FL DrugDiscovery Deployment Guide

We deployed a model for predicting drug solubility on FATE

Quick Start

1. Install FATE

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

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 .

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.

2. Deploy FL_DrugDiscovery Model

Deploy model

$ cd /${FATE path}    
$ git https://github.com/chengziqiang/FL_DrugDiscovery
$ sh ./deploy.sh

Upload data and train model

$ cd /${FATE path}/examples/federatedml-1.x-examples/FL_DrugDiscovery
$ sh ./upload_data.sh
$ python /${FATE path}/fate_flow/fate_flow_client.py -f submit_job -d dsl.json -c runtime.json

you can check running status of model at FATEboard that is a suite of visualization tool of FATE

Test model

model saved in /fate/model/${job ID}/