Skip to content

Commit 116eff6

Browse files
zhuangchongzhuangchong
authored
[3.1.2-prepare][version] Modify the version to 3.1.2 (#13121)
1 parent ccc3a61 commit 116eff6

File tree

23 files changed

+350
-279
lines changed

23 files changed

+350
-279
lines changed

Diff for: ‎deploy/docker/.env

+1-1
Original file line numberDiff line numberDiff line change
@@ -16,7 +16,7 @@
1616
# under the License.
1717
#
1818
HUB=apache
19-
TAG=3.1.1
19+
TAG=3.1.2
2020

2121
TZ=Asia/Shanghai
2222
DATABASE=postgresql

Diff for: ‎deploy/kubernetes/dolphinscheduler/Chart.yaml

+2-2
Original file line numberDiff line numberDiff line change
@@ -35,11 +35,11 @@ type: application
3535

3636
# This is the chart version. This version number should be incremented each time you make changes
3737
# to the chart and its templates, including the app version.
38-
version: 3.1.1
38+
version: 3.1.2
3939

4040
# This is the version number of the application being deployed. This version number should be
4141
# incremented each time you make changes to the application.
42-
appVersion: 3.1.1
42+
appVersion: 3.1.2
4343

4444
dependencies:
4545
- name: postgresql

Diff for: ‎deploy/kubernetes/dolphinscheduler/values.yaml

+1-1
Original file line numberDiff line numberDiff line change
@@ -23,7 +23,7 @@ timezone: "Asia/Shanghai"
2323

2424
image:
2525
registry: "dolphinscheduler.docker.scarf.sh/apache"
26-
tag: "3.1.1"
26+
tag: "3.1.2"
2727
pullPolicy: "IfNotPresent"
2828
pullSecret: ""
2929
master: dolphinscheduler-master

Diff for: ‎docs/configs/docsdev.js

+242-242
Large diffs are not rendered by default.

Diff for: ‎docs/configs/site.js

+1-1
Original file line numberDiff line numberDiff line change
@@ -45,7 +45,7 @@ export default {
4545
children: [
4646
{
4747
key: 'docs0',
48-
text: 'latest(3.1.1)',
48+
text: 'latest(3.1.2)',
4949
link: '/en-us/docs/latest/user_doc/about/introduction.html',
5050
},
5151
{

Diff for: ‎docs/docs/en/guide/expansion-reduction.md

+2-2
Original file line numberDiff line numberDiff line change
@@ -30,9 +30,9 @@ Attention: DolphinScheduler itself does not depend on Hadoop, Hive, Spark, but w
3030
mkdir -p /opt
3131
cd /opt
3232
# decompress
33-
tar -zxvf apache-dolphinscheduler-3.1.1-bin.tar.gz -C /opt
33+
tar -zxvf apache-dolphinscheduler-3.1.2-bin.tar.gz -C /opt
3434
cd /opt
35-
mv apache-dolphinscheduler-3.1.1-bin dolphinscheduler
35+
mv apache-dolphinscheduler-3.1.2-bin dolphinscheduler
3636
```
3737

3838
```markdown

Diff for: ‎docs/docs/en/guide/start/docker.md

+4-4
Original file line numberDiff line numberDiff line change
@@ -19,7 +19,7 @@ Start DolphinScheduler with standalone-server Docker images is the easiest way t
1919
you can learn DolphinScheduler's concepts and usage, with minimal cost.
2020

2121
```shell
22-
$ DOLPHINSCHEDULER_VERSION=3.1.1
22+
$ DOLPHINSCHEDULER_VERSION=3.1.2
2323
$ docker run --name dolphinscheduler-standalone-server -p 12345:12345 -p 25333:25333 -d apache/dolphinscheduler-standalone-server:"${DOLPHINSCHEDULER_VERSION}"
2424
```
2525

@@ -49,7 +49,7 @@ After complete the configuration, we can get the `docker-compose.yaml` file from
4949
form its source package, and make sure you get the right version. After download the package, you can run the commands as below.
5050

5151
```shell
52-
$ DOLPHINSCHEDULER_VERSION=3.1.1
52+
$ DOLPHINSCHEDULER_VERSION=3.1.2
5353
$ tar -zxf apache-dolphinscheduler-"${DOLPHINSCHEDULER_VERSION}"-src.tar.gz
5454
# Going to docker-compose's location
5555
# For Mac or Linux users
@@ -72,7 +72,7 @@ $ docker-compose --profile all up -d
7272
container when it up. You could start DolphinScheduler server separately if you want to reuse your exists services.
7373

7474
```shell
75-
$ DOLPHINSCHEDULER_VERSION=3.1.1
75+
$ DOLPHINSCHEDULER_VERSION=3.1.2
7676
# Initialize the database, make sure database <DATABASE> already exists
7777
$ docker run -d --name dolphinscheduler-tools \
7878
-e DATABASE="postgresql" \
@@ -133,5 +133,5 @@ and use `admin` and `dolphinscheduler123` as default username and password in th
133133

134134
You can modify some environment variables to change configurations when you are starting servers through Docker. We have
135135
an example in [using exists PostgreSQL ZooKeeper](#using-exists-postgresql-zookeeper) to change database and ZooKeeper configurations,
136-
and you could find all environment variables in [all environment variables](https://github.com/apache/dolphinscheduler/blob/3.1.1/script/env/dolphinscheduler_env.sh) <!-- markdown-link-check-disable-line -->
136+
and you could find all environment variables in [all environment variables](https://github.com/apache/dolphinscheduler/blob/3.1.2/script/env/dolphinscheduler_env.sh) <!-- markdown-link-check-disable-line -->
137137
and change them if you want.

Diff for: ‎docs/docs/en/guide/task/mlflow.md

+7-7
Original file line numberDiff line numberDiff line change
@@ -61,13 +61,13 @@ The MLflow plugin currently supports and will support the following:
6161

6262
**Task Parameter**
6363

64-
| **Parameter** | **Description** |
65-
|----------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
66-
| Register Model | Register the model or not. If register is selected, the following parameters are expanded. |
67-
| model name | The registered model name is added to the original model version and registered as Production. |
68-
| 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). |
69-
| Parameters | Parameter when initializing the algorithm/AutoML model, which can be empty. For example, parameters `n_estimators=200;learning_rate=0.2` 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()`. The detailed parameter list is as follows: <ul><li>[flaml](https://microsoft.github.io/FLAML/docs/reference/automl#automl-objects)</li><li>[autosklearn](https://automl.github.io/auto-sklearn/master/api.html)</li></ul> |
70-
| AutoML tool | The AutoML tool used, currently supports [autosklearn](https://github.com/automl/auto-sklearn) and [flaml](https://github.com/microsoft/FLAML). |
64+
| **Parameter** | **Description** |
65+
|----------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
66+
| Register Model | Register the model or not. If register is selected, the following parameters are expanded. |
67+
| model name | The registered model name is added to the original model version and registered as Production. |
68+
| 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). |
69+
| Parameters | Parameter when initializing the algorithm/AutoML model, which can be empty. For example, parameters `n_estimators=200;learning_rate=0.2` 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()`. The detailed parameter list is as follows: <ul><li>[flaml](https://microsoft.github.io/FLAML/docs/Use-Cases/Task-Oriented-AutoML)</li><li>[autosklearn](https://automl.github.io/auto-sklearn/master/api.html)</li></ul> |
70+
| AutoML tool | The AutoML tool used, currently supports [autosklearn](https://github.com/automl/auto-sklearn) and [flaml](https://github.com/microsoft/FLAML). |
7171

7272
#### Custom projects
7373

Diff for: ‎docs/docs/en/history-versions.md

+4
Original file line numberDiff line numberDiff line change
@@ -4,6 +4,10 @@
44

55
#### Setup instructions, are available for each stable version of Apache DolphinScheduler below:
66

7+
### Versions: 3.1.2
8+
9+
#### Links： [3.1.2 Document](../3.1.2/user_doc/about/introduction.md)
10+
711
### Versions: 3.1.1
812

913
#### Links： [3.1.1 Document](../3.1.1/user_doc/about/introduction.md)

Diff for: ‎docs/docs/zh/guide/expansion-reduction.md

+2-2
Original file line numberDiff line numberDiff line change
@@ -30,9 +30,9 @@
3030
mkdir -p /opt
3131
cd /opt
3232
# 解压缩
33-
tar -zxvf apache-dolphinscheduler-3.1.1-bin.tar.gz -C /opt
33+
tar -zxvf apache-dolphinscheduler-3.1.2-bin.tar.gz -C /opt
3434
cd /opt
35-
mv apache-dolphinscheduler-3.1.1-bin dolphinscheduler
35+
mv apache-dolphinscheduler-3.1.2-bin dolphinscheduler
3636
```
3737

3838
```markdown

Diff for: ‎docs/docs/zh/guide/start/docker.md

+4-4
Original file line numberDiff line numberDiff line change
@@ -17,7 +17,7 @@
1717
你可以最快速的体验到 DolphinScheduler 的大部分功能，了解主要和概念和内容。
1818

1919
```shell
20-
$ DOLPHINSCHEDULER_VERSION=3.1.1
20+
$ DOLPHINSCHEDULER_VERSION=3.1.2
2121
$ docker run --name dolphinscheduler-standalone-server -p 12345:12345 -p 25333:25333 -d apache/dolphinscheduler-standalone-server:"${DOLPHINSCHEDULER_VERSION}"
2222
```
2323

@@ -43,7 +43,7 @@ $ docker run --name dolphinscheduler-standalone-server -p 12345:12345 -p 25333:2
4343
源码包对应的值为 "Total Source Code"。当下载完源码后就可以运行命令进行部署了。
4444

4545
```shell
46-
$ DOLPHINSCHEDULER_VERSION=3.1.1
46+
$ DOLPHINSCHEDULER_VERSION=3.1.2
4747
$ tar -zxf apache-dolphinscheduler-"${DOLPHINSCHEDULER_VERSION}"-src.tar.gz
4848
# Mac Linux 用户
4949
$ cd apache-dolphinscheduler-"${DOLPHINSCHEDULER_VERSION}"-src/deploy/docker
@@ -65,7 +65,7 @@ $ docker-compose --profile all up -d
6565
ZooKeeper 且不想启动新的服务，可以使用这个方式分别启动 DolphinScheduler 容器。
6666

6767
```shell
68-
$ DOLPHINSCHEDULER_VERSION=3.1.1
68+
$ DOLPHINSCHEDULER_VERSION=3.1.2
6969
# 初始化数据库，其确保数据库 <DATABASE> 已经存在
7070
$ docker run -d --name dolphinscheduler-tools \
7171
-e DATABASE="postgresql" \
@@ -126,4 +126,4 @@ $ docker run -d --name dolphinscheduler-alert-server \
126126
## 环境变量
127127

128128
可以通过环境变量来修改 Docker 运行的配置，我们在沿用已有的 PostgreSQL 和 ZooKeeper 服务中就通过环境变量修改了 Docker 的数据库配置和
129-
注册中心配置，关于全部的配置环境可以查看[全部的配置文件](https://github.com/apache/dolphinscheduler/blob/3.1.1/script/env/dolphinscheduler_env.sh) 了解 <!-- markdown-link-check-disable-line -->
129+
注册中心配置，关于全部的配置环境可以查看[全部的配置文件](https://github.com/apache/dolphinscheduler/blob/3.1.2/script/env/dolphinscheduler_env.sh) 了解 <!-- markdown-link-check-disable-line -->

Diff for: ‎docs/docs/zh/guide/task/mlflow.md

+7-7
Original file line numberDiff line numberDiff line change
@@ -57,13 +57,13 @@ MLflow 组件用于执行 MLflow 任务，目前包含Mlflow Projects，和MLflo
5757

5858
![mlflow-automl](../../../../img/tasks/demo/mlflow-automl.png)
5959

60-
| **任务参数** | **描述** |
61-
|----------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
62-
| 注册模型 | 是否注册模型，若选择注册，则会展开以下参数 |
63-
| 注册的模型名称 | 注册的模型名称，会在原来的基础上加上一个模型版本，并注册为Production |
64-
| 数据路径 | 文件/文件夹的绝对路径，若文件需以.csv结尾（自动切分训练集与测试集），文件夹需包含train.csv和test.csv（建议方式，用户应自行构建测试集用于模型评估） |
65-
| 参数 | 初始化AutoML训练器时的参数，可为空，如针对 flaml 设置`time_budget=30;estimator_list=['lgbm']`。约定传入后会以; 切分各个参数，等号前的名字作为参数名，等号后的名字将以python eval执行得到对应的参数值。详细的参数列表如下: [flaml](https://microsoft.github.io/FLAML/docs/reference/automl#automl-objects)，[autosklearn](https://automl.github.io/auto-sklearn/master/api.html) |
66-
| AutoML工具 | 使用的AutoML工具，目前支持 [autosklearn](https://github.com/automl/auto-sklearn)，[flaml](https://github.com/microsoft/FLAML) |
60+
| **任务参数** | **描述** |
61+
|----------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
62+
| 注册模型 | 是否注册模型，若选择注册，则会展开以下参数 |
63+
| 注册的模型名称 | 注册的模型名称，会在原来的基础上加上一个模型版本，并注册为Production |
64+
| 数据路径 | 文件/文件夹的绝对路径，若文件需以.csv结尾（自动切分训练集与测试集），文件夹需包含train.csv和test.csv（建议方式，用户应自行构建测试集用于模型评估） |
65+
| 参数 | 初始化AutoML训练器时的参数，可为空，如针对 flaml 设置`time_budget=30;estimator_list=['lgbm']`。约定传入后会以; 切分各个参数，等号前的名字作为参数名，等号后的名字将以python eval执行得到对应的参数值。详细的参数列表如下: [flaml](https://microsoft.github.io/FLAML/docs/Use-Cases/Task-Oriented-AutoML)，[autosklearn](https://automl.github.io/auto-sklearn/master/api.html) |
66+
| AutoML工具 | 使用的AutoML工具，目前支持 [autosklearn](https://github.com/automl/auto-sklearn)，[flaml](https://github.com/microsoft/FLAML) |
6767

6868
#### Custom projects
6969

0 commit comments

Comments
 (0)