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Initially, Airflow only has SequentialExecutor, LocalExecutor, CeleryExecutor, and MesosExecutor available. In the last two years, since Airflow 1.9.0, Airflow gets more attention, and more executors have been contributed to the community, those executors include DaskExecutor, KubernetesExecutor, DebugExecutor. Sep 28, 2020 In Airflow 1.10 and 2.0 there is an airflow config command but there is a difference in behavior. In Airflow 1.10, it prints all config options while in Airflow 2.0, it's a command group. Airflow config is now airflow config list. You can check other options by running the command airflow config -help.

Apache Airflow¶

Apache Airflowis a platform that enables you to programmatically author, schedule, and monitor workflows. Using Airflow,you can build a workflow for SageMaker training, hyperparameter tuning, batch transform and endpoint deployment.You can use any SageMaker deep learning framework or Amazon algorithms to perform above operations in Airflow.

There are two ways to build a SageMaker workflow. Using Airflow SageMaker operators or using Airflow PythonOperator.

1. SageMaker Operators: In Airflow 1.10.1, the SageMaker team contributed special operators for SageMaker operations.Each operator takes a configuration dictionary that defines the corresponding operation. We provide APIs to generatethe configuration dictionary in the SageMaker Python SDK. Currently, the following SageMaker operators are supported:

• SageMakerTrainingOperator

• SageMakerTuningOperator

• SageMakerModelOperator

• SageMakerTransformOperator

• SageMakerEndpointConfigOperator

• SageMakerEndpointOperator

2. PythonOperator: Airflow built-in operator that executes Python callables. You can use the PythonOperator to executeoperations in the SageMaker Python SDK to create a SageMaker workflow.

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Using Airflow on AWS¶

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Turbine is an open-source AWS CloudFormation template that enables you to create an Airflow resource stack on AWS.You can get it here: https://github.com/villasv/aws-airflow-stack Mac game shank 1 0 1.

Using Airflow SageMaker Operators¶

Bettertouchtool 3 02. Starting with Airflow 1.10.1, you can use SageMaker operators in Airflow. All SageMaker operators take a configurationdictionary that can be generated by the SageMaker Python SDK. For example:

Now you can pass these configurations to the corresponding SageMaker operators and create the workflow:

Using Airflow Python Operator¶

Airflow PythonOperatoris a built-in operator that can execute any Python callable. If you want to build the SageMaker workflow in a moreflexible way, write your python callables for SageMaker operations by using the SageMaker Python SDK.

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Then build your workflow by using the PythonOperator with the Python callables defined above:

A workflow that runs a SageMaker training job and a batch transform job is finished. You can customize your Pythoncallables with the SageMaker Python SDK according to your needs, and build more flexible and powerful workflows.