[GitHub] [dolphinscheduler] zhongjiajie commented on a diff in pull request #10217: [Feature] [MLOps] support mlflow deploy with docker compose

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

zhongjiajie commented on code in PR #10217:
URL: https://github.com/apache/dolphinscheduler/pull/10217#discussion_r882261004


##########
dolphinscheduler-task-plugin/dolphinscheduler-task-mlflow/src/main/java/org/apache/dolphinscheduler/plugin/task/mlflow/MlflowTask.java:
##########
@@ -98,10 +98,15 @@ public void handle() throws Exception {
     @Override
     public void cancelApplication(boolean cancelApplication) throws Exception {
         // cancel process
-        shellCommandExecutor.cancelApplication();
+        if (mlflowParameters.getDeployType().equals(MlflowConstants.MLFLOW_MODELS_DEPLOY_TYPE_DOCKER_COMPOSE)) {
+            String envCommand = mlflowParameters.getDockerComposeEnvCommand();
+            shellCommandExecutor.run(envCommand + "\n" + MlflowConstants.DOCKER_COMPOSE_CANCEL);

Review Comment:
   did you task cancel the task locally? I find out your task starts with `docker up -d` and it not alway running task which I think it should not be cancel



##########
docs/docs/en/guide/task/mlflow.md:
##########
@@ -92,71 +95,65 @@ First, introduce some general parameters of DolphinScheduler
 
 **Task Parameter**
 
-- **mlflow server tracking uri** ：MLflow 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.
-- **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
+- **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
+- **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:
   - [flaml](https://microsoft.github.io/FLAML/docs/reference/automl#automl-objects)
   - [autosklearn](https://automl.github.io/auto-sklearn/master/api.html)
-- **AutoML tool** : The AutoML tool used, currently
+- **AutoML tool**: The AutoML tool used, currently
   supports [autosklearn](https://github.com/automl/auto-sklearn)
-  and [flaml](https://github.com/microsoft/FLAML)
+  and [flaml](https://github.com/microsoft/FLAML).
 

Review Comment:
   ```suggestion
   ```



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