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[Chore] Some release improvement (#17107)
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‎docs/docs/en/contribute/backend/spi/alert.md‎

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AbsPluginParams This class is the base class of all parameters, RadioParam these classes all inherit this class. Each DS alert plug-in will return a list of AbsPluginParams in the implementation of AlertChannelFactory.
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The specific design of alert_spi can be seen in the issue: [Alert Plugin Design](https://github.com/apache/incubator-dolphinscheduler/issues/3049)
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The specific design of alert_spi can be seen in the issue: [Alert Plugin Design](https://github.com/apache/dolphinscheduler/issues/3049)
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#### Alert SPI built-in implementation
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‎docs/docs/en/contribute/release.md‎

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- `deploy/kubernetes/dolphinscheduler`:
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- `Chart.yaml`: `appVersion` and `version` needs to be updated to x.y.z
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- `values.yaml`: `image.tag` needs to be updated to x.y.z
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- `config`
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- `install-plugins.sh`: `dev-SNAPSHOT` needs to be updated to x.y.z
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- Version in the docs:
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- Change the placeholder `<version>`(except `pom`) to the `x.y.z` in directory `docs`
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- Add new history version

‎docs/docs/en/guide/task/dinky.md‎

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## Overview
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Use `Dinky Task` to create a dinky-type task and support one-stop development, debugging, operation and maintenance of FlinkSql, Flink jar and SQL. When the worker executes `Dinky Task`,
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it will call `Dinky API` to trigger dinky task. Click [here](http://www.dlink.top/) for details about `Dinky`.
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it will call `Dinky API` to trigger dinky task. Click [here](https://www.dinky.org.cn/) for details about `Dinky`.
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## Create Task
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‎docs/docs/en/guide/task/mlflow.md‎

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**Task Parameters**
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| **Parameter** | **Description** |
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|------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
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| Register Model | Register the model or not. If register is selected, the following parameters are expanded. |
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| Model Name | The registered model name is added to the original model version and registered as Production. |
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| Data Path | The absolute path of the file or folder. Ends with .csv for file or contain train.csv and test.csv for folder（In the suggested way, users should build their own test sets for model evaluation. |
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| Parameters | Parameter when initializing the algorithm/AutoML model, which can be empty. For example, parameters `"time_budget=30;estimator_list=['lgbm']"` for flaml. The convention will be passed with '; ' shards each parameter, using the name before the equal sign as the parameter name, and using the name after the equal sign to get the corresponding parameter value through `python eval()`. <ul><li>[Logistic Regression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html#sklearn.linear_model.LogisticRegression)</li><li>[SVM](https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html?highlight=svc#sklearn.svm.SVC)</li><li>[lightgbm](https://lightgbm.readthedocs.io/en/latest/pythonapi/lightgbm.LGBMClassifier.html#lightgbm.LGBMClassifier)</li><li>[xgboost](https://xgboost.readthedocs.io/en/stable/python/python_api.html#xgboost.XGBClassifier)</li></ul> |
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| Algorithm | The selected algorithm currently supports `LR`, `SVM`, `LightGBM` and `XGboost` based on [scikit-learn](https://scikit-learn.org/) form. |
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| Parameter Search Space | Parameter search space when running the corresponding algorithm, which can be empty. For example, the parameter `max_depth=[5, 10];n_estimators=[100, 200]` for lightgbm. The convention will be passed with '; 'shards each parameter, using the name before the equal sign as the parameter name, and using the name after the equal sign to get the corresponding parameter value through `python eval()`. |
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| **Parameter** | **Description** |
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|------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
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| Register Model | Register the model or not. If register is selected, the following parameters are expanded. |
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| Model Name | The registered model name is added to the original model version and registered as Production. |
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| Data Path | The absolute path of the file or folder. Ends with .csv for file or contain train.csv and test.csv for folder（In the suggested way, users should build their own test sets for model evaluation. |
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| Parameters | Parameter when initializing the algorithm/AutoML model, which can be empty. For example, parameters `"time_budget=30;estimator_list=['lgbm']"` for flaml. The convention will be passed with '; ' shards each parameter, using the name before the equal sign as the parameter name, and using the name after the equal sign to get the corresponding parameter value through `python eval()`. <ul><li>[Logistic Regression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html#sklearn.linear_model.LogisticRegression)</li><li>[SVM](https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html?highlight=svc#sklearn.svm.SVC)</li><li>[lightgbm](https://lightgbm.readthedocs.io/en/latest/pythonapi/lightgbm.LGBMClassifier.html#lightgbm.LGBMClassifier)</li><li>[xgboost](https://xgboost.readthedocs.io/en/release_3.0.0/python/python_api.html#xgboost.XGBClassifier)</li></ul> |
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| Algorithm | The selected algorithm currently supports `LR`, `SVM`, `LightGBM` and `XGboost` based on [scikit-learn](https://scikit-learn.org/) form. |
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| Parameter Search Space | Parameter search space when running the corresponding algorithm, which can be empty. For example, the parameter `max_depth=[5, 10];n_estimators=[100, 200]` for lightgbm. The convention will be passed with '; 'shards each parameter, using the name before the equal sign as the parameter name, and using the name after the equal sign to get the corresponding parameter value through `python eval()`. |
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#### AutoML
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‎docs/docs/zh/contribute/backend/spi/alert.md‎

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AbsPluginParams 该类是所有参数的基类，RadioParam 这些类都继承了该类。每个 DS 的告警插件都会在 AlertChannelFactory 的实现中返回一个 AbsPluginParams 的 list。
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alert_spi 具体设计可见 issue：[Alert Plugin Design](https://github.com/apache/incubator-dolphinscheduler/issues/3049)
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alert_spi 具体设计可见 issue：[Alert Plugin Design](https://github.com/apache/dolphinscheduler/issues/3049)
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#### Alert SPI 内置实现
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‎docs/docs/zh/contribute/release.md‎

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- `deploy/kubernetes/dolphinscheduler`:
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- `Chart.yaml`: `appVersion` 和 `version` 版本更新为 x.y.z
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- `values.yaml`: `image.tag` 版本更新为 x.y.z
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- `config`
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- `install-plugins.sh`: `version` 版本 `dev-SNAPSHOT` 更新为 x.y.z
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- 修改文档（docs 模块）中的版本号:
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- 将 `docs` 文件夹下文件的占位符 `<version>` (除了 pom.xml 相关的) 修改成 `x.y.z`
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- 新增历史版本

‎docs/docs/zh/guide/task/dinky.md‎

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## Overview
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`Dinky`任务类型，用于创建并执行`Dinky`类型任务以支撑一站式的开发、调试、运维 FlinkSQL、Flink Jar、SQL。worker 执行该任务的时候，会通过`Dinky API`触发`Dinky 的作业`。
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点击[这里](http://www.dlink.top/) 获取更多关于`Dinky`的信息。
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点击[这里](https://www.dinky.org.cn/) 获取更多关于`Dinky`的信息。
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## Create Task
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