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Posted to commits@dolphinscheduler.apache.org by GitBox <gi...@apache.org> on 2022/09/17 11:52:08 UTC
[GitHub] [dolphinscheduler] zhongjiajie commented on a diff in pull request #11962: [Feature][PyDolphinScheduler] Support MLflow task in pyds
zhongjiajie commented on code in PR #11962:
URL: https://github.com/apache/dolphinscheduler/pull/11962#discussion_r973577029
##########
dolphinscheduler-python/pydolphinscheduler/src/pydolphinscheduler/tasks/mlflow.py:
##########
@@ -0,0 +1,251 @@
+# 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.
+
+"""Task shell."""
+
+from pydolphinscheduler.constants import TaskType
+from pydolphinscheduler.core.task import Task
+
+
+class MLflowTaskType(str):
+ """MLflow task type."""
+
+ MLFLOW_PROJECTS = "MLflow Projects"
+ MLFLOW_MODELS = "MLflow Models"
+
+
+class MLflowJobType(str):
+ """MLflow job type."""
+
+ AUTOML = "AutoML"
+ BASIC_ALGORITHM = "BasicAlgorithm"
+ CUSTOM_PROJECT = "CustomProject"
+
+
+class MLflowDeployType(str):
+ """MLflow deploy type."""
+
+ MLFLOW = "MLFLOW"
+ DOCKER = "DOCKER"
+ DOCKER_COMPOSE = "DOCKER COMPOSE"
+
+
+DEFAULT_MLFLOW_TRACKING_URI = "http://127.0.0.1:5000"
+DEFAULT_VERSION = "master"
+
+
+class MLflowModels(Task):
+ """Task MLflow models object, declare behavior for MLflow models task to dolphinscheduler.
+
+ Deploy machine learning models in diverse serving environments.
+
+ :param name: task name
+ :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 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
+
+ _task_custom_attr = {
+ "mlflow_tracking_uri",
+ "mlflow_task_type",
Review Comment:
I find out we have same attribute with this two parameter, should we add a `Base` class for mlflow just we do for DVC task type?
##########
dolphinscheduler-python/pydolphinscheduler/src/pydolphinscheduler/tasks/mlflow.py:
##########
@@ -0,0 +1,251 @@
+# 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.
+
+"""Task shell."""
+
+from pydolphinscheduler.constants import TaskType
+from pydolphinscheduler.core.task import Task
+
+
+class MLflowTaskType(str):
+ """MLflow task type."""
+
+ MLFLOW_PROJECTS = "MLflow Projects"
+ MLFLOW_MODELS = "MLflow Models"
+
+
+class MLflowJobType(str):
+ """MLflow job type."""
+
+ AUTOML = "AutoML"
+ BASIC_ALGORITHM = "BasicAlgorithm"
+ CUSTOM_PROJECT = "CustomProject"
+
+
+class MLflowDeployType(str):
+ """MLflow deploy type."""
+
+ MLFLOW = "MLFLOW"
+ DOCKER = "DOCKER"
+ DOCKER_COMPOSE = "DOCKER COMPOSE"
+
+
+DEFAULT_MLFLOW_TRACKING_URI = "http://127.0.0.1:5000"
+DEFAULT_VERSION = "master"
+
+
+class MLflowModels(Task):
+ """Task MLflow models object, declare behavior for MLflow models task to dolphinscheduler.
+
+ Deploy machine learning models in diverse serving environments.
+
+ :param name: task name
+ :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 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
+
+ _task_custom_attr = {
+ "mlflow_tracking_uri",
+ "mlflow_task_type",
+ "deploy_type",
+ "deploy_model_key",
+ "deploy_port",
+ "cpu_limit",
+ "memory_limit",
+ }
+
+ def __init__(
+ self,
+ name: str,
+ model_uri: str,
+ mlflow_tracking_uri: str = DEFAULT_MLFLOW_TRACKING_URI,
+ deploy_mode: str = MLflowDeployType.DOCKER,
+ port: int = 7000,
+ cpu_limit: float = 1.0,
+ memory_limit: str = "500M",
Review Comment:
```suggestion
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",
```
##########
dolphinscheduler-python/pydolphinscheduler/src/pydolphinscheduler/tasks/mlflow.py:
##########
@@ -0,0 +1,251 @@
+# 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.
+
+"""Task shell."""
+
+from pydolphinscheduler.constants import TaskType
+from pydolphinscheduler.core.task import Task
+
+
+class MLflowTaskType(str):
+ """MLflow task type."""
+
+ MLFLOW_PROJECTS = "MLflow Projects"
+ MLFLOW_MODELS = "MLflow Models"
+
+
+class MLflowJobType(str):
+ """MLflow job type."""
+
+ AUTOML = "AutoML"
+ BASIC_ALGORITHM = "BasicAlgorithm"
+ CUSTOM_PROJECT = "CustomProject"
+
+
+class MLflowDeployType(str):
+ """MLflow deploy type."""
+
+ MLFLOW = "MLFLOW"
+ DOCKER = "DOCKER"
+ DOCKER_COMPOSE = "DOCKER COMPOSE"
+
+
+DEFAULT_MLFLOW_TRACKING_URI = "http://127.0.0.1:5000"
+DEFAULT_VERSION = "master"
+
+
+class MLflowModels(Task):
+ """Task MLflow models object, declare behavior for MLflow models task to dolphinscheduler.
+
+ Deploy machine learning models in diverse serving environments.
+
+ :param name: task name
+ :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 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
+
+ _task_custom_attr = {
+ "mlflow_tracking_uri",
+ "mlflow_task_type",
+ "deploy_type",
+ "deploy_model_key",
+ "deploy_port",
+ "cpu_limit",
+ "memory_limit",
+ }
+
+ def __init__(
+ self,
+ name: str,
+ model_uri: str,
+ mlflow_tracking_uri: str = DEFAULT_MLFLOW_TRACKING_URI,
+ deploy_mode: str = MLflowDeployType.DOCKER,
+ port: int = 7000,
+ cpu_limit: float = 1.0,
+ memory_limit: str = "500M",
+ *args,
+ **kwargs
+ ):
+ """Init mlflow models task."""
+ super().__init__(name, task_type=TaskType.MLFLOW, *args, **kwargs)
+ self.mlflow_tracking_uri = mlflow_tracking_uri
+ 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(Task):
+ """Task MLflow projects object, declare behavior for MLflow Custom projects task to dolphinscheduler.
+
+ :param name: task name
+ :param repository: Repository url of MLflow Project, Support git address and directory on worker.
+ If it's in a subdirectory, We add # to support this (same as mlflow run) ,
+ for example https://github.com/mlflow/mlflow#examples/xgboost/xgboost_native.
+ :param mlflow_tracking_uri: MLflow tracking server uri, default is http://127.0.0.1:5000
+ :param experiment_name: MLflow experiment name, default is empty
+ :param parameters: MLflow project parameters, default is empty
+ :param version: MLflow project version, default is master
+
+ """
+
+ mlflow_task_type = MLflowTaskType.MLFLOW_PROJECTS
+ mlflow_job_type = MLflowJobType.CUSTOM_PROJECT
+
+ _task_custom_attr = {
+ "mlflow_tracking_uri",
+ "mlflow_task_type",
+ "mlflow_job_type",
+ "experiment_name",
+ "params",
+ "mlflow_project_repository",
+ "mlflow_project_version",
+ }
+
+ def __init__(
+ self,
+ name: str,
+ repository: str,
+ mlflow_tracking_uri: str = DEFAULT_MLFLOW_TRACKING_URI,
+ experiment_name: str = "",
+ parameters: str = "",
+ version: str = "master",
+ *args,
+ **kwargs
+ ):
+ """Init mlflow projects task."""
+ super().__init__(name, task_type=TaskType.MLFLOW, *args, **kwargs)
+ self.mlflow_tracking_uri = mlflow_tracking_uri
+ self.mlflow_project_repository = repository
+ self.experiment_name = experiment_name
+ self.params = parameters
+ self.mlflow_project_version = version
+
+
+class MLFlowProjectsAutoML(Task):
+ """Task MLflow projects object, declare behavior for AutoML task to dolphinscheduler.
+
+ :param name: task name
+ :param data_path: data path of MLflow Project, Support git address and directory on worker.
+ :param automl_tool: The AutoML tool used, currently supports autosklearn and flaml.
+ :param mlflow_tracking_uri: MLflow tracking server uri, default is http://127.0.0.1:5000
+ :param experiment_name: MLflow experiment name, default is empty
+ :param model_name: MLflow model name, default is empty
+ :param parameters: MLflow project parameters, default is empty
+
+ """
+
+ mlflow_task_type = MLflowTaskType.MLFLOW_PROJECTS
+ mlflow_job_type = MLflowJobType.AUTOML
+
+ _task_custom_attr = {
+ "mlflow_task_type",
+ "mlflow_tracking_uri",
+ "mlflow_job_type",
+ "experiment_name",
+ "model_name",
+ "register_model",
+ "data_path",
+ "params",
+ "automl_tool",
+ }
+
+ def __init__(
+ self,
+ name: str,
+ data_path: str,
+ automl_tool: str = "flaml",
+ mlflow_tracking_uri: str = DEFAULT_MLFLOW_TRACKING_URI,
+ experiment_name: str = "",
+ model_name: str = "",
+ parameters: str = "",
Review Comment:
`optional` here
##########
dolphinscheduler-python/pydolphinscheduler/src/pydolphinscheduler/tasks/mlflow.py:
##########
@@ -0,0 +1,251 @@
+# 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.
+
+"""Task shell."""
+
+from pydolphinscheduler.constants import TaskType
+from pydolphinscheduler.core.task import Task
+
+
+class MLflowTaskType(str):
+ """MLflow task type."""
+
+ MLFLOW_PROJECTS = "MLflow Projects"
+ MLFLOW_MODELS = "MLflow Models"
+
+
+class MLflowJobType(str):
+ """MLflow job type."""
+
+ AUTOML = "AutoML"
+ BASIC_ALGORITHM = "BasicAlgorithm"
+ CUSTOM_PROJECT = "CustomProject"
+
+
+class MLflowDeployType(str):
+ """MLflow deploy type."""
+
+ MLFLOW = "MLFLOW"
+ DOCKER = "DOCKER"
+ DOCKER_COMPOSE = "DOCKER COMPOSE"
+
+
+DEFAULT_MLFLOW_TRACKING_URI = "http://127.0.0.1:5000"
+DEFAULT_VERSION = "master"
+
+
+class MLflowModels(Task):
+ """Task MLflow models object, declare behavior for MLflow models task to dolphinscheduler.
+
+ Deploy machine learning models in diverse serving environments.
+
+ :param name: task name
+ :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 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
+
+ _task_custom_attr = {
+ "mlflow_tracking_uri",
+ "mlflow_task_type",
+ "deploy_type",
+ "deploy_model_key",
+ "deploy_port",
+ "cpu_limit",
+ "memory_limit",
+ }
+
+ def __init__(
+ self,
+ name: str,
+ model_uri: str,
+ mlflow_tracking_uri: str = DEFAULT_MLFLOW_TRACKING_URI,
+ deploy_mode: str = MLflowDeployType.DOCKER,
+ port: int = 7000,
+ cpu_limit: float = 1.0,
+ memory_limit: str = "500M",
+ *args,
+ **kwargs
+ ):
+ """Init mlflow models task."""
+ super().__init__(name, task_type=TaskType.MLFLOW, *args, **kwargs)
+ self.mlflow_tracking_uri = mlflow_tracking_uri
+ 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(Task):
+ """Task MLflow projects object, declare behavior for MLflow Custom projects task to dolphinscheduler.
+
+ :param name: task name
+ :param repository: Repository url of MLflow Project, Support git address and directory on worker.
+ If it's in a subdirectory, We add # to support this (same as mlflow run) ,
+ for example https://github.com/mlflow/mlflow#examples/xgboost/xgboost_native.
+ :param mlflow_tracking_uri: MLflow tracking server uri, default is http://127.0.0.1:5000
+ :param experiment_name: MLflow experiment name, default is empty
+ :param parameters: MLflow project parameters, default is empty
+ :param version: MLflow project version, default is master
+
+ """
+
+ mlflow_task_type = MLflowTaskType.MLFLOW_PROJECTS
+ mlflow_job_type = MLflowJobType.CUSTOM_PROJECT
+
+ _task_custom_attr = {
+ "mlflow_tracking_uri",
+ "mlflow_task_type",
+ "mlflow_job_type",
+ "experiment_name",
+ "params",
+ "mlflow_project_repository",
+ "mlflow_project_version",
+ }
+
+ def __init__(
+ self,
+ name: str,
+ repository: str,
+ mlflow_tracking_uri: str = DEFAULT_MLFLOW_TRACKING_URI,
+ experiment_name: str = "",
+ parameters: str = "",
+ version: str = "master",
Review Comment:
we should also add keyword `optional` here
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