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  1. 86
      docs/docs/en/guide/task/jupyter.md
  2. 48
      docs/docs/en/guide/task/kubernetes.md
  3. 156
      docs/docs/en/guide/task/mlflow.md
  4. 65
      docs/docs/en/guide/task/openmldb.md
  5. 40
      docs/docs/en/guide/task/zeppelin.md

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# Jupyter
## Overview
Use `Jupyter Task` to create a jupyter-type task and execute jupyter notes. When the worker executes `Jupyter Task`,
it will use `papermill` to evaluate jupyter notes. Click [here](https://papermill.readthedocs.io/en/latest/) for details about `papermill`.
## Conda Configuration
- Config `conda.path` in `common.properties` to the path of your `conda.sh`, which should be the same `conda` you use to manage the python environment of your `papermill` and `jupyter`.
Click [here](https://docs.conda.io/en/latest/) for more information about `conda`.
- `conda.path` is set to `/opt/anaconda3/etc/profile.d/conda.sh` by default. If you have no idea where your `conda` is, simply run `conda info | grep -i 'base environment'`.
> NOTE: `Jupyter Task Plugin` uses `source` command to activate conda environment.
> If your tenant does not have permission to use `source`, `Jupyter Task Plugin` will not function.
## Python Dependency Management
### Use Pre-Installed Conda Environment
1. Create a conda environment manually or using `shell task` on your target worker.
2. In your `jupyter task`, set `condaEnvName` as the name of the conda environment you just created.
### Use Packed Conda Environment
1. Use [Conda-Pack](https://conda.github.io/conda-pack/) to pack your conda environment into `tarball`.
2. Upload packed conda environment to `resource center`.
3. Select your packed conda environment as `resource` in your `jupyter task`, e.g. `jupyter_env.tar.gz`.
> NOTE: Make sure you follow the [Conda-Pack](https://conda.github.io/conda-pack/) official instructions.
> If you unpack your packed conda environment, the directory structure should be the same as below:
```
.
├── bin
├── conda-meta
├── etc
├── include
├── lib
├── share
└── ssl
```
> NOTICE: Please follow the `conda pack` instructions above strictly, and DO NOT modify `bin/activate`.
> `Jupyter Task Plugin` uses `source` command to activate your packed conda environment.
> If you are concerned about using `source`, choose other options to manage your python dependency.
## Create Task
- Click `Project Management-Project Name-Workflow Definition`, and click the `Create Workflow` button to enter the DAG editing page.
- Drag <img src="../../../../img/tasks/icons/jupyter.png" width="15"/> from the toolbar to the canvas.
## Task Parameters
| **Parameter** | **Description** |
| ------- | ---------- |
| Node Name | Set the name of the task. Node names within a workflow definition are unique. |
| Run flag | Indicates whether the node can be scheduled normally. If it is not necessary to execute, you can turn on the prohibiting execution switch. |
| Description | Describes the function of this node. |
| Task priority | When the number of worker threads is insufficient, they are executed in order from high to low according to the priority, and they are executed according to the first-in, first-out principle when the priority is the same. |
| Worker group | The task is assigned to the machines in the worker group for execution. If Default is selected, a worker machine will be randomly selected for execution. |
| Task group name | The group in Resources, if not configured, it will not be used. |
| Environment Name | Configure the environment in which to run the script. |
| Number of failed retries | The number of times the task is resubmitted after failure. It supports drop-down and manual filling. |
| Failure Retry Interval | The time interval for resubmitting the task if the task fails. It supports drop-down and manual filling. |
| Cpu quota | Assign the specified CPU time quota to the task executed. Takes a percentage value. Default -1 means unlimited. For example, the full CPU load of one core is 100%,and that of 16 cores is 1600%. This function is controlled by [task.resource.limit.state](../../architecture/configuration.md). |
| Max memory | Assign the specified max memory to the task executed. Exceeding this limit will trigger oom to be killed and will not automatically retry. Takes an MB value. Default -1 means unlimited. This function is controlled by [task.resource.limit.state](../../architecture/configuration.md). |
| Timeout alarm | Check the timeout alarm and timeout failure. When the task exceeds the "timeout period", an alarm email will send and the task execution will fail. |
| Conda Env Name | Name of conda environment or packed conda environment tarball. |
|Input Note Path | Path of input jupyter note template. |
| Out Note Path | Path of output note. |
| Jupyter Parameters | Parameters in json format used for jupyter note parameterization. |
| Kernel | Jupyter notebook kernel. |
| Engine | Engine to evaluate jupyter notes. |
| Jupyter Execution Timeout | Timeout set for each jupyter notebook cell. |
| Jupyter Start Timeout | Timeout set for jupyter notebook kernel. |
| Others | Other command options for papermill. |
## Task Example
### Jupyter Task Example
This example illustrates how to create a jupyter task node.
![demo-jupyter-simple](../../../../img/tasks/demo/jupyter.png)

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# K8S Node
## Overview
K8S task type used to execute a batch task. In this task, the worker submits the task by using a k8s client.
## Create Task
- Click `Project Management -> Project Name -> Workflow Definition`, and click the `Create Workflow` button to enter the DAG editing page.
- Drag from the toolbar <img src="../../../../img/tasks/icons/kubernetes.png" width="15"/> to the canvas.
## Task Parameters
| **Parameter** | **Description** |
| ------- | ---------- |
| Node Name | Set the name of the task. Node names within a workflow definition are unique. |
| Run flag | Indicates whether the node can be scheduled normally. If it is not necessary to execute, you can turn on the prohibiting execution switch. |
| Description | Describes the function of this node. |
| Task priority | When the number of worker threads is insufficient, they are executed in order from high to low according to the priority, and they are executed according to the first-in, first-out principle when the priority is the same. |
| Worker group | The task is assigned to the machines in the worker group for execution. If Default is selected, a worker machine will be randomly selected for execution. |
| Task group name | The group in Resources, if not configured, it will not be used. |
| Environment Name | Configure the environment in which to run the script. |
| Number of failed retries | The number of times the task is resubmitted after failure. It supports drop-down and manual filling. |
| Failure Retry Interval | The time interval for resubmitting the task if the task fails. It supports drop-down and manual filling. |
| Timeout alarm | Check Timeout Alarm and Timeout Failure. When the task exceeds the "timeout duration", an alarm email will be sent and the task execution will fail. |
| Namespace | The namespace for running k8s task. |
| Min CPU | Minimum CPU requirement for running k8s task. |
| Min Memory | Minimum memory requirement for running k8s task. |
| Image | The registry url for image. |
| Custom parameter | It is a local user-defined parameter for K8S task, these params will pass to container as environment variables. |
| Predecessor task | Selecting a predecessor task for the current task, will set the selected predecessor task as upstream of the current task. |
## Task Example
### Configure the K8S Environment in DolphinScheduler
If you are using the K8S task type in a production environment, the K8S cluster environment is required.
### Configure K8S Nodes
Configure the required content according to the parameter descriptions above.
![K8S](../../../../img/tasks/demo/kubernetes-task-en.png)
## Note
Task name contains only lowercase alphanumeric characters or '-'

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# MLflow Node
## Overview
[MLflow](https://mlflow.org) is an excellent open source platform to manage the ML lifecycle, including experimentation,
reproducibility, deployment, and a central model registry.
MLflow task plugin used to execute MLflow tasks，Currently contains MLflow Projects and MLflow Models. (Model Registry will soon be rewarded for support)
- MLflow Projects: Package data science code in a format to reproduce runs on any platform.
- MLflow Models: Deploy machine learning models in diverse serving environments.
- Model Registry: Store, annotate, discover, and manage models in a central repository.
The MLflow plugin currently supports and will support the following:
- [x] MLflow Projects
- [x] BasicAlgorithm: contains LogisticRegression, svm, lightgbm, xgboost
- [x] AutoML: AutoML tool，contains autosklean, flaml
- [x] Custom projects: Support for running your own MLflow projects
- [ ] MLflow Models
- [x] MLFLOW: Use `MLflow models serve` to deploy a model service
- [x] Docker: Run the container after packaging the docker image
- [x] Docker Compose: Use docker compose to run the container, it will replace the docker run above
- [ ] Seldon core: Use Selcon core to deploy model to k8s cluster
- [ ] k8s: Deploy containers directly to K8S
- [ ] MLflow deployments: Built-in deployment modules, such as built-in deployment to SageMaker, etc
- [ ] Model Registry
- [ ] Register Model: Allows artifacts (Including model and related parameters, indicators) to be registered directly into the model center
## Create Task
- Click `Project Management -> Project Name -> Workflow Definition`, and click the `Create Workflow` button to enter the DAG editing page.
- Drag from the toolbar <img src="../../../../img/tasks/icons/mlflow.png" width="15"/> task node to canvas.
## Task Parameters and Example
| **Parameter** | **Description** |
| ------- | ---------- |
| Node Name | Set the name of the task. Node names within a workflow definition are unique. |
| Run flag | Indicates whether the node can be scheduled normally. If it is not necessary to execute, you can turn on the prohibiting execution switch. |
| Description | Describes the function of this node. |
| Task priority | When the number of worker threads is insufficient, they are executed in order from high to low according to the priority, and they are executed according to the first-in, first-out principle when the priority is the same. |
| Worker group | The task is assigned to the machines in the worker group for execution. If Default is selected, a worker machine will be randomly selected for execution. |
| Task group name | The group in Resources, if not configured, it will not be used. |
| Environment Name | Configure the environment in which to run the script. |
| Number of failed retries | The number of times the task is resubmitted after failure. It supports drop-down and manual filling. |
| Failure Retry Interval | The time interval for resubmitting the task if the task fails. It supports drop-down and manual filling. |
| Timeout alarm | Check Timeout Alarm and Timeout Failure. When the task exceeds the "timeout duration", an alarm email will be sent and the task execution will fail. |
| Predecessor task | Selecting the predecessor task of the current task will set the selected predecessor task as the upstream of the current task. |
| MLflow Tracking Server URI | MLflow Tracking Server URI, default http://localhost:5000. |
| Experiment Name | Create the experiment where the task is running, if the experiment does not exist. If the name is empty, it is set to ` Default `, the same as MLflow. |
### MLflow Projects
#### BasicAlgorithm
![mlflow-conda-env](../../../../img/tasks/demo/mlflow-basic-algorithm.png)
**Task Parameters**
| **Parameter** | **Description** |
| ------- | ---------- |
| Register Model | Register the model or not. If register is selected, the following parameters are expanded. |
| Model Name | The registered model name is added to the original model version and registered as Production. |
| 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. |
| 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> |
| Algorithm |The selected algorithm currently supports `LR`, `SVM`, `LightGBM` and `XGboost` based on [scikit-learn](https://scikit-learn.org/) form. |
| 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()`. |
#### AutoML
![mlflow-automl](../../../../img/tasks/demo/mlflow-automl.png)
**Task Parameter**
| **Parameter** | **Description** |
| ------- | ---------- |
| Register Model | Register the model or not. If register is selected, the following parameters are expanded. |
| model name | The registered model name is added to the original model version and registered as Production. |
| 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). |
| 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> |
| AutoML tool | The AutoML tool used, currently supports [autosklearn](https://github.com/automl/auto-sklearn) and [flaml](https://github.com/microsoft/FLAML). |
#### Custom projects
![mlflow-custom-project.png](../../../../img/tasks/demo/mlflow-custom-project.png)
**Task Parameter**
| **Parameter** | **Description** |
| ------- | ---------- |
| parameters | `--param-list` in `mlflow run`. For example `-P learning_rate=0.2 -P colsample_bytree=0.8 -P subsample=0.9`. |
| 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. |
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.
### MLflow Models
**General Parameters**
| **Parameter** | **Description** |
| ------- | ---------- |
| Model-URI | Model-URI of MLflow , support `models:/<model_name>/suffix` format and `runs:/` format. See https://mlflow.org/docs/latest/tracking.html#artifact-stores |
| Port | The port to listen on. |
#### MLflow
![mlflow-models-mlflow](../../../../img/tasks/demo/mlflow-models-mlflow.png)
#### Docker
![mlflow-models-docker](../../../../img/tasks/demo/mlflow-models-docker.png)
#### DOCKER COMPOSE
![mlflow-models-docker-compose](../../../../img/tasks/demo/mlflow-models-docker-compose.png)
| **Parameter** | **Description** |
| ------- | ---------- |
| Max Cpu Limit | For example, `1.0` or `0.5`, the same as docker compose. |
| Max Memory Limit | For example `1G` or `500M`, the same as docker compose. |
## Environment to Prepare
### Conda Environment
You need to enter the admin account to configure a conda environment variable（Please
install [anaconda](https://docs.continuum.io/anaconda/install/)
or [miniconda](https://docs.conda.io/en/latest/miniconda.html#installing ) in advance).
![mlflow-conda-env](../../../../img/tasks/demo/mlflow-conda-env.png)
Note During the configuration task, select the conda environment created above. Otherwise, the program cannot find the
Conda environment.
![mlflow-set-conda-env](../../../../img/tasks/demo/mlflow-set-conda-env.png)
### Start the MLflow Service
Make sure you have installed MLflow, using 'pip install mlflow'.
Create a folder where you want to save your experiments and models and start MLflow service.
```sh
mkdir mlflow
cd mlflow
mlflow server -h 0.0.0.0 -p 5000 --serve-artifacts --backend-store-uri sqlite:///mlflow.db
```
After running, an MLflow service is started.
After this, you can visit the MLflow service (`http://localhost:5000`) page to view the experiments and models.
![mlflow-server](../../../../img/tasks/demo/mlflow-server.png)

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# OpenMLDB Node
## Overview
[OpenMLDB](https://openmldb.ai/) is an excellent open source machine learning database, providing a full-stack
FeatureOps solution for production.
OpenMLDB task plugin used to execute tasks on OpenMLDB cluster.
## Create Task
- Click `Project Management -> Project Name -> Workflow Definition`, and click the `Create Workflow` button to enter the DAG editing page.
- Drag from the toolbar <img src="../../../../img/tasks/icons/openmldb.png" width="15"/> task node to canvas.
## Task Parameters
| **Parameter** | **Description** |
| ------- | ---------- |
| Node Name | Set the name of the task. Node names within a workflow definition are unique. |
| Run flag | Indicates whether the node can be scheduled normally. If it is not necessary to execute, you can turn on the prohibiting execution switch. |
| Description | Describes the function of this node. |
| Task priority | When the number of worker threads is insufficient, they are executed in order from high to low according to the priority, and they are executed according to the first-in, first-out principle when the priority is the same. |
| Worker group | The task is assigned to the machines in the worker group for execution. If Default is selected, a worker machine will be randomly selected for execution. |
| Task group name | The group in Resources, if not configured, it will not be used. |
| Environment Name | Configure the environment in which to run the script. |
| Number of failed retries | The number of times the task is resubmitted after failure. It supports drop-down and manual filling. |
| Failure Retry Interval | The time interval for resubmitting the task if the task fails. It supports drop-down and manual filling. |
| Timeout alarm | Check Timeout Alarm and Timeout Failure. When the task exceeds the "timeout duration", an alarm email will be sent and the task execution will fail. |
| Predecessor task | Selecting the predecessor task of the current task will set the selected predecessor task as the upstream of the current task. |
| zookeeper | OpenMLDB cluster zookeeper address, e.g. 127.0.0.1:2181. |
| zookeeper path | OpenMLDB cluster zookeeper path, e.g. /openmldb. |
| Execute Mode | Determine the init mode, offline or online. You can switch it in sql statement. |
| SQL statement | SQL statement. |
| Custom parameters | It is the user-defined parameters of Python, which will replace the content with \${variable} in the script. |
## Task Examples
### Load data
![load data](../../../../img/tasks/demo/openmldb-load-data.png)
We use `LOAD DATA` to load data into OpenMLDB cluster. We select `offline` here, so it will load to offline storage.
### Feature extraction
![fe](../../../../img/tasks/demo/openmldb-feature-extraction.png)
We use `SELECT INTO` to do feature extraction. We select `offline` here, so it will run sql on offline engine.
### Environment to Prepare
#### Start the OpenMLDB Cluster
You should create an OpenMLDB cluster first. If in production env, please check [deploy OpenMLDB](https://openmldb.ai/docs/en/v0.5/deploy/install_deploy.html).
You can follow [run OpenMLDB in docker](https://openmldb.ai/docs/zh/v0.5/quickstart/openmldb_quickstart.html#id11)
to a quick start.
#### Python Environment
The OpenMLDB task will use OpenMLDB Python SDK to connect OpenMLDB cluster. So you should have the Python env.
We will use `python3` by default. You can set `PYTHON_HOME` to use your custom python env.
Make sure you have installed OpenMLDB Python SDK in the host where the worker server running, using `pip install openmldb`.

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# Apache Zeppelin
## Overview
Use `Zeppelin Task` to create a zeppelin-type task and execute zeppelin notebook paragraphs. When the worker executes `Zeppelin Task`,
it will call `Zeppelin Client API` to trigger zeppelin notebook paragraph. Click [here](https://zeppelin.apache.org/) for details about `Apache Zeppelin Notebook`.
## Create Task
- Click `Project Management -> Project Name -> Workflow Definition`, and click the `Create Workflow` button to enter the DAG editing page.
- Drag <img src="../../../../img/tasks/icons/zeppelin.png" width="15"/> from the toolbar to the canvas.
## Task Parameters
| **Parameter** | **Description** |
| ------- | ---------- |
| Node Name | Set the name of the task. Node names within a workflow definition are unique. |
| Run flag | Indicates whether the node can be scheduled normally. If it is not necessary to execute, you can turn on the prohibiting execution switch. |
| Description | Describes the function of this node. |
| Task priority | When the number of worker threads is insufficient, they are executed in order from high to low according to the priority, and they are executed according to the first-in, first-out principle when the priority is the same. |
| Worker group | The task is assigned to the machines in the worker group for execution. If Default is selected, a worker machine will be randomly selected for execution. |
| Task group name | The group in Resources, if not configured, it will not be used. |
| Environment Name | Configure the environment in which to run the script. |
| Number of failed retries | The number of times the task is resubmitted after failure. It supports drop-down and manual filling. |
| Failure Retry Interval | The time interval for resubmitting the task if the task fails. It supports drop-down and manual filling. |
| Timeout alarm | Check Timeout Alarm and Timeout Failure. When the task exceeds the "timeout duration", an alarm email will be sent and the task execution will fail. |
| Zeppelin Note ID | The unique note id for a zeppelin notebook note. |
| Zeppelin Paragraph ID | The unique paragraph id for a zeppelin notebook paragraph. If you want to schedule a whole note at a time, leave this field blank. |
| Zeppelin Parameters | Parameters in json format used for zeppelin dynamic form. |
## Task Example
### Zeppelin Paragraph Task Example
This example illustrates how to create a zeppelin paragraph task node.
![demo-zeppelin-paragraph](../../../../img/tasks/demo/zeppelin.png)
![demo-get-zeppelin-id](../../../../img/tasks/demo/zeppelin_id.png)
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