![]() We’ll have to create a different queue for these GPU tasks, pass the newly created queue as a parameter to whatever Airflow operator we are using and then configure the GPU nodes (which are also Celery workers) to wait for tasks on this new queue. What happens when some tasks require a GPU to do their work? By default, all tasks are pushed to a default queue and all Celery workers are configured to listen to the default queue and so the task could get scheduled on any node. Targeting tasks to particular nodes in the Celery cluster Another way is to containerize our Airflow tasks (code + dependencies) and use the DockerOperator or KubernetesPodOperator. But, there are severe limitations to the PythonVirtualenvOperator in that we can only run simple functions (not object-oriented code) and the start-up times for those tasks are atrocious when we have to create virtual environments with huge Python packages like TensorFlow and PyTorch every time the task is executed. What happens when we need to run legacy code which depends on TensorFlow 1.x alongside modern code which depends on TensorFlow 2.x? One way is to have Airflow create a virtual environment and run code inside it using something like the PythonVirtualenvOperator. Sure, we could automate all of this, but it is still a pain when we have to deal with thousands of Airflow tasks with different, often conflicting dependencies. For example, if a PythonOperator task depends on TensorFlow, we need to have TensorFlow installed on every worker node. We will either have to use a shared volume or synchronize code across multiple nodes ourselves. ![]() Unless we are using DockerOperator or KubernetesPodOperator (which we will discuss shortly), our DAGs and all its dependencies must be present on all the Celery worker nodes. We probably also need Flower for monitoring the Celery workers.The Celery executor requires a message broker like RabbitMQ or Redis.Then why do we need something more Kubernetes native? What would we do without you, Kubernetes?Īirflow has a mature Celery executor that allows us to run tasks concurrently across multiple worker nodes. This blog post is not an Airflow tutorial but rather talks about my journey with building and running highly scalable and reliable ML pipelines on Kubernetes with Airflow and requires some understanding of Airflow and Kubernetes. example postfix from the file and proceed with the helm lint, install, and upgrade commands as normal.I build ML pipelines for processing vast amounts of data and serving hundreds of data science models with very strict QoS parameters. file can be used to validate the helm values you are using work with the default airflow chart shipped with this repo. The code in this repo is licensed Apache 2.0 with Commons Clause, however it installs Astronomer components that have a commercial license, and requires a commercial subscription from Astronomer, Inc. These can be found by running the same ag command against the astronomer/airflow-chart values.yaml file. They are podMutation and useAstroSecurityManager. There are two additional params that need to be at this location outside of what is returned from above. The values output by this command will need to be inserted manually into astronomer/ at the. parameter. Run this script from the root of this repository:Īg "\.Values\.\w+" -o -no-filename -no-numbers | sort | uniq docker (make sure your user has permissions - try 'docker ps'). ![]()
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