![]() ![]() ![]() We have a script that pulls data from a very crappy and slow API. Here is the idea to solve your problem: from _execute_operator import SSHExecuteOperatorįrom _rule import TriggerRuleįrom import SSHHookįirst of all, sorry for the lengthy post, but I wanted to share the complete solution that works for me. The trigger rule possibilities: ALL_SUCCESS = 'all_success' If you're installing an Airflow version >=1.10.3, you can also return a list of task ids, allowing you to skip multiple downstream paths in a single Operator and don't have to use a dummy task before joining.Īll operators have a trigger_rule argument which defines the rule by which the generated task get triggered. ![]() If you want to skip some tasks, keep in mind that you can’t have an empty path, if so make a dummy task.Ī_task = DummyOperator(task_id='branch_a', dag=dag)ī_task = DummyOperator(task_id='branch_false', dag=dag) The task_id returned by the Python function has to be referencing a task directly downstream from the BranchPythonOperator task. The task_id returned is followed, and all of the other paths are skipped. The BranchPythonOperator is much like the PythonOperator except that it expects a python_callable that returns a task_id. Airflow 1.xĪirflow has a BranchPythonOperator that can be used to express the branching dependency more directly. You can also inherit directly from BaseBranchOperator overriding the choose_branch method, but for simple branching logic the decorator is best. Return "big_task" # run just this one task, skip all else Xcom_value = int(ti.xcom_pull(task_ids="start_task")) Tensor learning, algebra and backends to seamlessly use NumPy, MXNet, PyTorch, TensorFlow or CuPy.Airflow provides a branching decorator that allows you to return the task_id (or list of task_ids) that should run: branch_func(ti): Python backend system that decouples API from implementation unumpy provides a NumPy API. ![]() Manipulate JSON-like data with NumPy-like idioms. Multi-dimensional arrays with broadcasting and lazy computing for numerical analysis. NumPy-compatible sparse array library that integrates with Dask and SciPy's sparse linear algebra.ĭeep learning framework that accelerates the path from research prototyping to production deployment.Īn end-to-end platform for machine learning to easily build and deploy ML powered applications.ĭeep learning framework suited for flexible research prototyping and production.Ī cross-language development platform for columnar in-memory data and analytics. Labeled, indexed multi-dimensional arrays for advanced analytics and visualization NumPy-compatible array library for GPU-accelerated computing with Python.Ĭomposable transformations of NumPy programs: differentiate, vectorize, just-in-time compilation to GPU/TPU. NumPy's API is the starting point when libraries are written to exploit innovative hardware, create specialized array types, or add capabilities beyond what NumPy provides.ĭistributed arrays and advanced parallelism for analytics, enabling performance at scale. With this power comes simplicity: a solution in NumPy is often clear and elegant. NumPy brings the computational power of languages like C and Fortran to Python, a language much easier to learn and use. Nearly every scientist working in Python draws on the power of NumPy. ![]()
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