In addition to creating, deleting, modifying and quering, we also locate the corresponding resource through url, and then append operations to it after the path, such as:
In addition to creating, deleting, modifying and querying, we also locate the corresponding resource through url, and then append operations to it after the path, such as:
@@ -119,7 +119,7 @@ The PMC member should add the new committer to the official committer list throu
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@@ -119,7 +119,7 @@ The PMC member should add the new committer to the official committer list throu
1. Go to [Apache Account Utility Platform](https://id.apache.org/), create your password, set up your personal mailbox (`Forwarding email address`) and GitHub account(`Your GitHub Username`). An organizational invite will be sent to you via email shortly thereafter (within 2 hours).
1. Go to [Apache Account Utility Platform](https://id.apache.org/), create your password, set up your personal mailbox (`Forwarding email address`) and GitHub account(`Your GitHub Username`). An organizational invite will be sent to you via email shortly thereafter (within 2 hours).
2. If you would like to use the `xxx@apache.org` email service, please refer to [here](https://infra.apache.org/committer-email.html). Gmail is recommended, because this forwarding mode is not easy to find in most mailbox service settings.
2. If you would like to use the `xxx@apache.org` email service, please refer to [here](https://infra.apache.org/committer-email.html). Gmail is recommended, because this forwarding mode is not easy to find in most mailbox service settings.
3. Follow the `Authorized GitHub 2FA wiki` to enable two-factor authorization (2FA) on [Github](http://github.com/). When you set 2FA to "off", it will be delisted by the corresponding Apache committer write permission group until you set it up again. (**NOTE: Treat your recovery codes with the same level of attention as you would your password!**)
3. Follow the `Authorized GitHub 2FA wiki` to enable two-factor authorization (2FA) on [GitHub](http://github.com/). When you set 2FA to "off", it will be delisted by the corresponding Apache committer write permission group until you set it up again. (**NOTE: Treat your recovery codes with the same level of attention as you would your password!**)
4. Use [GitBox Account Linking Utility](https://gitbox.apache.org/setup/) to obtain write permission of the Dolphinscheduler project.
4. Use [GitBox Account Linking Utility](https://gitbox.apache.org/setup/) to obtain write permission of the Dolphinscheduler project.
If you would like to show up publicly in the Apache GitHub org, you need to go to the [Apache GitHub org people page](https://github.com/orgs/apache/people),
If you would like to show up publicly in the Apache GitHub org, you need to go to the [Apache GitHub org people page](https://github.com/orgs/apache/people),
@@ -498,7 +498,7 @@ Default is postgresql driver because of license problem.
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@@ -498,7 +498,7 @@ Default is postgresql driver because of license problem.
A: 1, Where is the executed server? Specify one worker to run the task, you can create worker group in Security Center, then the task can be send to the particular worker. If a worker group have multiple servers, which server actually execute is determined by scheduling and has randomness.
A: 1, Where is the executed server? Specify one worker to run the task, you can create worker group in Security Center, then the task can be send to the particular worker. If a worker group have multiple servers, which server actually execute is determined by scheduling and has randomness.
2, If it is a shell file of a path on the server, how to point to the path? The server shell file, involving permissions issues, it is not recommended to do so. It is recommended that you use the storage function of the resource center, and then use the resource reference in the shell editor. The system will help you download the script to the execution directory. If the task dependent on resource center files, worker use "hdfs dfs -get" to get the resource files in HDFS, then run the task in /tmp/escheduler/exec/process, this path can be customized when installtion dolphinscheduler.
2, If it is a shell file of a path on the server, how to point to the path? The server shell file, involving permissions issues, it is not recommended to do so. It is recommended that you use the storage function of the resource center, and then use the resource reference in the shell editor. The system will help you download the script to the execution directory. If the task dependent on resource center files, worker use "hdfs dfs -get" to get the resource files in HDFS, then run the task in /tmp/escheduler/exec/process, this path can be customized when installing dolphinscheduler.
3, Which user execute the task? Task is run by the tenant through "sudo -u ${tenant}", tenant is a linux user.
3, Which user execute the task? Task is run by the tenant through "sudo -u ${tenant}", tenant is a linux user.
@@ -81,7 +81,7 @@ The MLflow plugin currently supports and will support the following:
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@@ -81,7 +81,7 @@ The MLflow plugin currently supports and will support the following:
| Repository | Repository url of MLflow Project, Support git address and directory on worker. If it's in a subdirectory, We add `#` to support this (same as `mlflow run`) , for example `https://github.com/mlflow/mlflow#examples/xgboost/xgboost_native`. |
| Repository | Repository url of MLflow Project, Support git address and directory on worker. If it's in a subdirectory, We add `#` to support this (same as `mlflow run`) , for example `https://github.com/mlflow/mlflow#examples/xgboost/xgboost_native`. |
| Project Version | Version of the project, default master. |
| Project Version | Version of the project, default master. |
You can now use this feature to run all MLFlow projects on Github (For example [MLflow examples](https://github.com/mlflow/mlflow/tree/master/examples) ). You can also create your own machine learning library to reuse your work, and then use DolphinScheduler to use your library with one click.
You can now use this feature to run all MLFlow projects on GitHub (For example [MLflow examples](https://github.com/mlflow/mlflow/tree/master/examples) ). You can also create your own machine learning library to reuse your work, and then use DolphinScheduler to use your library with one click.
