zhongjiajie commented on code in PR #10036: URL: https://github.com/apache/dolphinscheduler/pull/10036#discussion_r873163428
########## docs/docs/en/guide/task/mlflow.md: ########## @@ -0,0 +1,117 @@ +# 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 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) + +## 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://127.0.0.1:5000. + - **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 + +#### Conda env + +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 ) + + + +Note During the configuration task, select the conda environment created above. Otherwise, the program cannot find the +Conda environment. + + + +#### 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. + +``` Review Comment: ```suggestion ```sh ``` ########## docs/docs/zh/guide/task/mlflow.md: ########## @@ -0,0 +1,97 @@ +# MLflow节点 + +## 综述 + +[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 任务用于执行 MLflow Project 任务，其中包含了阈值的基础算法能力与AutoML能力（将在不久将来支持用户自定义的mlflow project任务执行）。 + +## 创建任务 + +- 点击项目管理-项目名称-工作流定义，点击“创建工作流”按钮，进入 DAG 编辑页面； +- 拖动工具栏的 <img src="/img/tasks/icons/mlflow.png" width="15"/> 任务节点到画板中。 + +## 任务参数 + +- DS通用参数 + - **节点名称** ：设置任务的名称。一个工作流定义中的节点名称是唯一的。 + - **运行标志** ：标识这个节点是否能正常调度,如果不需要执行，可以打开禁止执行开关。 + - **描述** ：描述该节点的功能。 + - **任务优先级** ：worker 线程数不足时，根据优先级从高到低依次执行，优先级一样时根据先进先出原则执行。 + - **Worker 分组** ：任务分配给 worker 组的机器执行，选择 Default，会随机选择一台 worker 机执行。 + - **环境名称** ：配置运行脚本的环境。 + - **失败重试次数** ：任务失败重新提交的次数。 + - **失败重试间隔** ：任务失败重新提交任务的时间间隔，以分钟为单位。 + - **延迟执行时间** ：任务延迟执行的时间，以分钟为单位。 + - **超时告警** ：勾选超时告警、超时失败，当任务超过"超时时长"后，会发送告警邮件并且任务执行失败。 + - **自定义参数** ：是 mlflow 局部的用户自定义参数，会替换脚本中以 ${变量} 的内容 + - **前置任务** ：选择当前任务的前置任务，会将被选择的前置任务设置为当前任务的上游。 + +- MLflow任务特定参数 + - **mlflow server tracking uri** ：MLflow server 的连接, 默认 http://127.0.0.1:5000。 + - **实验名称** ：任务运行时所在的实验，若无则创建。 + - **注册模型** ：是否注册模型，若选择注册，则会展开以下参数。 + - **注册的模型名称** : 注册的模型名称，会在原来的基础上加上一个模型版本，并注册为Production。 + - **任务类型** : 运行的任务类型，目前包括基础算法与AutoML, 后续将会支持用户自定义的ML Project。 + - 基础算法下的特有参数 + - **算法** ：选择的算法，目前基于 [scikit-learn](https://scikit-learn.org/) 形式支持 `lr`, `svm`, `lightgbm`, `xgboost`. + - **参数搜索空间** : 运行对应算法的参数搜索空间, 可为空。如针对lightgbm 的 `max_depth=[5, 10];n_estimators=[100, 200]` + 则会进行对应搜索。约定传入后会以`;`切分各个参数，等号前的名字作为参数名，等号后的名字将以python eval执行得到对应的参数值 + - AutoML下的参数下的特有参数 + - **AutoML工具** : 使用的AutoML工具，目前支持 [autosklearn](https://github.com/automl/auto-sklearn) + , [flaml](https://github.com/microsoft/FLAML) + - BasicAlgorithm 和 AutoML共有参数 + - **数据路径** : 文件/文件夹的绝对路径, 若文件需以.csv结尾（自动切分训练集与测试集）, 文件夹需包含train.csv和test.csv（建议方式，用户应自行构建测试集用于模型评估）。 + - **参数** : 初始化模型/AutoML训练器时的参数，可为空, 如针对 flaml 设置`"time_budget=30;estimator_list=['lgbm']"`。约定传入后会以`;` + 切分各个参数，等号前的名字作为参数名，等号后的名字将以python eval执行得到对应的参数值。详细的参数列表如下: + - 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) + +## 任务样例 + +### 前置准备 + +#### conda 环境配置 + +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) ) + + + +后续注意配置任务时，环境选择上面创建的conda环境，否则程序会找不到conda环境 + + + +#### mlflow service 启动 + +确保你已经安装mlflow，可以使用`pip install mlflow`进行安装 + +在你想保存实验和模型的地方建立一个文件夹，然后启动 mlflow service + +``` Review Comment: ```suggestion ```sh ``` ########## dolphinscheduler-task-plugin/dolphinscheduler-task-mlflow/src/main/java/org/apache/dolphinscheduler/plugin/task/mlflow/MlflowConstants.java: ########## @@ -0,0 +1,38 @@ +/* + * 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. + */ + +package org.apache.dolphinscheduler.plugin.task.mlflow; + +public class MlflowConstants { + private MlflowConstants() { + throw new IllegalStateException("Utility class"); + } + + public static final String JOB_TYPE_AUTOML = "AutoML"; + + public static final String JOB_TYPE_BASIC_ALGORITHM = "BasicAlgorithm"; + + public static final String PRESET_AUTOML_PROJECT = "https://github.com/jieguangzhou/MLflow-AutoML";; + + public static final String PRESET_BASIC_ALGORITHM_PROJECT = "https://github.com/jieguangzhou/mlflow_sklearn_gallery";; Review Comment: I find you use your repo in our codebase, why? -- This is an automated message from the Apache Git Service. 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