分布式调度框架。
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# 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.
# Define variable `mlflow_tracking_uri`
mlflow_tracking_uri: &mlflow_tracking_uri "http://127.0.0.1:5000"
# Define the workflow
workflow:
name: "MLflow"
# Define the tasks under the workflow
tasks:
- name: train_xgboost_native
task_type: MLFlowProjectsCustom
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
- 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
parameters: time_budget=30;estimator_list=['lgbm']
experiment_name: automl_iris
model_name: iris_A
automl_tool: flaml
data_path: /data/examples/iris
- name: deploy_docker
task_type: MLflowModels
deps: [train_automl]
model_uri: models:/iris_A/Production
mlflow_tracking_uri: *mlflow_tracking_uri
deploy_mode: DOCKER
port: 7002
- name: train_basic_algorithm
task_type: MLFlowProjectsBasicAlgorithm
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]
- name: deploy_docker_compose
task_type: MLflowModels
deps: [train_basic_algorithm]
model_uri: models:/iris_B/Production
mlflow_tracking_uri: *mlflow_tracking_uri
deploy_mode: DOCKER COMPOSE
port: 7003