalone to cluster requires configuration change only. No algorithm change is needed.
To deploy FATE on a cluster, please refer to cluster deployment guide: [cluster-deploy](./cluster-deploy).
##### Get source
```shell
git clone --recursive git@github.com:FederatedAI/FATE.git
```
### KubeFATE installation:
Using KubeFATE, FATE can be deployed by either docker-compose or Kubernetes:
* For development or testing purposes, docker-compose is recommended. It only requires Docker enviroment. For more detail, please refer to [Deployment by Docker Compose](https://github.com/FederatedAI/KubeFATE/tree/master/docker-deploy).
* For a production or a large scale deployment, Kubernetes is recommended as an underlying infrastructure to manage FATE system. For more detail, please refer to [Deployment on Kubernetes](https://github.com/FederatedAI/KubeFATE/blob/master/k8s-deploy).
More instructions can be found in [KubeFATE](https://github.com/FederatedAI/KubeFATE).
## Running Tests
A script to run all the unittests has been provided in ./federatedml/test folder.
Once FATE is installed, tests can be run using:
> sh ./federatedml/test/run_test.sh
All the unittests shall pass if FATE is installed properly.
## Example Programs
### Quick Start
We have provided a python script for quick starting modeling task. This scrip is located at ["examples/federatedml-1.x-examples"](./examples/federatedml-1.x-examples)
#### Standalone Version
1. Start standalone version hetero-lr task (default)
> python quick_run.py
#### Cluster Version
1. Host party:
> python quick_run.py -r host
This is just uploading data
2. Guest party:
> python quick_run.py -r guest
The config files that generated is stored in a new created folder named **user_config**
#### Start a Predict Task
Once you finish one training task, you can start a predict task. You need to modify "TASK" variable in quick_run.py script as "predict":
```
# Define what type of task it is
# TASK = 'train'
TASK = 'predict'
```
Then all you need to do is running the following command:
> python quick_run.py
Please note this works only if you have finished the trainning task.
### Obtain Model and Check Out Results
We provided functions such as tracking component output models or logs etc. through a tool called fate-flow. The deployment and usage of fate-flow can be found [here](./fate_flow/README.md)
## Doc
### API doc
FATE provides some API documents in [doc-api](./doc/api/), including federatedml, eggroll, federation.
### Develop Guide doc
How to develop your federated learning algorithm using FATE? you can see FATE develop guide document in [develop-guide](./doc/develop_guide.md)
### Other doc
FATE also provides many other documents in [doc](./doc/). These documents can help you understand FATE better.
### License
[Apache License 2.0](LICENSE)