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79 lines
2.5 KiB
79 lines
2.5 KiB
2 years ago
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# Licensed to the Apache Software Foundation (ASF) under one
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# or more contributor license agreements. See the NOTICE file
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# distributed with this work for additional information
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# regarding copyright ownership. The ASF licenses this file
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# to you under the Apache License, Version 2.0 (the
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# "License"); you may not use this file except in compliance
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# with the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing,
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# software distributed under the License is distributed on an
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# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
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# KIND, either express or implied. See the License for the
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# specific language governing permissions and limitations
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# under the License.
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# Define variable `mlflow_tracking_uri`
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mlflow_tracking_uri: &mlflow_tracking_uri "http://127.0.0.1:5000"
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# Define the workflow
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workflow:
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name: "MLflow"
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# Define the tasks under the workflow
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tasks:
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- name: train_xgboost_native
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task_type: MLFlowProjectsCustom
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repository: https://github.com/mlflow/mlflow#examples/xgboost/xgboost_native
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mlflow_tracking_uri: *mlflow_tracking_uri
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parameters: -P learning_rate=0.2 -P colsample_bytree=0.8 -P subsample=0.9
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experiment_name: xgboost
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- name: deploy_mlflow
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deps: [train_xgboost_native]
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task_type: MLflowModels
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model_uri: models:/xgboost_native/Production
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mlflow_tracking_uri: *mlflow_tracking_uri
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deploy_mode: MLFLOW
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port: 7001
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- name: train_automl
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task_type: MLFlowProjectsAutoML
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mlflow_tracking_uri: *mlflow_tracking_uri
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parameters: time_budget=30;estimator_list=['lgbm']
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experiment_name: automl_iris
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model_name: iris_A
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automl_tool: flaml
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data_path: /data/examples/iris
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- name: deploy_docker
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task_type: MLflowModels
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deps: [train_automl]
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model_uri: models:/iris_A/Production
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mlflow_tracking_uri: *mlflow_tracking_uri
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deploy_mode: DOCKER
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port: 7002
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- name: train_basic_algorithm
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task_type: MLFlowProjectsBasicAlgorithm
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mlflow_tracking_uri: *mlflow_tracking_uri
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parameters: n_estimators=200;learning_rate=0.2
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experiment_name: basic_algorithm_iris
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model_name: iris_B
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algorithm: lightgbm
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data_path: /data/examples/iris
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search_params: max_depth=[5, 10];n_estimators=[100, 200]
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- name: deploy_docker_compose
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task_type: MLflowModels
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deps: [train_basic_algorithm]
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model_uri: models:/iris_B/Production
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mlflow_tracking_uri: *mlflow_tracking_uri
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deploy_mode: DOCKER COMPOSE
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port: 7003
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