Building upon the POC done by Manikandan, I tried my hands at an experiment too.
I wanted to mainly experiment with the Yunikorn admission controller, with an aim to make no changes to my older DAGs. Deployed a setup that looks like this: - Deployed Yunikorn in a kind cluster with the default configurations. The default configurations launches the Yunikorn scheduler as well as an admission controller which watches for a `yunikorn-configs` configmap that can define queues, partitions, placement rules etc. - Deployed Airflow using helm charts in the same kind cluster while specifying the executor as KubernetesExecutor. Wanted to test out if Yunikorn can take over the scheduling of Airflow workers. Created some queues using this config present here: https://github.com/apache/yunikorn-k8shim/blob/master/deployments/examples/namespace/queues.yaml Tried running the Airflow K8s executor dag <https://github.com/apache/airflow/blob/main/airflow/example_dags/example_kubernetes_executor.py> without any changes to the DAG. I was able to run the DAG successfully. Results 1. The task pods get scheduled by Yunikorn instead of the default K8s scheduler 2. I was able to observe a single application run for the Airflow DAG in the Yunikorn UI. Observations 1. Yunikorn treats applications at the DAG level not at the task level, which is great. Due to this, we can try to leverage gang scheduling abilities of Yunikorn. 2. With the admission controller running, even the older DAGs will be able to benefit from the Yunikorn scheduling ablities without the need to make changes to the DAGs. This means that the same DAG will run with default scheduler (K8s default) as well as Yunikorn if need be! 3. As Mani mentioned, preemption capabilities can be explored due to this as well. I am happy to work on this effort and looking forward to it. Thanks & Regards, Amogh Desai On Tue, Oct 15, 2024 at 4:26 PM Jarek Potiuk <ja...@potiuk.com> wrote: > Hello here, > > *Tl;DR; I would love to start discussion about creating (for Airflow 3.x - > it does not have to be Airflow 3.0) a new community executor based on > YuniKorn* > > You might remember my point "replacing Celery Executor" when I raised the > Airflow 3 question. I never actually "meant" to replace (and remove) Celery > Executor, but I was more in a quest to see if we have a viable alternative. > > And I think we have one with Apache Yunicorn. https://yunikorn.apache.org/ > > While it is not a direct replacement (so I'd say it should be an additional > executor), I think Yunikorn can provide us with a number of features that > we currently cannot give to our users and from the discussions I had and > talk I saw at the Community Over Code in Denver, I believe it might be > something that might make Airflow also more capable especially in the > "optimization wars" context that I wrote about in > https://lists.apache.org/thread/1mp6jcfvx67zd3jjt9w2hlj0c5ysbh8r > > It seems like quite a good fit for the "Inference" use case that we want to > support for Airflow 3. > > At the Community Over Code I attended a talk (and had quite nice follow-up > discussion) from Apple engineers - named: "Maximizing GPU Utilization: > Apache YuniKorn Preemption" and had a very long discussion with Cloudera > people who are using YuniKorn for years to optimize their workloads. > > The presentation is not recorded, but I will try to get slides and send it > your way. > > I think we should take a close look at it - because it seems to save a ton > of implementation effort for the Apple team running Batch inference for > their multi-tenant internal environment - which I think is precisely what > you want to do. > > YuniKorn (https://yunikorn.apache.org/) is an "app-aware" scheduler that > has a number of queue / capacity management models, policies that allow > controlling various applications - competing for GPUs from a common pool. > > They mention things like: > > * Gang Scheduling / with gang scheduling preemption where there are > workloads requiring minimum number of workers > * Supports Latency sensitive workloads > * Resource quota management - things like priorities of execution > * YuniKorn preemption - with guaranteed capacity and preemption when needed > - which improves the utilisation > * Preemption that minimizes preemption cost (Pod level preemption rather > than application level preemption) - very customizable preemption with > opt-in/opt-out, queues, resource weights, fencing, supporting fifo/lifo > sorting etc. > * Runs in Cloud and on-premise > > The talk described quite a few scenarios of preemption/utilization/ > guaranteed resources etc. They also outlined on what YuniKorn works on new > features (intra-queue preemption etc.) and what future things can be done. > > > Coincidentally - Amogh Desai with a friend submitted a talk for Airflow > Summit: > > "A Step Towards Multi-Tenant Airflow Using Apache YuniKorn" > > Which did not make it to the Summit (other talk of Amogh did) - but I think > back then we have not realized about the potential of utilising YuniKorn to > optimize workflows managed by Airflow. > > But we seem to have people in the community who know more about YuniKorn <> > Airflow relation (Amogh :) ) and could probably comment and add some "from > the trenches" experience to the discussion. > > Here is the description of the talk that Amoghs submitted: > > Multi-tenant Airflow is hard and there have been novel approaches in the > recent past to converge this gap. A key obstacle in multi-tenant Airflow is > the management of cluster resources. This is crucial to avoid one malformed > workload from hijacking an entire cluster. It is also vital to restrict > users and groups from monopolizing resources in a shared cluster using > their workloads. > > To tackle these challenges, we turn to Apache YuniKorn, a K8s scheduler > catering all kinds of workloads. We leverage YuniKorn’s hierarchical queues > in conjunction with resource quotas to establish multi-tenancy at both the > shared namespace level and within individual namespaces where Airflow is > deployed. > > YuniKorn also introduces Airflow to a new dimension of preemption. Now, > Airflow workers can preempt resources from lower-priority jobs, ensuring > critical schedules in our data pipelines are met without compromise. > > Join us for a discussion on integrating Airflow with YuniKorn, unraveling > solutions to these multi-tenancy challenges. We will also share our past > experiences while scaling Airflow and the steps we have taken to handle > real world production challenges in equitable multi-tenant K8s clusters. > > I would love to hear what you think about it. I know we are deep into > Airflow 3.0 implementation - but that one can be discussed/implemented > independently and maybe it's a good idea to start doing it earlier than > later if we see that it has good potential. > > J. >
