[GitHub] [dolphinscheduler] zhongjiajie commented on a diff in pull request #10150: [Feature][MLops] Support MLflow Models to deploy model service (MLflow models serve and Docker)

[GitBox <gi...@apache.org>]

zhongjiajie commented on code in PR #10150:
URL: https://github.com/apache/dolphinscheduler/pull/10150#discussion_r877861232


##########
docs/docs/en/guide/task/mlflow.md:
##########
@@ -5,77 +5,140 @@
 [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 is used to perform mlflow project tasks, which include basic algorithmic and autoML capabilities (
-User-defined MLFlow project task execution will be supported in the near future)
+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:
+
+- [ ] MLflow Projects
+    - [x] BasicAlgorithm: contains lr, svm, lightgbm, xgboost
+    - [x] AutoML: AutoML tool，contains autosklean, flaml
+    - [ ] 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
+    - [ ] Docker Compose: Use docker compose to run the container, 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

Review Comment:
   It is a document for users, I think we should not add something we do not support yet, it is for developers or contributors, maybe we should only add what we support here, but I do not have strong opinions here.



##########
docs/docs/en/guide/task/mlflow.md:
##########
@@ -5,77 +5,140 @@
 [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 is used to perform mlflow project tasks, which include basic algorithmic and autoML capabilities (
-User-defined MLFlow project task execution will be supported in the near future)
+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:
+
+- [ ] MLflow Projects
+    - [x] BasicAlgorithm: contains lr, svm, lightgbm, xgboost
+    - [x] AutoML: AutoML tool，contains autosklean, flaml
+    - [ ] 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
+    - [ ] Docker Compose: Use docker compose to run the container, 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 Parameter
-
-- DolphinScheduler common parameters
-    - **Node name**: The node name in a workflow definition is unique.
-    - **Run flag**: Identifies whether this node schedules normally, if it does not need to execute, select
-      the `prohibition execution`.
-    - **Descriptive information**: Describe the function of the node.
-    - **Task priority**: When the number of worker threads is insufficient, execute in the order of priority from high
-      to low, and tasks with the same priority will execute in a first-in first-out order.
-    - **Worker grouping**: Assign tasks to the machines of the worker group to execute. If `Default` is selected,
-      randomly select a worker machine for execution.
-    - **Environment Name**: Configure the environment name in which run the script.
-    - **Times of failed retry attempts**: The number of times the task failed to resubmit.
-    - **Failed retry interval**: The time interval (unit minute) for resubmitting the task after a failed task.
-    - **Delayed execution time**: The time (unit minute) that a task delays in execution.
-    - **Timeout alarm**: Check the timeout alarm and timeout failure. When the task runs exceed the "timeout", an alarm
-      email will send and the task execution will fail.
-    - **Custom parameter**: It is a local user-defined parameter for mlflow, and will replace the content
-      with `${variable}` in the script.
-    - **Predecessor task**: Selecting a predecessor task for the current task, will set the selected predecessor task as
-      upstream of the current task.
-
-- MLflow task specific parameters
-    - **mlflow server tracking uri** ：MLflow server uri, default http://localhost:5000.
-    - **experiment name** ：The experiment in which the task is running, if none, is created.
-    - **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.
-    - **job type** : The type of task to run, currently including the underlying algorithm and AutoML. (User-defined
-      MLFlow project task execution will be supported in the near future)
-        - BasicAlgorithm specific parameters
-            - **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 specific parameters
-            - **AutoML tool** : The AutoML tool used, currently
-              supports [autosklearn](https://github.com/automl/auto-sklearn)
-              and [flaml](https://github.com/microsoft/FLAML)
-        - Parameters common to BasicAlgorithm and AutoML
-        - **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()`.
-            - BasicAlgorithm
-                - [lr](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html#sklearn.linear_model.LogisticRegression)
-                - [SVM](https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html?highlight=svc#sklearn.svm.SVC)
-                - [lightgbm](https://lightgbm.readthedocs.io/en/latest/pythonapi/lightgbm.LGBMClassifier.html#lightgbm.LGBMClassifier)
-                - [xgboost](https://xgboost.readthedocs.io/en/stable/python/python_api.html#xgboost.XGBClassifier)
-            - AutoML
-                - [flaml](https://microsoft.github.io/FLAML/docs/reference/automl#automl-objects)
-                - [autosklearn](https://automl.github.io/auto-sklearn/master/api.html)
-
 ## Task Example
 
-### Preparation
+First, introduce some general parameters of DolphinScheduler
+
+- **Node name**: The node name in a workflow definition is unique.
+- **Run flag**: Identifies whether this node schedules normally, if it does not need to execute, select
+  the `prohibition execution`.
+- **Descriptive information**: Describe the function of the node.
+- **Task priority**: When the number of worker threads is insufficient, execute in the order of priority from high
+  to low, and tasks with the same priority will execute in a first-in first-out order.
+- **Worker grouping**: Assign tasks to the machines of the worker group to execute. If `Default` is selected,
+  randomly select a worker machine for execution.
+- **Environment Name**: Configure the environment name in which run the script.
+- **Times of failed retry attempts**: The number of times the task failed to resubmit.
+- **Failed retry interval**: The time interval (unit minute) for resubmitting the task after a failed task.
+- **Delayed execution time**: The time (unit minute) that a task delays in execution.
+- **Timeout alarm**: Check the timeout alarm and timeout failure. When the task runs exceed the "timeout", an alarm
+  email will send and the task execution will fail.
+- **Predecessor task**: Selecting a predecessor task for the current task, will set the selected predecessor task as
+  upstream of the current task.
+
+### MLflow Projects
+
+#### BasicAlgorithm
+
+![mlflow-conda-env](/img/tasks/demo/mlflow-basic-algorithm.png)
+
+**Task Parameter**
+
+- **mlflow server tracking uri** ：MLflow server uri, default http://localhost:5000.
+- **job type** : The type of task to run, currently including the underlying algorithm and AutoML. (User-defined
+  MLFlow project task execution will be supported in the near future)
+- **experiment name** ：The experiment in which the task is running, if none, is created.
+- **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()`.
+    - [lr](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html#sklearn.linear_model.LogisticRegression)

Review Comment:
   I do not like abbreviation in the document, I do not think all the reader will know its mean.
   ```suggestion
       - [Logistic Regression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html#sklearn.linear_model.LogisticRegression)
   ```



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