[improve] Optimize MLFlow task plugin for easy of use (#12071)

optimize code

(cherry picked from commit cf522e2f)

docs/docs/en/guide/task/mlflow.md

@@ -20,7 +20,6 @@ The MLflow plugin currently supports and will support the following:
 - MLflow Models
   - MLFLOW: Use `MLflow models serve` to deploy a model service
   - Docker: Run the container after packaging the docker image
-  - Docker Compose: Use docker compose to run the container, it will replace the docker run above
 
 ## Create Task
 
@@ -98,22 +97,26 @@ You can now use this feature to run all MLFlow projects on Github (For example [
 
 ![mlflow-models-docker](../../../../img/tasks/demo/mlflow-models-docker.png)
 
-#### DOCKER COMPOSE
+## Environment to Prepare
 
-![mlflow-models-docker-compose](../../../../img/tasks/demo/mlflow-models-docker-compose.png)
+### Conda Environment
+Please install [anaconda](https://docs.continuum.io/anaconda/install/) or [miniconda](https://docs.conda.io/en/latest/miniconda.html#installing) in advance.
 
-|  **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.  |
+**Method A:**
 
-## Environment to Prepare
+Config anaconda environment in `/dolphinscheduler/conf/env/dolphinscheduler_env.sh`.
 
-### Conda Environment
+Add the following content to the file:
+
+```bash
+# config anaconda environment
+export PATH=/opt/anaconda3/bin:$PATH
+```
 
-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).
+
+**Method B:**
+
+You need to enter the admin account to configure a conda environment variable.
 
 ![mlflow-conda-env](../../../../img/tasks/demo/mlflow-conda-env.png)
 
@@ -139,3 +142,14 @@ 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)
+
+### Preset Algorithm Repository Configuration
+
+If you can't access github, you can modify the following fields in the `commom.properties` configuration file to replace the github address with an accessible address.
+
+```yaml
+# mlflow task plugin preset repository
+ml.mlflow.preset_repository=https://github.com/apache/dolphinscheduler-mlflow
+# mlflow task plugin preset repository version
+ml.mlflow.preset_repository_version="main"
+```

docs/docs/zh/guide/task/mlflow.md

@@ -4,7 +4,7 @@
 
 [MLflow](https://mlflow.org) 是一个MLops领域一个优秀的开源项目， 用于管理机器学习的生命周期，包括实验、可再现性、部署和中心模型注册。
 
-MLflow 组件用于执行 MLflow 任务，目前包含Mlflow Projects, 和MLflow Models。（Model Registry将在不就的将来支持）。
+MLflow 组件用于执行 MLflow 任务，目前包含Mlflow Projects，和MLflow Models。（Model Registry将在不就的将来支持）。
 
 - MLflow Projects: 将代码打包，并可以运行到任务的平台上。
 - MLflow Models: 在不同的服务环境中部署机器学习模型。
@@ -12,19 +12,13 @@ MLflow 组件用于执行 MLflow 任务，目前包含Mlflow Projects, 和MLflow
 
 目前 Mlflow 组件支持的和即将支持的内容如下中：
 
-- [x] MLflow Projects
-  - [x] BasicAlgorithm: 基础算法，包含LogisticRegression, svm, lightgbm, xgboost
-  - [x] AutoML: AutoML工具，包含autosklean, flaml
-  - [x] Custom projects: 支持运行自己的MLflow Projects项目
-- [ ] MLflow Models
-  - [x] MLFLOW: 直接使用 `mlflow models serve` 部署模型。
-  - [x] Docker: 打包 DOCKER 镜像后部署模型。
-  - [x] Docker Compose: 使用Docker Compose 部署模型，将会取代上面的Docker部署。
-  - [ ] Seldon core: 构建完镜像后，使用Seldon Core 部署到k8s集群上, 可以使用Seldon Core的生成模型管理能力。
-  - [ ] k8s: 构建完镜像后， 部署到k8s集群上。
-  - [ ] MLflow deployments: 内置的允许MLflow 部署模块, 如内置的部署到Sagemaker等。
-- [ ] Model Registry
-  - [ ] Register Model: 注册相关工件(模型以及相关的参数，指标)到模型中心
+- MLflow Projects
+  - BasicAlgorithm: 基础算法，包含LogisticRegression， svm， lightgbm， xgboost
+  - AutoML: AutoML工具，包含autosklean， flaml
+  - Custom projects: 支持运行自己的MLflow Projects项目
+- MLflow Models
+  - MLFLOW: 直接使用 `mlflow models serve` 部署模型。
+  - Docker: 打包 DOCKER 镜像后部署模型。
 
 ## 创建任务
 
@@ -95,21 +89,25 @@ MLflow 组件用于执行 MLflow 任务，目前包含Mlflow Projects, 和MLflow
 
 ![mlflow-models-docker](../../../../img/tasks/demo/mlflow-models-docker.png)
 
-#### DOCKER COMPOSE
+## 环境准备
 
-![mlflow-models-docker-compose](../../../../img/tasks/demo/mlflow-models-docker-compose.png)
+### conda 环境配置
 
-| **任务参数** |                **描述**                |
-|----------|--------------------------------------|
-| 最大CPU限制  | 如 `1.0` 或者 `0.5`，与 docker compose 一致 |
-| 最大内存限制   | 如 `1G` 或者 `500M`，与 docker compose 一致 |
+请提前[安装anaconda](https://docs.continuum.io/anaconda/install/) 或者[安装miniconda](https://docs.conda.io/en/latest/miniconda.html#installing)
 
-## 环境准备
+**方法A：**
 
-### conda 环境配置
+配置文件：/dolphinscheduler/conf/env/dolphinscheduler_env.sh。
+
+在文件最后添加内容
+```
+# 配置你的conda环境路径
+export PATH=/opt/anaconda3/bin:$PATH
+```
 
-你需要进入admin账户配置一个conda环境变量（请提前[安装anaconda](https://docs.continuum.io/anaconda/install/)
-或者[安装miniconda](https://docs.conda.io/en/latest/miniconda.html#installing) )。
+**方法B：**
+
+你需要进入admin账户配置一个conda环境变量。
 
 ![mlflow-conda-env](../../../../img/tasks/demo/mlflow-conda-env.png)
 
@@ -117,6 +115,7 @@ MLflow 组件用于执行 MLflow 任务，目前包含Mlflow Projects, 和MLflow
 
 ![mlflow-set-conda-env](../../../../img/tasks/demo/mlflow-set-conda-env.png)
 
+
 ### MLflow service 启动
 
 确保你已经安装MLflow，可以使用`pip install mlflow`进行安装。
@@ -135,3 +134,15 @@ mlflow server -h 0.0.0.0 -p 5000 --serve-artifacts --backend-store-uri sqlite://
 
 ![mlflow-server](../../../../img/tasks/demo/mlflow-server.png)
 
+
+### 内置算法仓库配置
+
+如果遇到github无法访问的情况，可以修改`commom.properties`配置文件的以下字段，将github地址替换能访问的地址。
+
+```yaml
+# mlflow task plugin preset repository
+ml.mlflow.preset_repository=https://github.com/apache/dolphinscheduler-mlflow
+# mlflow task plugin preset repository version
+ml.mlflow.preset_repository_version="main"
+```
+

dolphinscheduler-common/src/main/resources/common.properties

@@ -105,4 +105,9 @@ alert.rpc.port=50052
 conda.path=/opt/anaconda3/etc/profile.d/conda.sh
 
 # Task resource limit state
-task.resource.limit.state=false
+task.resource.limit.state=false
+
+# mlflow task plugin preset repository
+ml.mlflow.preset_repository=https://github.com/apache/dolphinscheduler-mlflow
+# mlflow task plugin preset repository version
+ml.mlflow.preset_repository_version="main"

dolphinscheduler-python/pydolphinscheduler/examples/yaml_define/mlflow.yaml

@@ -32,15 +32,6 @@ tasks:
     parameters: -P learning_rate=0.2 -P colsample_bytree=0.8 -P subsample=0.9
     experiment_name: xgboost
 
-
-  - name: deploy_mlflow
-    deps: [train_xgboost_native]
-    task_type: MLflowModels 
-    model_uri: models:/xgboost_native/Production
-    mlflow_tracking_uri: *mlflow_tracking_uri
-    deploy_mode: MLFLOW
-    port: 7001
-
   - name: train_automl
     task_type: MLFlowProjectsAutoML 
     mlflow_tracking_uri: *mlflow_tracking_uri
@@ -68,11 +59,11 @@ tasks:
     data_path: /data/examples/iris
     search_params: max_depth=[5, 10];n_estimators=[100, 200]
 
-
-  - name: deploy_docker_compose
-    task_type: MLflowModels 
+  - name: deploy_mlflow
     deps: [train_basic_algorithm]
+    task_type: MLflowModels
     model_uri: models:/iris_B/Production
     mlflow_tracking_uri: *mlflow_tracking_uri
-    deploy_mode: DOCKER COMPOSE
-    port: 7003
+    deploy_mode: MLFLOW
+    port: 7001
+

dolphinscheduler-python/pydolphinscheduler/src/pydolphinscheduler/examples/task_mlflow_example.py

@@ -43,17 +43,6 @@ with ProcessDefinition(
         experiment_name="xgboost",
     )
 
-    # Using MLFLOW to deploy model from custom mlflow project
-    deploy_mlflow = MLflowModels(
-        name="deploy_mlflow",
-        model_uri="models:/xgboost_native/Production",
-        mlflow_tracking_uri=mlflow_tracking_uri,
-        deploy_mode=MLflowDeployType.MLFLOW,
-        port=7001,
-    )
-
-    train_custom >> deploy_mlflow
-
     # run automl to train model
     train_automl = MLFlowProjectsAutoML(
         name="train_automl",
@@ -88,16 +77,16 @@ with ProcessDefinition(
         search_params="max_depth=[5, 10];n_estimators=[100, 200]",
     )
 
-    # Using DOCKER COMPOSE to deploy model from train_basic_algorithm
-    deploy_docker_compose = MLflowModels(
-        name="deploy_docker_compose",
+    # Using MLFLOW to deploy model from training lightgbm project
+    deploy_mlflow = MLflowModels(
+        name="deploy_mlflow",
         model_uri="models:/iris_B/Production",
         mlflow_tracking_uri=mlflow_tracking_uri,
-        deploy_mode=MLflowDeployType.DOCKER_COMPOSE,
-        port=7003,
+        deploy_mode=MLflowDeployType.MLFLOW,
+        port=7001,
     )
 
-    train_basic_algorithm >> deploy_docker_compose
+    train_basic_algorithm >> deploy_mlflow
 
     pd.submit()
 

dolphinscheduler-python/pydolphinscheduler/src/pydolphinscheduler/tasks/mlflow.py

@@ -43,7 +43,6 @@ class MLflowDeployType(str):
 
     MLFLOW = "MLFLOW"
     DOCKER = "DOCKER"
-    DOCKER_COMPOSE = "DOCKER COMPOSE"
 
 
 DEFAULT_MLFLOW_TRACKING_URI = "http://127.0.0.1:5000"
@@ -83,10 +82,8 @@ class MLflowModels(BaseMLflow):
     :param 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
     :param mlflow_tracking_uri: MLflow tracking server uri, default is http://127.0.0.1:5000
-    :param deploy_mode: MLflow deploy mode, support MLFLOW, DOCKER, DOCKER COMPOSE, default is DOCKER
+    :param deploy_mode: MLflow deploy mode, support MLFLOW, DOCKER, default is DOCKER
     :param port: deploy port, default is 7000
-    :param cpu_limit: cpu limit, default is 1.0
-    :param memory_limit: memory limit, default is 500M
     """
 
     mlflow_task_type = MLflowTaskType.MLFLOW_MODELS
@@ -95,8 +92,6 @@ class MLflowModels(BaseMLflow):
         "deploy_type",
         "deploy_model_key",
         "deploy_port",
-        "cpu_limit",
-        "memory_limit",
     }
 
     def __init__(
@@ -106,8 +101,6 @@ class MLflowModels(BaseMLflow):
         mlflow_tracking_uri: Optional[str] = DEFAULT_MLFLOW_TRACKING_URI,
         deploy_mode: Optional[str] = MLflowDeployType.DOCKER,
         port: Optional[int] = 7000,
-        cpu_limit: Optional[float] = 1.0,
-        memory_limit: Optional[str] = "500M",
         *args,
         **kwargs
     ):
@@ -116,8 +109,6 @@ class MLflowModels(BaseMLflow):
         self.deploy_type = deploy_mode.upper()
         self.deploy_model_key = model_uri
         self.deploy_port = port
-        self.cpu_limit = cpu_limit
-        self.memory_limit = memory_limit
 
 
 class MLFlowProjectsCustom(BaseMLflow):

dolphinscheduler-python/pydolphinscheduler/tests/tasks/test_mlflow.py

@@ -63,19 +63,15 @@ def test_mlflow_models_get_define():
     name = "mlflow_models"
     model_uri = "models:/xgboost_native/Production"
     port = 7001
-    cpu_limit = 2.0
-    memory_limit = "600M"
 
     expect = deepcopy(EXPECT)
     expect["name"] = name
     task_params = expect["taskParams"]
     task_params["mlflowTrackingUri"] = MLFLOW_TRACKING_URI
     task_params["mlflowTaskType"] = MLflowTaskType.MLFLOW_MODELS
-    task_params["deployType"] = MLflowDeployType.DOCKER_COMPOSE
+    task_params["deployType"] = MLflowDeployType.DOCKER
     task_params["deployModelKey"] = model_uri
     task_params["deployPort"] = port
-    task_params["cpuLimit"] = cpu_limit
-    task_params["memoryLimit"] = memory_limit
 
     with patch(
         "pydolphinscheduler.core.task.Task.gen_code_and_version",
@@ -85,10 +81,8 @@ def test_mlflow_models_get_define():
             name=name,
             model_uri=model_uri,
             mlflow_tracking_uri=MLFLOW_TRACKING_URI,
-            deploy_mode=MLflowDeployType.DOCKER_COMPOSE,
+            deploy_mode=MLflowDeployType.DOCKER,
             port=port,
-            cpu_limit=cpu_limit,
-            memory_limit=memory_limit,
         )
         assert task.get_define() == expect
 

dolphinscheduler-task-plugin/dolphinscheduler-task-mlflow/src/main/java/org/apache/dolphinscheduler/plugin/task/mlflow/MlflowConstants.java

@@ -28,15 +28,17 @@ public class MlflowConstants {
 
     public static final String JOB_TYPE_CUSTOM_PROJECT = "CustomProject";
 
-    public static final String PRESET_REPOSITORY = "https://github.com/apache/dolphinscheduler-mlflow";
+    public static final String PRESET_REPOSITORY_KEY = "ml.mlflow.preset_repository";
+
+    public static final String PRESET_REPOSITORY_VERSION_KEY = "ml.mlflow.preset_repository_version";
 
-    public static final String PRESET_PATH = "dolphinscheduler-mlflow";
+    public static final String PRESET_REPOSITORY = "https://github.com/apache/dolphinscheduler-mlflow";
 
     public static final String PRESET_REPOSITORY_VERSION = "main";
 
-    public static final String PRESET_AUTOML_PROJECT = PRESET_PATH + "#Project-AutoML";
+    public static final String PRESET_AUTOML_PROJECT = "#Project-AutoML";
 
-    public static final String PRESET_BASIC_ALGORITHM_PROJECT = PRESET_PATH + "#Project-BasicAlgorithm";
+    public static final String PRESET_BASIC_ALGORITHM_PROJECT = "#Project-BasicAlgorithm";
 
     public static final String MLFLOW_TASK_TYPE_PROJECTS = "MLflow Projects";
 
@@ -46,14 +48,6 @@ public class MlflowConstants {
 
     public static final String MLFLOW_MODELS_DEPLOY_TYPE_DOCKER = "DOCKER";
 
-    public static final String MLFLOW_MODELS_DEPLOY_TYPE_DOCKER_COMPOSE = "DOCKER COMPOSE";
-
-    /**
-     * template file
-     */
-    public static final String TEMPLATE_DOCKER_COMPOSE = "docker-compose.yml";
-
-
     /**
      * mlflow command
      */
@@ -81,8 +75,7 @@ public class MlflowConstants {
 
     public static final String MLFLOW_RUN_CUSTOM_PROJECT = "mlflow run $repo "
         + "%s "
-        + "--experiment-name=\"%s\" "
-        + "--version=\"%s\" ";
+        + "--experiment-name=\"%s\"";
 
     public static final String MLFLOW_MODELS_SERVE = "mlflow models serve -m %s --port %s -h 0.0.0.0";
 
@@ -94,20 +87,10 @@ public class MlflowConstants {
         + "--health-cmd \"curl --fail http://127.0.0.1:8080/ping || exit 1\" --health-interval 5s --health-retries 20"
         + " %s";
 
-    public static final String DOCKER_COMPOSE_RUN = "docker-compose up -d";
-
-    public static final String SET_DOCKER_COMPOSE_ENV = "export DS_TASK_MLFLOW_IMAGE_NAME=%s\n"
-        + "export DS_TASK_MLFLOW_CONTAINER_NAME=%s\n"
-        + "export DS_TASK_MLFLOW_DEPLOY_PORT=%s\n"
-        + "export DS_TASK_MLFLOW_CPU_LIMIT=%s\n"
-        + "export DS_TASK_MLFLOW_MEMORY_LIMIT=%s";
-
-
     public static final String DOCKER_HEALTH_CHECK = "docker inspect --format \"{{json .State.Health.Status }}\" %s";
 
     public static final int DOCKER_HEALTH_CHECK_TIMEOUT = 20;
 
     public static final int DOCKER_HEALTH_CHECK_INTERVAL = 5000;
 
-    public static final String GIT_CLONE_REPO  = "git clone %s %s";
 }

dolphinscheduler-task-plugin/dolphinscheduler-task-mlflow/src/main/java/org/apache/dolphinscheduler/plugin/task/mlflow/MlflowParameters.java

@@ -17,10 +17,14 @@
 
 package org.apache.dolphinscheduler.plugin.task.mlflow;
 
+import lombok.Data;
+
 import org.apache.dolphinscheduler.plugin.task.api.parameters.AbstractParameters;
+import org.apache.dolphinscheduler.spi.utils.StringUtils;
 
 import java.util.HashMap;
 
+@Data
 public class MlflowParameters extends AbstractParameters {
 
     /**
@@ -36,7 +40,7 @@ public class MlflowParameters extends AbstractParameters {
      */
     private String mlflowProjectRepository;
 
-    private String mlflowProjectVersion = "master";
+    private String mlflowProjectVersion = "";
 
     /**
      * AutoML parameters
@@ -76,160 +80,9 @@ public class MlflowParameters extends AbstractParameters {
 
     private String deployPort;
 
-    private String cpuLimit;
-
-    private String memoryLimit;
-
-    public void setAlgorithm(String algorithm) {
-        this.algorithm = algorithm;
-    }
-
-    public String getAlgorithm() {
-        return algorithm;
-    }
-
-    public void setParams(String params) {
-        this.params = params;
-    }
-
-    public String getParams() {
-        return params;
-    }
-
-    public void setSearchParams(String searchParams) {
-        this.searchParams = searchParams;
-    }
-
-    public String getSearchParams() {
-        return searchParams;
-    }
-
-    public void setDataPaths(String dataPath) {
-        this.dataPath = dataPath;
-    }
-
-    public String getDataPath() {
-        return dataPath;
-    }
-
-    public void setMlflowTaskType(String mlflowTaskType) {
-        this.mlflowTaskType = mlflowTaskType;
-    }
-
-    public String getMlflowTaskType() {
-        return mlflowTaskType;
-    }
-
-    public void setExperimentNames(String experimentName) {
-        this.experimentName = experimentName;
-    }
-
-    public String getExperimentName() {
-        return experimentName;
-    }
-
-    public void setModelNames(String modelName) {
-        this.modelName = modelName;
-    }
-
-    public String getModelName() {
-        return modelName;
-    }
-
-    public void setMlflowTrackingUris(String mlflowTrackingUri) {
-        this.mlflowTrackingUri = mlflowTrackingUri;
-    }
-
-    public String getMlflowTrackingUri() {
-        return mlflowTrackingUri;
-    }
-
-    public void setMlflowJobType(String mlflowJobType) {
-        this.mlflowJobType = mlflowJobType;
-    }
-
-    public String getMlflowJobType() {
-        return mlflowJobType;
-    }
-
-    public void setAutomlTool(String automlTool) {
-        this.automlTool = automlTool;
-    }
-
-    public String getMlflowProjectRepository() {
-        return mlflowProjectRepository;
-    }
-
-    public void setMlflowProjectRepository(String mlflowProjectRepository) {
-        this.mlflowProjectRepository = mlflowProjectRepository;
-    }
-
-    public String getMlflowProjectVersion() {
-        return mlflowProjectVersion;
-    }
-
-    public void setMlflowProjectVersion(String mlflowProjectVersion) {
-        this.mlflowProjectVersion = mlflowProjectVersion;
-    }
-
-    public String getAutomlTool() {
-        return automlTool;
-    }
-
-    public void setDeployType(String deployType) {
-        this.deployType = deployType;
-    }
-
-    public String getDeployType() {
-        return deployType;
-    }
-
-    public void setDeployModelKey(String deployModelKey) {
-        this.deployModelKey = deployModelKey;
-    }
-
-    public String getDeployModelKey() {
-        return deployModelKey;
-    }
-
-    public void setDeployPort(String deployPort) {
-        this.deployPort = deployPort;
-    }
-
-    public String getDeployPort() {
-        return deployPort;
-    }
-
-    public void setCpuLimit(String cpuLimit) {
-        this.cpuLimit = cpuLimit;
-    }
-
-    public String getCpuLimit() {
-        return cpuLimit;
-    }
-
-    public void setMemoryLimit(String memoryLimit) {
-        this.memoryLimit = memoryLimit;
-    }
-
-    public String getMemoryLimit() {
-        return memoryLimit;
-    }
-
     @Override
     public boolean checkParameters() {
-        Boolean checkResult = true;
-//        Boolean checkResult = mlflowTrackingUri != null;
-//        if (mlflowJobType.equals(MlflowConstants.JOB_TYPE_BASIC_ALGORITHM)) {
-//            checkResult &= dataPath != null;
-//            checkResult &= experimentName != null;
-//        } else if (mlflowJobType.equals(MlflowConstants.JOB_TYPE_AUTOML)) {
-//            checkResult &= dataPath != null;
-//            checkResult &= automlTool != null;
-//            checkResult &= experimentName != null;
-//        } else {
-//        }
-        return checkResult;
+        return StringUtils.isNotEmpty(mlflowTrackingUri);
     }
 
     public HashMap<String, String> getParamsMap() {
@@ -240,11 +93,13 @@ public class MlflowParameters extends AbstractParameters {
         paramsMap.put("experiment_name", experimentName);
         paramsMap.put("model_name", modelName);
         paramsMap.put("MLFLOW_TRACKING_URI", mlflowTrackingUri);
-        if (mlflowJobType.equals(MlflowConstants.JOB_TYPE_BASIC_ALGORITHM)) {
-            addParamsMapForBasicAlgorithm(paramsMap);
-        } else if (mlflowJobType.equals(MlflowConstants.JOB_TYPE_AUTOML)) {
-            getParamsMapForAutoML(paramsMap);
-        } else {
+        switch (mlflowJobType){
+            case MlflowConstants.JOB_TYPE_BASIC_ALGORITHM:
+                addParamsMapForBasicAlgorithm(paramsMap);
+                break;
+            case MlflowConstants.JOB_TYPE_AUTOML:
+                getParamsMapForAutoML(paramsMap);
+                break;
         }
         return paramsMap;
     }
@@ -262,6 +117,10 @@ public class MlflowParameters extends AbstractParameters {
         paramsMap.put("repo_version", MlflowConstants.PRESET_REPOSITORY_VERSION);
     }
 
+    public Boolean isCustomProject() {
+        return mlflowJobType.equals(MlflowConstants.JOB_TYPE_CUSTOM_PROJECT);
+    }
+
     public String getModelKeyName(String tag) throws IllegalArgumentException {
         String imageName;
         if (deployModelKey.startsWith("runs:")) {
@@ -271,23 +130,18 @@ public class MlflowParameters extends AbstractParameters {
         } else {
             throw new IllegalArgumentException("model key must start with runs:/ or models:/ ");
         }
-        imageName = imageName.replace("/", tag);
+        imageName = imageName.replace("/", tag).toLowerCase();
         return imageName;
     }
 
-    public String getDockerComposeEnvCommand() {
-        String imageName = "mlflow/" + getModelKeyName(":");
-        String env = String.format(MlflowConstants.SET_DOCKER_COMPOSE_ENV, imageName, getContainerName(), deployPort, cpuLimit, memoryLimit);
-        return env;
-    }
-
     public String getContainerName(){
-        String containerName = "ds-mlflow-" + getModelKeyName("-");
-        return containerName;
+        return "ds-mlflow-" + getModelKeyName("-");
     }
 
     public boolean getIsDeployDocker(){
-        return deployType.equals(MlflowConstants.MLFLOW_MODELS_DEPLOY_TYPE_DOCKER) || deployType.equals(MlflowConstants.MLFLOW_MODELS_DEPLOY_TYPE_DOCKER_COMPOSE);
+        if (StringUtils.isEmpty(deployType)) {
+            return false;
+        }
+        return deployType.equals(MlflowConstants.MLFLOW_MODELS_DEPLOY_TYPE_DOCKER);
     }
-
-};
+}

dolphinscheduler-task-plugin/dolphinscheduler-task-mlflow/src/main/java/org/apache/dolphinscheduler/plugin/task/mlflow/MlflowTask.java

@@ -17,6 +17,8 @@
 
 package org.apache.dolphinscheduler.plugin.task.mlflow;
 
+import static org.apache.dolphinscheduler.plugin.task.api.TaskConstants.EXIT_CODE_FAILURE;
+
 import org.apache.dolphinscheduler.common.thread.ThreadUtils;
 import org.apache.dolphinscheduler.plugin.task.api.AbstractTaskExecutor;
 import org.apache.dolphinscheduler.plugin.task.api.ShellCommandExecutor;
@@ -25,28 +27,24 @@ import org.apache.dolphinscheduler.plugin.task.api.TaskException;
 import org.apache.dolphinscheduler.plugin.task.api.TaskExecutionContext;
 import org.apache.dolphinscheduler.plugin.task.api.model.Property;
 import org.apache.dolphinscheduler.plugin.task.api.model.TaskResponse;
-import org.apache.dolphinscheduler.plugin.task.api.parameters.AbstractParameters;
 import org.apache.dolphinscheduler.plugin.task.api.parser.ParamUtils;
 import org.apache.dolphinscheduler.plugin.task.api.parser.ParameterUtils;
 import org.apache.dolphinscheduler.plugin.task.api.utils.OSUtils;
 import org.apache.dolphinscheduler.spi.utils.JSONUtils;
+import org.apache.dolphinscheduler.spi.utils.PropertyUtils;
+import org.apache.dolphinscheduler.spi.utils.StringUtils;
 
 import java.util.ArrayList;
 import java.util.List;
 import java.util.Map;
-
-import static org.apache.dolphinscheduler.plugin.task.api.TaskConstants.EXIT_CODE_FAILURE;
+import java.util.regex.Pattern;
 
 /**
  * shell task
  */
 public class MlflowTask extends AbstractTaskExecutor {
 
-    /**
-     * shell parameters
-     */
-    private MlflowParameters mlflowParameters;
-
+    private static final Pattern GIT_CHECK_PATTERN = Pattern.compile("^(git@|https?://)");
     /**
      * shell command executor
      */
@@ -56,6 +54,10 @@ public class MlflowTask extends AbstractTaskExecutor {
      * taskExecutionContext
      */
     private final TaskExecutionContext taskExecutionContext;
+    /**
+     * shell parameters
+     */
+    private MlflowParameters mlflowParameters;
 
     /**
      * constructor
@@ -69,6 +71,34 @@ public class MlflowTask extends AbstractTaskExecutor {
         this.shellCommandExecutor = new ShellCommandExecutor(this::logHandle, taskExecutionContext, logger);
     }
 
+    static public String getPresetRepository() {
+        String presetRepository = PropertyUtils.getString(MlflowConstants.PRESET_REPOSITORY_KEY);
+        if (StringUtils.isEmpty(presetRepository)) {
+            presetRepository = MlflowConstants.PRESET_REPOSITORY;
+        }
+        return presetRepository;
+    }
+
+    static public String getPresetRepositoryVersion() {
+        String version = PropertyUtils.getString(MlflowConstants.PRESET_REPOSITORY_VERSION_KEY);
+        if (StringUtils.isEmpty(version)) {
+            version = MlflowConstants.PRESET_REPOSITORY_VERSION;
+        }
+        return version;
+    }
+
+    static public String getVersionString(String version, String repository) {
+        String versionString;
+        if (StringUtils.isEmpty(version)) {
+            versionString = "";
+        } else if (GIT_CHECK_PATTERN.matcher(repository).find()) {
+            versionString = String.format("--version=%s", version);
+        } else {
+            versionString = "";
+        }
+        return versionString;
+    }
+
     @Override
     public void init() {
         logger.info("shell task params {}", taskExecutionContext.getTaskParams());
@@ -137,43 +167,59 @@ public class MlflowTask extends AbstractTaskExecutor {
         args.add(String.format(MlflowConstants.EXPORT_MLFLOW_TRACKING_URI_ENV, mlflowParameters.getMlflowTrackingUri()));
 
         String runCommand;
+        String versionString;
 
-        if (mlflowParameters.getMlflowJobType().equals(MlflowConstants.JOB_TYPE_BASIC_ALGORITHM)) {
-            args.add(String.format(MlflowConstants.SET_DATA_PATH, mlflowParameters.getDataPath()));
-            args.add(String.format(MlflowConstants.SET_REPOSITORY, MlflowConstants.PRESET_BASIC_ALGORITHM_PROJECT));
-            args.add(String.format(MlflowConstants.GIT_CLONE_REPO, MlflowConstants.PRESET_REPOSITORY, MlflowConstants.PRESET_PATH));
+        if (mlflowParameters.isCustomProject()) {
+            versionString = getVersionString(mlflowParameters.getMlflowProjectVersion(), mlflowParameters.getMlflowProjectRepository());
+        } else {
+            versionString = getVersionString(getPresetRepositoryVersion(), getPresetRepository());
+        }
 
-            runCommand = MlflowConstants.MLFLOW_RUN_BASIC_ALGORITHM;
-            runCommand = String.format(runCommand, mlflowParameters.getAlgorithm(), mlflowParameters.getParams(), mlflowParameters.getSearchParams(), mlflowParameters.getModelName(),
-                mlflowParameters.getExperimentName());
 
-        } else if (mlflowParameters.getMlflowJobType().equals(MlflowConstants.JOB_TYPE_AUTOML)) {
-            args.add(String.format(MlflowConstants.SET_DATA_PATH, mlflowParameters.getDataPath()));
-            args.add(String.format(MlflowConstants.SET_REPOSITORY, MlflowConstants.PRESET_AUTOML_PROJECT));
-            args.add(String.format(MlflowConstants.GIT_CLONE_REPO, MlflowConstants.PRESET_REPOSITORY, MlflowConstants.PRESET_PATH));
+        switch (mlflowParameters.getMlflowJobType()) {
+            case MlflowConstants.JOB_TYPE_BASIC_ALGORITHM:
+                args.add(String.format(MlflowConstants.SET_DATA_PATH, mlflowParameters.getDataPath()));
 
-            runCommand = MlflowConstants.MLFLOW_RUN_AUTOML_PROJECT;
-            runCommand = String.format(runCommand, mlflowParameters.getAutomlTool(), mlflowParameters.getParams(), mlflowParameters.getModelName(), mlflowParameters.getExperimentName());
+                String repoBasicAlgorithm = getPresetRepository() + MlflowConstants.PRESET_BASIC_ALGORITHM_PROJECT;
+                args.add(String.format(MlflowConstants.SET_REPOSITORY, repoBasicAlgorithm));
 
-        } else if (mlflowParameters.getMlflowJobType().equals(MlflowConstants.JOB_TYPE_CUSTOM_PROJECT)) {
-            args.add(String.format(MlflowConstants.SET_REPOSITORY, mlflowParameters.getMlflowProjectRepository()));
+                runCommand = MlflowConstants.MLFLOW_RUN_BASIC_ALGORITHM;
+                runCommand = String.format(runCommand, mlflowParameters.getAlgorithm(), mlflowParameters.getParams(), mlflowParameters.getSearchParams(), mlflowParameters.getModelName(),
+                    mlflowParameters.getExperimentName());
+                break;
 
-            runCommand = MlflowConstants.MLFLOW_RUN_CUSTOM_PROJECT;
-            runCommand = String.format(runCommand, mlflowParameters.getParams(), mlflowParameters.getExperimentName(), mlflowParameters.getMlflowProjectVersion());
-        } else {
-            runCommand = String.format("Cant not Support %s", mlflowParameters.getMlflowJobType());
+            case MlflowConstants.JOB_TYPE_AUTOML:
+                args.add(String.format(MlflowConstants.SET_DATA_PATH, mlflowParameters.getDataPath()));
+                String repoAutoML = getPresetRepository() + MlflowConstants.PRESET_AUTOML_PROJECT;
+                args.add(String.format(MlflowConstants.SET_REPOSITORY, repoAutoML));
 
+                runCommand = MlflowConstants.MLFLOW_RUN_AUTOML_PROJECT;
+                runCommand = String.format(runCommand, mlflowParameters.getAutomlTool(), mlflowParameters.getParams(), mlflowParameters.getModelName(), mlflowParameters.getExperimentName());
+                break;
+
+            case MlflowConstants.JOB_TYPE_CUSTOM_PROJECT:
+                args.add(String.format(MlflowConstants.SET_REPOSITORY, mlflowParameters.getMlflowProjectRepository()));
+                runCommand = MlflowConstants.MLFLOW_RUN_CUSTOM_PROJECT;
+                runCommand = String.format(runCommand, mlflowParameters.getParams(), mlflowParameters.getExperimentName());
+                break;
+
+            default:
+                throw new TaskException("Unsupported mlflow job type: " + mlflowParameters.getMlflowJobType());
+        }
+
+        // add version string to command if repository is local path
+        if (StringUtils.isNotEmpty(versionString)) {
+            runCommand = runCommand + " " + versionString;
         }
         args.add(runCommand);
 
-        String command = ParameterUtils.convertParameterPlaceholders(String.join("\n", args), ParamUtils.convert(paramsMap));
-        return command;
+        return ParameterUtils.convertParameterPlaceholders(String.join("\n", args), ParamUtils.convert(paramsMap));
     }
 
+    /**
+     * build mlflow models command
+     */
     protected String buildCommandForMlflowModels() {
-        /**
-         * build mlflow models command
-         */
 
         Map<String, Property> paramsMap = getParamsMap();
         List<String> args = new ArrayList<>();
@@ -190,20 +236,9 @@ public class MlflowTask extends AbstractTaskExecutor {
             args.add(String.format(MlflowConstants.MLFLOW_BUILD_DOCKER, deployModelKey, imageName));
             args.add(String.format(MlflowConstants.DOCKER_RREMOVE_CONTAINER, containerName));
             args.add(String.format(MlflowConstants.DOCKER_RUN, containerName, mlflowParameters.getDeployPort(), imageName));
-        } else if (mlflowParameters.getDeployType().equals(MlflowConstants.MLFLOW_MODELS_DEPLOY_TYPE_DOCKER_COMPOSE)) {
-            String templatePath = getTemplatePath(MlflowConstants.TEMPLATE_DOCKER_COMPOSE);
-            args.add(String.format("cp %s %s", templatePath, taskExecutionContext.getExecutePath()));
-            String imageName = "mlflow/" + mlflowParameters.getModelKeyName(":");
-            String containerName = mlflowParameters.getContainerName();
-
-            args.add(String.format(MlflowConstants.MLFLOW_BUILD_DOCKER, deployModelKey, imageName));
-            args.add(String.format(MlflowConstants.DOCKER_RREMOVE_CONTAINER, containerName));
-            args.add(mlflowParameters.getDockerComposeEnvCommand());
-            args.add(MlflowConstants.DOCKER_COMPOSE_RUN);
         }
 
-        String command = ParameterUtils.convertParameterPlaceholders(String.join("\n", args), ParamUtils.convert(paramsMap));
-        return command;
+        return ParameterUtils.convertParameterPlaceholders(String.join("\n", args), ParamUtils.convert(paramsMap));
     }
 
     private Map<String, Property> getParamsMap() {
@@ -212,7 +247,7 @@ public class MlflowTask extends AbstractTaskExecutor {
 
     }
 
-    public int checkDockerHealth() throws Exception {
+    public int checkDockerHealth() {
         logger.info("checking container healthy ... ");
         int exitCode = -1;
         String[] command = {"sh", "-c", String.format(MlflowConstants.DOCKER_HEALTH_CHECK, mlflowParameters.getContainerName())};
@@ -240,13 +275,8 @@ public class MlflowTask extends AbstractTaskExecutor {
     }
 
     @Override
-    public AbstractParameters getParameters() {
+    public MlflowParameters getParameters() {
         return mlflowParameters;
     }
 
-    public String getTemplatePath(String template) {
-        String templatePath = MlflowTask.class.getClassLoader().getResource(template).getPath();
-        return templatePath;
-    }
-
 }

dolphinscheduler-task-plugin/dolphinscheduler-task-mlflow/src/main/resources/docker-compose.yml

@@ -1,39 +0,0 @@
-# Licensed to the Apache Software Foundation (ASF) under one
-# or more contributor license agreements.  See the NOTICE file
-# distributed with this work for additional information
-# regarding copyright ownership.  The ASF licenses this file
-# to you under the Apache License, Version 2.0 (the
-# "License"); you may not use this file except in compliance
-# with the License.  You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-version: "3"
-
-services:
-  mlflow-model:
-    image: "${DS_TASK_MLFLOW_IMAGE_NAME}"
-    container_name: "${DS_TASK_MLFLOW_CONTAINER_NAME}"
-    ports:
-      - "${DS_TASK_MLFLOW_DEPLOY_PORT}:8080"
-    deploy:
-      resources:
-        limits:
-          cpus: "${DS_TASK_MLFLOW_CPU_LIMIT}"
-          memory: "${DS_TASK_MLFLOW_MEMORY_LIMIT}"
-
-    environment:
-      PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION: python
-
-
-    healthcheck:
-      test: ["CMD", "curl", "http://127.0.0.1:8080/ping"]
-      interval: 5s
-      timeout: 5s
-      retries: 5

dolphinscheduler-task-plugin/dolphinscheduler-task-mlflow/src/test/java/org/apache/dolphinler/plugin/task/mlflow/MlflowTaskTest.java

@@ -17,6 +17,8 @@
 
 package org.apache.dolphinler.plugin.task.mlflow;
 
+import static org.powermock.api.mockito.PowerMockito.when;
+
 import org.apache.dolphinscheduler.plugin.task.api.TaskExecutionContext;
 import org.apache.dolphinscheduler.plugin.task.api.TaskExecutionContextCacheManager;
 import org.apache.dolphinscheduler.plugin.task.mlflow.MlflowConstants;
@@ -76,21 +78,46 @@ public class MlflowTaskTest {
         return taskExecutionContext;
     }
 
+    @Test
+    public void testGetPresetRepositoryData() {
+        Assert.assertEquals("https://github.com/apache/dolphinscheduler-mlflow", MlflowTask.getPresetRepository());
+
+        Assert.assertEquals("main", MlflowTask.getPresetRepositoryVersion());
+
+        String definedRepository = "https://github.com/<MY-ID>/dolphinscheduler-mlflow";
+        when(PropertyUtils.getString(MlflowConstants.PRESET_REPOSITORY_KEY)).thenAnswer(invocation -> definedRepository);
+        Assert.assertEquals(definedRepository, MlflowTask.getPresetRepository());
+
+        String definedRepositoryVersion = "dev";
+        when(PropertyUtils.getString(MlflowConstants.PRESET_REPOSITORY_VERSION_KEY)).thenAnswer(invocation -> definedRepositoryVersion);
+        Assert.assertEquals(definedRepositoryVersion, MlflowTask.getPresetRepositoryVersion());
+    }
+
+    @Test
+    public void testGetVersionString() {
+        Assert.assertEquals("--version=main", MlflowTask.getVersionString("main", "https://github.com/apache/dolphinscheduler-mlflow"));
+        Assert.assertEquals("--version=master", MlflowTask.getVersionString("master", "https://github.com/apache/dolphinscheduler-mlflow"));
+        Assert.assertEquals("--version=main", MlflowTask.getVersionString("main", "git@github.com:apache/dolphinscheduler-mlflow.git"));
+        Assert.assertEquals("--version=master", MlflowTask.getVersionString("master", "git@github.com:apache/dolphinscheduler-mlflow.git"));
+        Assert.assertEquals("", MlflowTask.getVersionString("main", "/tmp/dolphinscheduler-mlflow"));
+        Assert.assertEquals("", MlflowTask.getVersionString("master", "/tmp/dolphinscheduler-mlflow"));
+    }
+
     @Test
     public void testInitBasicAlgorithmTask() {
         MlflowTask mlflowTask = initTask(createBasicAlgorithmParameters());
         Assert.assertEquals(mlflowTask.buildCommand(),
             "export MLFLOW_TRACKING_URI=http://127.0.0.1:5000\n"
                 + "data_path=/data/iris.csv\n"
-                + "repo=dolphinscheduler-mlflow#Project-BasicAlgorithm\n"
-                + "git clone https://github.com/apache/dolphinscheduler-mlflow dolphinscheduler-mlflow\n"
+                + "repo=https://github.com/apache/dolphinscheduler-mlflow#Project-BasicAlgorithm\n"
                 + "mlflow run $repo "
                 + "-P algorithm=xgboost "
                 + "-P data_path=$data_path "
                 + "-P params=\"n_estimators=100\" "
                 + "-P search_params=\"\" "
                 + "-P model_name=\"BasicAlgorithm\" "
-                + "--experiment-name=\"BasicAlgorithm\"");
+                + "--experiment-name=\"BasicAlgorithm\" "
+                + "--version=main");
     }
 
     @Test
@@ -99,19 +126,32 @@ public class MlflowTaskTest {
         Assert.assertEquals(mlflowTask.buildCommand(),
             "export MLFLOW_TRACKING_URI=http://127.0.0.1:5000\n"
                 + "data_path=/data/iris.csv\n"
-                + "repo=dolphinscheduler-mlflow#Project-AutoML\n"
-                + "git clone https://github.com/apache/dolphinscheduler-mlflow dolphinscheduler-mlflow\n"
+                + "repo=https://github.com/apache/dolphinscheduler-mlflow#Project-AutoML\n"
                 + "mlflow run $repo "
                 + "-P tool=autosklearn "
                 + "-P data_path=$data_path "
                 + "-P params=\"time_left_for_this_task=30\" "
                 + "-P model_name=\"AutoML\" "
-                + "--experiment-name=\"AutoML\"");
+                + "--experiment-name=\"AutoML\" "
+                + "--version=main");
     }
 
     @Test
     public void testInitCustomProjectTask() {
         MlflowTask mlflowTask = initTask(createCustomProjectParameters());
+
+        // Version will be set if parameter.mlflowProjectVersion is empty
+        Assert.assertEquals(mlflowTask.buildCommand(),
+            "export MLFLOW_TRACKING_URI=http://127.0.0.1:5000\n"
+                + "repo=https://github.com/mlflow/mlflow#examples/xgboost/xgboost_native\n"
+                + "mlflow run $repo "
+                + "-P learning_rate=0.2 "
+                + "-P colsample_bytree=0.8 "
+                + "-P subsample=0.9 "
+                + "--experiment-name=\"custom_project\"");
+
+        // Version will be set if repository is remote path
+        mlflowTask.getParameters().setMlflowProjectVersion("dev");
         Assert.assertEquals(mlflowTask.buildCommand(),
             "export MLFLOW_TRACKING_URI=http://127.0.0.1:5000\n"
                 + "repo=https://github.com/mlflow/mlflow#examples/xgboost/xgboost_native\n"
@@ -120,7 +160,19 @@ public class MlflowTaskTest {
                 + "-P colsample_bytree=0.8 "
                 + "-P subsample=0.9 "
                 + "--experiment-name=\"custom_project\" "
-                + "--version=\"master\" ");
+                + "--version=dev");
+
+        // Version will not be set if repository is local path
+        mlflowTask.getParameters().setMlflowProjectRepository("/tmp/dolphinscheduler-mlflow");
+        Assert.assertEquals(mlflowTask.buildCommand(),
+            "export MLFLOW_TRACKING_URI=http://127.0.0.1:5000\n"
+                + "repo=/tmp/dolphinscheduler-mlflow\n"
+                + "mlflow run $repo "
+                + "-P learning_rate=0.2 "
+                + "-P colsample_bytree=0.8 "
+                + "-P subsample=0.9 "
+                + "--experiment-name=\"custom_project\"");
+
     }
 
     @Test
@@ -143,24 +195,6 @@ public class MlflowTaskTest {
                 + "mlflow/model:1");
     }
 
-    @Test
-    public void testModelsDeployDockerCompose() throws Exception {
-        MlflowTask mlflowTask = initTask(createModelDeplyDockerComposeParameters());
-        Assert.assertEquals(mlflowTask.buildCommand(),
-            "export MLFLOW_TRACKING_URI=http://127.0.0.1:5000\n"
-                + "cp "
-                + mlflowTask.getTemplatePath(MlflowConstants.TEMPLATE_DOCKER_COMPOSE)
-                + " /tmp/dolphinscheduler_test\n"
-                + "mlflow models build-docker -m models:/model/1 -n mlflow/model:1 --enable-mlserver\n"
-                + "docker rm -f ds-mlflow-model-1\n"
-                + "export DS_TASK_MLFLOW_IMAGE_NAME=mlflow/model:1\n"
-                + "export DS_TASK_MLFLOW_CONTAINER_NAME=ds-mlflow-model-1\n"
-                + "export DS_TASK_MLFLOW_DEPLOY_PORT=7000\n"
-                + "export DS_TASK_MLFLOW_CPU_LIMIT=0.5\n"
-                + "export DS_TASK_MLFLOW_MEMORY_LIMIT=200m\n"
-                + "docker-compose up -d");
-    }
-
     private MlflowTask initTask(MlflowParameters mlflowParameters) {
         TaskExecutionContext taskExecutionContext = createContext(mlflowParameters);
         MlflowTask mlflowTask = new MlflowTask(taskExecutionContext);
@@ -174,11 +208,11 @@ public class MlflowTaskTest {
         mlflowParameters.setMlflowTaskType(MlflowConstants.MLFLOW_TASK_TYPE_PROJECTS);
         mlflowParameters.setMlflowJobType(MlflowConstants.JOB_TYPE_BASIC_ALGORITHM);
         mlflowParameters.setAlgorithm("xgboost");
-        mlflowParameters.setDataPaths("/data/iris.csv");
+        mlflowParameters.setDataPath("/data/iris.csv");
         mlflowParameters.setParams("n_estimators=100");
-        mlflowParameters.setExperimentNames("BasicAlgorithm");
-        mlflowParameters.setModelNames("BasicAlgorithm");
-        mlflowParameters.setMlflowTrackingUris("http://127.0.0.1:5000");
+        mlflowParameters.setExperimentName("BasicAlgorithm");
+        mlflowParameters.setModelName("BasicAlgorithm");
+        mlflowParameters.setMlflowTrackingUri("http://127.0.0.1:5000");
         return mlflowParameters;
     }
 
@@ -188,10 +222,10 @@ public class MlflowTaskTest {
         mlflowParameters.setMlflowJobType(MlflowConstants.JOB_TYPE_AUTOML);
         mlflowParameters.setAutomlTool("autosklearn");
         mlflowParameters.setParams("time_left_for_this_task=30");
-        mlflowParameters.setDataPaths("/data/iris.csv");
-        mlflowParameters.setExperimentNames("AutoML");
-        mlflowParameters.setModelNames("AutoML");
-        mlflowParameters.setMlflowTrackingUris("http://127.0.0.1:5000");
+        mlflowParameters.setDataPath("/data/iris.csv");
+        mlflowParameters.setExperimentName("AutoML");
+        mlflowParameters.setModelName("AutoML");
+        mlflowParameters.setMlflowTrackingUri("http://127.0.0.1:5000");
         return mlflowParameters;
     }
 
@@ -199,8 +233,8 @@ public class MlflowTaskTest {
         MlflowParameters mlflowParameters = new MlflowParameters();
         mlflowParameters.setMlflowTaskType(MlflowConstants.MLFLOW_TASK_TYPE_PROJECTS);
         mlflowParameters.setMlflowJobType(MlflowConstants.JOB_TYPE_CUSTOM_PROJECT);
-        mlflowParameters.setMlflowTrackingUris("http://127.0.0.1:5000");
-        mlflowParameters.setExperimentNames("custom_project");
+        mlflowParameters.setMlflowTrackingUri("http://127.0.0.1:5000");
+        mlflowParameters.setExperimentName("custom_project");
         mlflowParameters.setParams("-P learning_rate=0.2 -P colsample_bytree=0.8 -P subsample=0.9");
         mlflowParameters.setMlflowProjectRepository("https://github.com/mlflow/mlflow#examples/xgboost/xgboost_native");
 
@@ -211,7 +245,7 @@ public class MlflowTaskTest {
         MlflowParameters mlflowParameters = new MlflowParameters();
         mlflowParameters.setMlflowTaskType(MlflowConstants.MLFLOW_TASK_TYPE_MODELS);
         mlflowParameters.setDeployType(MlflowConstants.MLFLOW_MODELS_DEPLOY_TYPE_MLFLOW);
-        mlflowParameters.setMlflowTrackingUris("http://127.0.0.1:5000");
+        mlflowParameters.setMlflowTrackingUri("http://127.0.0.1:5000");
         mlflowParameters.setDeployModelKey("models:/model/1");
         mlflowParameters.setDeployPort("7000");
         return mlflowParameters;
@@ -221,21 +255,9 @@ public class MlflowTaskTest {
         MlflowParameters mlflowParameters = new MlflowParameters();
         mlflowParameters.setMlflowTaskType(MlflowConstants.MLFLOW_TASK_TYPE_MODELS);
         mlflowParameters.setDeployType(MlflowConstants.MLFLOW_MODELS_DEPLOY_TYPE_DOCKER);
-        mlflowParameters.setMlflowTrackingUris("http://127.0.0.1:5000");
-        mlflowParameters.setDeployModelKey("models:/model/1");
-        mlflowParameters.setDeployPort("7000");
-        return mlflowParameters;
-    }
-
-    private MlflowParameters createModelDeplyDockerComposeParameters() {
-        MlflowParameters mlflowParameters = new MlflowParameters();
-        mlflowParameters.setMlflowTaskType(MlflowConstants.MLFLOW_TASK_TYPE_MODELS);
-        mlflowParameters.setDeployType(MlflowConstants.MLFLOW_MODELS_DEPLOY_TYPE_DOCKER_COMPOSE);
-        mlflowParameters.setMlflowTrackingUris("http://127.0.0.1:5000");
+        mlflowParameters.setMlflowTrackingUri("http://127.0.0.1:5000");
         mlflowParameters.setDeployModelKey("models:/model/1");
         mlflowParameters.setDeployPort("7000");
-        mlflowParameters.setCpuLimit("0.5");
-        mlflowParameters.setMemoryLimit("200m");
         return mlflowParameters;
     }
 }

dolphinscheduler-ui/src/locales/en_US/project.ts

@@ -710,7 +710,7 @@ export default {
     mlflow_deployModelKey: 'Model-URI',
     mlflow_deployPort: 'Port',
     mlflowProjectRepository: 'Repository',
-    mlflowProjectRepository_tips: 'github respository or path on worker',
+    mlflowProjectRepository_tips: 'git respository or path on worker',
     mlflowProjectVersion: 'Project Version',
     mlflowProjectVersion_tips: 'git version',
     mlflow_cpuLimit: 'Max Cpu Limit',

dolphinscheduler-ui/src/locales/zh_CN/project.ts

@@ -693,7 +693,7 @@ export default {
     mlflow_deployModelKey: '部署的模型URI',
     mlflow_deployPort: '监听端口',
     mlflowProjectRepository: '运行仓库',
-    mlflowProjectRepository_tips: '可以为github仓库或worker上的路径',
+    mlflowProjectRepository_tips: '可以为git仓库或worker上的路径',
     mlflowProjectVersion: '项目版本',
     mlflowProjectVersion_tips: '项目git版本',
     mlflow_cpuLimit: '最大cpu限制',

dolphinscheduler-ui/src/views/projects/task/components/node/fields/use-mlflow-models.ts

@@ -23,8 +23,6 @@ export function useMlflowModels(model: { [field: string]: any }): IJsonItem[] {
   const deployTypeSpan = ref(0)
   const deployModelKeySpan = ref(0)
   const deployPortSpan = ref(0)
-  const cpuLimitSpan = ref(0)
-  const memoryLimitSpan = ref(0)
 
   const setFlag = () => {
     model.isModels = model.mlflowTaskType === 'MLflow Models' ? true : false
@@ -44,14 +42,6 @@ export function useMlflowModels(model: { [field: string]: any }): IJsonItem[] {
     }
   )
 
-  watch(
-    () => [model.deployType],
-    () => {
-      cpuLimitSpan.value = model.deployType === 'DOCKER COMPOSE' ? 12 : 0
-      memoryLimitSpan.value = model.deployType === 'DOCKER COMPOSE' ? 12 : 0
-    }
-  )
-
   setFlag()
   resetSpan()
 
@@ -74,18 +64,6 @@ export function useMlflowModels(model: { [field: string]: any }): IJsonItem[] {
       field: 'deployPort',
       name: t('project.node.mlflow_deployPort'),
       span: deployPortSpan
-    },
-    {
-      type: 'input',
-      field: 'cpuLimit',
-      name: t('project.node.mlflow_cpuLimit'),
-      span: cpuLimitSpan
-    },
-    {
-      type: 'input',
-      field: 'memoryLimit',
-      name: t('project.node.mlflow_memoryLimit'),
-      span: memoryLimitSpan
     }
   ]
 }
@@ -98,9 +76,5 @@ const DEPLOY_TYPE = [
   {
     label: 'DOCKER',
     value: 'DOCKER'
-  },
-  {
-    label: 'DOCKER COMPOSE',
-    value: 'DOCKER COMPOSE'
   }
 ]

dolphinscheduler-ui/src/views/projects/task/components/node/fields/use-mlflow-projects.ts

@@ -280,16 +280,16 @@ export function useCustomProject(model: { [field: string]: any }): IJsonItem[] {
 
 export const MLFLOW_JOB_TYPE = [
   {
-    label: 'BasicAlgorithm',
-    value: 'BasicAlgorithm'
+    label: 'Custom Project',
+    value: 'CustomProject'
   },
   {
     label: 'AutoML',
     value: 'AutoML'
   },
   {
-    label: 'Custom Project',
-    value: 'CustomProject'
+    label: 'BasicAlgorithm',
+    value: 'BasicAlgorithm'
   }
 ]
 export const ALGORITHM = [
@@ -311,12 +311,12 @@ export const ALGORITHM = [
   }
 ]
 export const AutoMLTOOL = [
-  {
-    label: 'autosklearn',
-    value: 'autosklearn'
-  },
   {
     label: 'flaml',
     value: 'flaml'
+  },
+  {
+    label: 'autosklearn',
+    value: 'autosklearn'
   }
 ]

dolphinscheduler-ui/src/views/projects/task/components/node/fields/use-mlflow.ts

@@ -17,6 +17,7 @@
 import { useI18n } from 'vue-i18n'
 import type { IJsonItem } from '../types'
 import { useMlflowProjects, useMlflowModels } from '.'
+import { useCustomParams, useResources } from '.'
 
 export const MLFLOW_TASK_TYPE = [
   {
@@ -61,6 +62,8 @@ export function useMlflow(model: { [field: string]: any }): IJsonItem[] {
       options: MLFLOW_TASK_TYPE
     },
     ...useMlflowProjects(model),
-    ...useMlflowModels(model)
+    ...useMlflowModels(model),
+    useResources(),
+    ...useCustomParams({ model, field: 'localParams', isSimple: true })
   ]
 }

dolphinscheduler-ui/src/views/projects/task/components/node/format-data.ts

@@ -369,8 +369,6 @@ export function formatParams(data: INodeData): {
     taskParams.deployModelKey = data.deployModelKey
     taskParams.mlflowProjectRepository = data.mlflowProjectRepository
     taskParams.mlflowProjectVersion = data.mlflowProjectVersion
-    taskParams.cpuLimit = data.cpuLimit
-    taskParams.memoryLimit = data.memoryLimit
   }
 
   if (data.taskType === 'DVC') {

dolphinscheduler-ui/src/views/projects/task/components/node/tasks/use-mlflow.ts

@@ -47,10 +47,7 @@ export function useMlflow({
     deployType: 'MLFLOW',
     deployPort: '7000',
     mlflowJobType: 'CustomProject',
-    mlflowProjectVersion: 'master',
     automlTool: 'flaml',
-    cpuLimit: '0.5',
-    memoryLimit: '500M',
     mlflowCustomProjectParameters: [],
     delayTime: 0,
     timeout: 30,