| # 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. |
| |
| # [start workflow_declare] |
| """A example workflow for task mlflow.""" |
| |
| from pydolphinscheduler.core.workflow import Workflow |
| from pydolphinscheduler.tasks.mlflow import ( |
| MLflowDeployType, |
| MLflowModels, |
| MLFlowProjectsAutoML, |
| MLFlowProjectsBasicAlgorithm, |
| MLFlowProjectsCustom, |
| ) |
| |
| mlflow_tracking_uri = "http://127.0.0.1:5000" |
| |
| with Workflow( |
| name="task_mlflow_example", |
| ) as workflow: |
| # run custom mlflow project to train model |
| train_custom = MLFlowProjectsCustom( |
| name="train_xgboost_native", |
| repository="https://github.com/mlflow/mlflow#examples/xgboost/xgboost_native", |
| mlflow_tracking_uri=mlflow_tracking_uri, |
| parameters="-P learning_rate=0.2 -P colsample_bytree=0.8 -P subsample=0.9", |
| experiment_name="xgboost", |
| ) |
| |
| # run automl to train model |
| train_automl = MLFlowProjectsAutoML( |
| name="train_automl", |
| mlflow_tracking_uri=mlflow_tracking_uri, |
| parameters="time_budget=30;estimator_list=['lgbm']", |
| experiment_name="automl_iris", |
| model_name="iris_A", |
| automl_tool="flaml", |
| data_path="/data/examples/iris", |
| ) |
| |
| # Using DOCKER to deploy model from train_automl |
| deploy_docker = MLflowModels( |
| name="deploy_docker", |
| model_uri="models:/iris_A/Production", |
| mlflow_tracking_uri=mlflow_tracking_uri, |
| deploy_mode=MLflowDeployType.DOCKER, |
| port=7002, |
| ) |
| |
| train_automl >> deploy_docker |
| |
| # run lightgbm to train model |
| train_basic_algorithm = MLFlowProjectsBasicAlgorithm( |
| name="train_basic_algorithm", |
| mlflow_tracking_uri=mlflow_tracking_uri, |
| parameters="n_estimators=200;learning_rate=0.2", |
| experiment_name="basic_algorithm_iris", |
| model_name="iris_B", |
| algorithm="lightgbm", |
| data_path="/data/examples/iris", |
| search_params="max_depth=[5, 10];n_estimators=[100, 200]", |
| ) |
| |
| # 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.MLFLOW, |
| port=7001, |
| ) |
| |
| train_basic_algorithm >> deploy_mlflow |
| |
| workflow.submit() |
| |
| # [end workflow_declare] |