update example link

Signed-off-by: nemirorox <[email protected]>

CONTRIBUTING.md

@@ -44,7 +44,7 @@ Before sending your pull request, here are some principle and standards to follo
 
 * Include unit tests when you contribute new features. You may created a test folder in your module and put your test files in it. Here is [an example](./federatedml/model_selection/test/).  FATE provides a [test script](./federatedml/test/run_test.sh) for you to check all unit test. Make sure your code work correctly.
 
-* Provide examples in [example folder](./examples). If specific format data is needed, please also provided example data in [data folder](./example/data). In your algorithm examples, some dsl and conf files should be provided and provide a testsuite file with which user can test your example easily. Here is [an example of testsuite file](./examples/federatedml-1.x-examples/hetero_logistic_regression/hetero_lr_testsuite.json)
+* Provide examples in [example folder](./examples). If specific format data is needed, please also provided example data in [data folder](./example/data). In your algorithm examples, some dsl and conf files should be provided and provide a testsuite file with which user can test your example easily. Here is [an example of testsuite file](./examples/dsl/v2/hetero_logistic_regression/hetero_lr_testsuite.json)
 
 * After you contribute a new feature to FATE, FATE team members are (by default) responsible for the maintenance of this feature. This means we must make comparison between the benefit of contribution and the cost of maintaining the feature and accept those features with greater benefits.
 

README.md

@@ -6,7 +6,7 @@
   <img src="./doc/images/FATE_logo.png">
 </div>
 
-[DOC](./doc) | [Quick Start](./examples/federatedml-1.x-examples) | [中文](./README_zh.md)
+[DOC](./doc) | [Quick Start](../examples/dsl/v2) | [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.
 
@@ -64,7 +64,7 @@ All the unittests shall pass if FATE is installed properly.
 
 ### 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)
+We have provided a python script for quick starting modeling task. This scrip is located at ["examples"](./examples/dsl/v2)
 
 ###  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.rst)

README_zh.md

@@ -4,7 +4,7 @@
   <img src="./doc/images/FATE_logo.png">
 </div>
 
-[DOC](./doc) | [Quick Start](./examples/federatedml-1.x-examples) | [English](./README.md)
+[DOC](./doc) | [Quick Start](./examples/dsl/v2) | [English](./README.md)
 
 FATE (Federated AI Technology Enabler) 是微众银行AI部门发起的开源项目，为联邦学习生态系统提供了可靠的安全计算框架。FATE项目使用多方安全计算 (MPC) 以及同态加密 (HE) 技术构建底层安全计算协议，以此支持不同种类的机器学习的安全计算，包括逻辑回归、基于树的算法、深度学习和迁移学习等。
 
@@ -60,7 +60,7 @@ FATE同样为大数据场景提供了分布式运行部署架构版本。从单
 
 ### 快速开始
 
-我们提供了一个用于快速搭建训练任务的python脚本作为示例。该脚本位于：["./examples/federatedml-1.x-examples"](./examples/federatedml-1.x-examples)
+我们提供了一个用于快速搭建训练任务的python脚本作为示例。该脚本位于：["./examples"](./examples/dsl/v2)
 
 ###  获取模型并检查结果
 FATE提供了名为 fate-flow 的工具用来跟踪组件输出模型或日志。fate-flow的部署和使用可以在 [这里](./fate_flow/README.rst) 找到。

doc/develop_guide_zh.rst

@@ -299,4 +299,4 @@ fate_flow_client 模块的运行规则是：
 :3.检查日志文件:
    现在，您可以在以下路径中检查日志：`${your_install_path}/logs/{your jobid}`.
 
-有关 dsl 配置文件和参数配置文件的更多详细信息，请参考此处的`examples/federatedml-1.x-examples`中查看。
+有关 dsl 配置文件和参数配置文件的更多详细信息，请参考此处的`examples/dsl/v2`中查看。

examples/test/README.md

@@ -22,7 +22,7 @@ Optional parameters<br>
 
 2.Tips
 ------
-If '-d' or '-s' is not given,the script will execute the tasks defined in task files from examples/federatedml-1.x-examples folder with a "testsuite.json" suffix.<br>
+If '-d' or '-s' is not given,the script will execute the tasks defined in task files from examples/dsl/v1 folder with a "testsuite.json" suffix.<br>
 If there is a '-d' or '-s' parameter,the script will execute the tasks defined in task files with a "testsuite.json" suffix from the dir given by '-d' or a single task file given by '-s'.
 An example task file is given in examples/test/demo/temp_testsuite.json including a training and a prediction task. <br>
 

examples/test/README_zh.md

@@ -28,7 +28,7 @@ default_env.json环境文件，根据用户实际情况指明所需要的环境
 
 2.执行规则
 ---------
-在没有指定-d或者-s参数的情况下，测试工具执行的任务为examples/federatedml-1.x-examples文件夹中以testsuite.json为后缀的任务文件。<br>
+在没有指定-d或者-s参数的情况下，测试工具执行的任务为examples/dsl/v1文件夹中以testsuite.json为后缀的任务文件。<br>
 指定-d或者-s参数后，测试工具执行的任务-d指定的目录下以testsuite.json为后缀的任务文件或者-s指定的单一任务文件<br>
 一个testsuite.json任务文件样例已经给出。<br>
 examples/test/demo/temp_testsuite.json<br>

index.rst

@@ -17,7 +17,7 @@ https://fate.fedai.org
    :caption: Quick Start
    :maxdepth: 3
 
-   Quick Start <examples/federatedml-1.x-examples/README>
+   Quick Start <examples/dsl/v2/README>
    Data Upload <doc/upload_data_guide>
    Configuration <doc/dsl_conf_setting_guide>
 

python/fate_flow/README_zh.rst

@@ -283,7 +283,7 @@ API <./doc/fate_flow_rest_api.rst>`__\ 和\ `命令行界面 <./doc/fate_flow_cl
 
     您可以在\ `CLI <./doc/fate_flow_cli.rst>`__ 中找到更多有用的命令。
 
-有关更多联邦学习Pipeline作业(job) 示例, 请参考\ `federatedml-1.x-examples <./../examples/federatedml-1.x-examples>`__ 和 `README <./../examples/federatedml-1.x-examples/README.rst>`__
+有关更多联邦学习Pipeline作业(job) 示例, 请参考\ `examples <./../../examples/dsl/v2>`__ 和 `README <./../../examples/dsl/v2/README.rst>`__
 
 在线推理
 ~~~~~~~~

python/federatedml/linear_model/linear_regression/README.rst

@@ -1,7 +1,7 @@
 Federated Linear Regression
 ===========================
 
-Linear Regression(LinR) is a simple statistic model widely used for predicting continuous numbers. FATE provides Heterogeneous Linear Regression(HeteroLinR). HeteroLinR also supports multi-Host training. You can specify multiple hosts in the job configuration file like the provided `examples/federatedml-1.x-examples/hetero_linear_regression`.
+Linear Regression(LinR) is a simple statistic model widely used for predicting continuous numbers. FATE provides Heterogeneous Linear Regression(HeteroLinR). HeteroLinR also supports multi-Host training. You can specify multiple hosts in the job configuration file like the provided `examples/dsl/v2/hetero_linear_regression`.
 
 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.
 

python/federatedml/linear_model/poisson_regression/README.rst

@@ -1,7 +1,7 @@
 Federated Poisson Regression
 ============================
 
-Poisson distribution is a convenient model for modeling occurrences within a certain time period or geographical area. It is commonly used for predicting rates of low-frequency events. FATE provides Heterogeneous Poisson Regression(HeteroPoisson). The module can accept data with exposure variable, whose column name may be specified in job configuration file. Please refer to the `examples/federatedml-1.x-examples/hetero_poisson_regression` on how to specify exposure element in job configuration.
+Poisson distribution is a convenient model for modeling occurrences within a certain time period or geographical area. It is commonly used for predicting rates of low-frequency events. FATE provides Heterogeneous Poisson Regression(HeteroPoisson). The module can accept data with exposure variable, whose column name may be specified in job configuration file. Please refer to the `examples/dsl/v1/hetero_poisson_regression` on how to specify exposure element in job configuration.
 
 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.
 

python/federatedml/local_baseline/README.rst

@@ -19,7 +19,7 @@ In addition, note that loss history is not available for Guest when running homo
 
 Currently local baseline does not support predict task from history job. This feature will be added in future updates.
 
-For examples of using Local Baseline module, please refer `examples/federatedml-1.x-examples/local_baseline`.
+For examples of using Local Baseline module, please refer `examples/dsl/v2/local_baseline`.
 
 Param
 ------

python/federatedml/model_selection/data_split/README.rst

@@ -28,4 +28,4 @@ specified. The module takes either int (instance count) or float
 (fraction) value for set sizes, but mixed-type inputs cannot be used.
 
 For examples of using Data Split module, please refer
-`here <../../examples/federatedml-1.x-examples/data_split>`__.
+`here <../../../examples/dsl/v2/data_split>`__.

python/federatedml/model_selection/stepwise/README.rst

@@ -30,7 +30,7 @@ To use stepwise, set 'need_stepwise' to `True` and specify stepwise parameters a
 
  
 
-For examples of using stepwise with linear models, please refer to `examples/federatedml-1.x-examples/hetero_stepwise`. For explanation on each stepwise module parameter, please refer to the comments in stepwise param :download:`stepwise_param.py <../../param/stepwise_param.py>`.
+For examples of using stepwise with linear models, please refer to `examples/dsl/v1/hetero_stepwise`. For explanation on each stepwise module parameter, please refer to the comments in stepwise param :download:`stepwise_param.py <../../param/stepwise_param.py>`.
 
 Please note that on FATE Board, the model information (max iters & coefficient/intercept values) represents the final result model. 
 

python/federatedml/nn/homo_nn/README.rst

@@ -267,7 +267,7 @@ Use
 ------
 
 .. Note::
-  For more information on task configuration, please refer to the `[doc] <../../examples/federatedml-1.x-examples/README.rst>`_ under example first. In this part we only talk about the parameter configuration.
+  For more information on task configuration, please refer to the `[doc] <../../../examples/dsl/v1/README.rst>`_ under example first. In this part we only talk about the parameter configuration.
 
 Since all parties training Homogeneous Neural Networks have the same network structure, a common practice is to configure parameters under algorithm_parameters, which is shared across all parties. The basic structure is:
 

python/federatedml/statistic/correlation/README.rst

@@ -49,4 +49,4 @@ How to Use
     	if both params are provided, the union of columns indicated are used for calculation.  
 
 :examples:
-    There is an example :download:`[conf] <../../../examples/federatedml-1.x-examples/hetero_pearson/test_conf.json>` and :download:`[dsl] <../../../examples/federatedml-1.x-examples/hetero_pearson/test_conf.json>`
+    There is an example :download:`[conf] <../../../../examples/dsl/v2/hetero_pearson/test_conf.json>` and :download:`[dsl] <../../../../examples/dsl/v2/hetero_pearson/test_conf.json>`

python/federatedml/statistic/psi/README.rst

@@ -32,4 +32,4 @@ Param
 How to use
 ^^^^^^^^^^
 
-An example is offered in examples/federatedml-1.x-examples/psi folder.
+An example is offered in examples/dsl/v2/psi folder.

python/federatedml/statistic/union/README.rst

@@ -37,7 +37,7 @@ If an id `42` exists in both `dataio_0.data` and `dataio_1.data`, and:
 2.) 'keep_duplicate` set to true: the value from `dataio_0.data` and the one from `dataio_1.data` are both kept; the value from `dataio_1.data` will have a new id `42_dataio_1` in the result table.
 
 
-For more example job configuration and dsl setting files, please refer `examples/federatedml-1.x-examples/union`.
+For more example job configuration and dsl setting files, please refer `examples/dsl/v2/union`.
 
 Param
 ------

standalone-deploy/README.md

@@ -62,7 +62,7 @@ bash install_standalone_docker.sh
 
 
 
-There are a few algorithms under [examples](../examples/federatedml-1.x-examples) folder, try them out!
+There are a few algorithms under [examples](../examples/dsl/v2) folder, try them out!
 
 You can also experience the fateboard access via a browser:
 Http://hostip:8080.
@@ -123,7 +123,7 @@ Http://hostip:8080.
 
 
 
-There are a few algorithms under [examples](../examples/federatedml-1.x-examples) folder, try them out!
+There are a few algorithms under [examples](../examples/dsl/v2) folder, try them out!
 
 You can also experience the fateboard access via a browser:
 Http://hostip:8080.

standalone-deploy/README.rst

@@ -75,7 +75,7 @@ possibility of encountering problems.
       success to calculate secure_sum, it is 2000.0
 
 There are a few algorithms under
-`examples <../examples/federatedml-1.x-examples>`__ folder, try them
+`examples <../examples/dsl/v2>`__ folder, try them
 out!
 
 You can also experience the fateboard access via a browser:
@@ -137,7 +137,7 @@ Http://hostip:8080.
         success to calculate secure_sum, it is 2000.0
 
 There are a few algorithms under
-`examples <../examples/federatedml-1.x-examples>`__ folder, try them out!
+`examples <../../examples/dsl/v2>`__ folder, try them out!
 
 You can also experience the fateboard access via a browser:
 Http://hostip:8080.

standalone-deploy/doc/Fate-standalone_deployment_guide_zh.md

@@ -61,7 +61,7 @@
 
 
 
-有些用例算法在 [examples](../../examples/federatedml-1.x-examples) 文件夹下, 请尝试使用。
+有些用例算法在 [examples](../../examples/dsl/v2) 文件夹下, 请尝试使用。
 
 您还可以通过浏览器体验算法过程看板，访问：Http://hostip:8080。
 
@@ -121,6 +121,6 @@
 
 
 
-有些用例算法在 [examples](../../examples/federatedml-1.x-examples) 文件夹下, 请尝试使用。
+有些用例算法在 [examples](../../examples/dsl/v2) 文件夹下, 请尝试使用。
 
 您还可以通过浏览器体验算法过程看板，访问：Http://hostip:8080。