kube-batch
sarus
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| kube-batch | sarus | |
|---|---|---|
| 3 | 2 | |
| 1,057 | 119 | |
| - | 4.2% | |
| 4.0 | 7.6 | |
| 11 months ago | about 2 months ago | |
| Go | C++ | |
| Apache License 2.0 | BSD 3-clause "New" or "Revised" License |
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kube-batch
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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
sarus
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Sarus VS Podman: comparison of both technologies
Sarus is An OCI-compatible container engine for HPC: https://sarus.readthedocs.io/en/stable/. At this point of view, it is very similar to use case of Podman.
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Scaling Kubernetes to 7,500 Nodes
The problem with slurm is how it's typically used: ssh into a shared login node with a shared file system, auth is handled by the linux users mostly, submit jobs with sbatch. Kubernetes deployment feels much more modern and safe.
I have worked with containers + slurm, where the vendor libmpi is injected in the container runtime [1] by a hook, which gives you close to bare metal performance with some container goodness in terms of isolation and deployment.
[1] https://github.com/eth-cscs/sarus
What are some alternatives?
volcano - A Cloud Native Batch System (Project under CNCF)
mpi-operator - Kubernetes Operator for MPI-based applications (distributed training, HPC, etc.)
argo - Workflow Engine for Kubernetes
pyTORCS-docker - Docker-based, gym-like torcs environment with vision.
udocker - A basic user tool to execute simple docker containers in batch or interactive systems without root privileges.
kube-scheduler-simulator - The simulator for the Kubernetes scheduler
img - Standalone, daemon-less, unprivileged Dockerfile and OCI compatible container image builder.
sidekick - High Performance HTTP Sidecar Load Balancer
crun - A fast and lightweight fully featured OCI runtime and C library for running containers
warewulf - Warewulf is a stateless and diskless container operating system provisioning system for large clusters of bare metal and/or virtual systems.
runtime-spec - OCI Runtime Specification