doc update

README.md

@@ -25,7 +25,7 @@ FATE is an open source project hosted by Linux Foundation. The [Technical Charte
 ### Version < 2.0
 Releases history can be found in [releases](https://github.com/FederatedAI/FATE/releases), deployment resources can be found on [wiki](https://github.com/FederatedAI/FATE/wiki/Download)
 
-### Version == 2.0.0-beta
+### Version == 2.0.0
 #### Standalone deployment
 - Deploying FATE on a single node via PyPI, pre-built docker images or installers. It is for simple testing purposes. Refer to this [guide](./deploy/standalone-deploy/).
 
@@ -35,7 +35,7 @@ Deploying FATE to multiple nodes to achieve scalability, reliability and managea
 - [Cluster deployment by CLI](./deploy/cluster-deploy): Using CLI to deploy a FATE cluster.
 
 ### Quick Start
-- [Training Demo With Installing FATE AND FATE-Flow From Pypi](doc/2.0/quick_start.md)
+- [Training Demo With Installing FATE AND FATE-Flow From Pypi](doc/2.0/fate/quick_start.md)
 - [Training Demo With Installing FATE Only From Pypi](doc/2.0/fate/ml)
 
 ## Related Repositories (Projects)

README_zh.md

@@ -23,15 +23,15 @@ FATE于2019年2月首次对外开源，并成立
 ### 2.0以前的版本
 FATE 2.0以前的版本在[发布页](https://github.com/FederatedAI/FATE/releases), 下载资源汇总页在[wiki](https://github.com/FederatedAI/FATE/wiki/Download)
 
-### 2.0.0-beta 版本
+### 2.0.0 版本
 #### 单机版部署
 在单节点上部署FATE单机版，支持从 PyPI 直接安装，docker，主机安装包三种方式。
 - [单机版部署教程](./deploy/standalone-deploy)
 #### 集群
 - [原生集群安装](./deploy/cluster-deploy): Using CLI to deploy a FATE cluster.
 
 ### 快速开始
-- [从 PyPI 下载安装 FATE 和 FATE-Flow 并启动训练任务示例](doc/2.0/quick_start.md)
+- [从 PyPI 下载安装 FATE 和 FATE-Flow 并启动训练任务示例](doc/2.0/fate/quick_start.md)
 - [从 PyPI 下载安装 FATE，并启动训练任务示例](doc/2.0/fate/ml)
 
 ## 关联仓库

doc/2.0/fate/components/linear_regression.md

@@ -19,7 +19,7 @@ Here we simplify participants of the federation process into three
 parties. Party A represents Guest, party B represents Host. Party C,
 which is also known as “Arbiter,” is a third party that works as
 coordinator. Party C is responsible for generating private and public
-keys.
+keys. (Security of this algorithm is lower than SSHE-LinR, use SSHE-LinR if possible)
 
 The process of HeteroLinR training is shown below:
 

doc/2.0/fate/components/logistic_regression.md

@@ -26,7 +26,7 @@ samples stored in databases of the two involved parties. The federated
 model is built based on those overlapping samples. The whole sample
 alignment process will **not** leak confidential information (e.g.,
 sample ids) on the two parties since it is conducted in an encrypted
-way.
+way. (Security of this algorithm is lower than SSHE-LR, use SSHE-LR if possible)
 
 ![Figure 1 (Federated HeteroLR Principle)](../../images/HeteroLR.png)
 

doc/2.0/fate/quick_start.md

@@ -0,0 +1,114 @@
+## Quick Start
+
+1. install `fate_client` with extra package `fate`  
+
+```sh
+python -m pip install -U pip && python -m pip install fate_client[fate,fate_flow]==2.0.0
+```
+after installing packages successfully, initialize fate_flow service and fate_client
+
+```sh
+mkdir fate_workspace
+fate_flow init --ip 127.0.0.1 --port 9380 --home $(pwd)/fate_workspace
+pipeline init --ip 127.0.0.1 --port 9380
+
+fate_flow start
+fate_flow status # make sure fate_flow service is started
+```
+
+
+2.  download example data
+
+```sh
+wget https://raw.githubusercontent.com/wiki/FederatedAI/FATE/example/data/breast_hetero_guest.csv && \
+wget https://raw.githubusercontent.com/wiki/FederatedAI/FATE/example/data/breast_hetero_host.csv
+```
+
+3. transform example data to dataframe using in fate
+```python
+import os
+from fate_client.pipeline import FateFlowPipeline
+
+
+base_path = os.path.abspath(os.path.join(__file__, os.path.pardir))
+guest_data_path = os.path.join(base_path, "breast_hetero_guest.csv")
+host_data_path = os.path.join(base_path, "breast_hetero_host.csv")
+
+data_pipeline = FateFlowPipeline().set_parties(local="0")
+guest_meta = {
+    "delimiter": ",", "dtype": "float64", "label_type": "int64","label_name": "y", "match_id_name": "id"
+}
+host_meta = {
+    "delimiter": ",", "input_format": "dense", "match_id_name": "id"
+}
+data_pipeline.transform_local_file_to_dataframe(file=guest_data_path, namespace="experiment", name="breast_hetero_guest",
+                                                meta=guest_meta, head=True, extend_sid=True)
+data_pipeline.transform_local_file_to_dataframe(file=host_data_path, namespace="experiment", name="breast_hetero_host",
+                                                meta=host_meta, head=True, extend_sid=True)
+```
+4. run example 
+
+```python
+from fate_client.pipeline.components.fate import (
+    HeteroSecureBoost,
+    Reader,
+    PSI,
+    Evaluation
+)
+from fate_client.pipeline import FateFlowPipeline
+
+
+# create pipeline for training
+pipeline = FateFlowPipeline().set_parties(guest="9999", host="10000")
+
+# create reader task_desc
+reader_0 = Reader("reader_0")
+reader_0.guest.task_parameters(namespace="experiment", name="breast_hetero_guest")
+reader_0.hosts[0].task_parameters(namespace="experiment", name="breast_hetero_host")
+
+# create psi component_desc
+psi_0 = PSI("psi_0", input_data=reader_0.outputs["output_data"])
+
+# create hetero secure_boost component_desc
+hetero_secureboost_0 = HeteroSecureBoost(
+    "hetero_secureboost_0", num_trees=1, max_depth=5,
+    train_data=psi_0.outputs["output_data"],
+    validate_data=psi_0.outputs["output_data"]
+)
+
+# create evaluation component_desc
+evaluation_0 = Evaluation(
+    'evaluation_0', runtime_parties=dict(guest="9999"), metrics=["auc"], input_data=[hetero_secureboost_0.outputs["train_data_output"]]
+)
+
+# add training task
+pipeline.add_tasks([reader_0, psi_0, hetero_secureboost_0, evaluation_0])
+
+# compile and train
+pipeline.compile()
+pipeline.fit()
+
+# print metric and model info
+print (pipeline.get_task_info("hetero_secureboost_0").get_output_model())
+print (pipeline.get_task_info("evaluation_0").get_output_metric())
+
+# deploy task for inference
+pipeline.deploy([psi_0, hetero_secureboost_0])
+
+# create pipeline for predicting
+predict_pipeline = FateFlowPipeline()
+
+# add input to deployed_pipeline
+deployed_pipeline = pipeline.get_deployed_pipeline()
+reader_1 = Reader("reader_1")
+reader_1.guest.task_parameters(namespace="experiment", name="breast_hetero_guest")
+reader_1.hosts[0].task_parameters(namespace="experiment", name="breast_hetero_host")
+deployed_pipeline.psi_0.input_data = reader_1.outputs["output_data"]
+
+# add task to predict pipeline
+predict_pipeline.add_tasks([reader_1, deployed_pipeline])
+
+# compile and predict
+predict_pipeline.compile()
+predict_pipeline.predict()
+```