mpi-operator
Kubernetes Operator for MPI-based applications (distributed training, HPC, etc.) (by kubeflow)
kube-batch
A batch scheduler of kubernetes for high performance workload, e.g. AI/ML, BigData, HPC (by kubernetes-retired)
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| mpi-operator | kube-batch | |
|---|---|---|
| 1 | 3 | |
| 395 | 1,057 | |
| 1.8% | - | |
| 7.3 | 4.0 | |
| 1 day ago | 11 months ago | |
| Go | Go | |
| Apache License 2.0 | Apache License 2.0 |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
mpi-operator
Posts with mentions or reviews of mpi-operator.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2021-01-25.
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Scaling Kubernetes to 7,500 Nodes
Hi, kube-scheduler maintainer here, currently looking into enabling MPI use cases in k8s.
We started a discussion in https://github.com/kubeflow/mpi-operator/issues/315
kube-batch
Posts with mentions or reviews of kube-batch.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-05-03.
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Volcano vs Yunikorn vs Knative
tldr; Knative Batch Job provider should support the respective coscheduling and kube-batch support. We had developed an in-house one for KubeFlow, from scratch. We had added Apache Arrow support into knative-serving with the respective CloudEvents interop layer, natively (i.e. secure shmem via IPC namespace, instead of message passing on the same host). We use it as a direct replacement for Apache Arrow Ballista, and had planned researching further DataFusion compat layer. Almost any modern ETL is pretty dubious without Apache Arrow.
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Kubernetes Was Never Designed for Batch Jobs
Another aspect of batch jobs is that we’ll often want to run distributed computations where we split our data into chunks and run a function on each chunk. One popular option is to run Spark, which is built for exactly this use case, on top of Kubernetes. And there are other options for additional software to make running distributed computations on Kubernetes easier.
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Scaling Kubernetes to 7,500 Nodes
> That said, strain on the kube-scheduler is spiky. A new job may consist of many hundreds of pods all being created at once, then return to a relatively low rate of churn.
Last I checked, the default scheduler places Pods one at a time. It might be advantageous to use a gang/batch scheduler like kube-batch[0], Poseidon[1] or DCM[2].
[0] https://github.com/kubernetes-sigs/kube-batch
[1] https://github.com/kubernetes-sigs/poseidon
[2] https://github.com/vmware/declarative-cluster-management
What are some alternatives?
When comparing mpi-operator and kube-batch you can also consider the following projects:
polyaxon - MLOps Tools For Managing & Orchestrating The Machine Learning LifeCycle
volcano - A Cloud Native Batch System (Project under CNCF)