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Kube-batch Alternatives
Similar projects and alternatives to kube-batch
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WorkOS
The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.
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InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
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PaddlePaddle
PArallel Distributed Deep LEarning: Machine Learning Framework from Industrial Practice (『飞桨』核心框架,深度学习&机器学习高性能单机、分布式训练和跨平台部署)
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warewulf
Warewulf is a stateless and diskless container operating system provisioning system for large clusters of bare metal and/or virtual systems.
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cromwell
Scientific workflow engine designed for simplicity & scalability. Trivially transition between one off use cases to massive scale production environments
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descheduler
Discontinued Descheduler for Kubernetes [Moved to: https://github.com/kubernetes-sigs/descheduler] (by kubernetes-incubator)
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singularity-cri
Discontinued The Singularity implementation of the Kubernetes Container Runtime Interface
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SaaSHub
SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives
kube-batch reviews and mentions
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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
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A note from our sponsor - WorkOS
workos.com | 19 Apr 2024
Stats
kubernetes-retired/kube-batch is an open source project licensed under Apache License 2.0 which is an OSI approved license.
The primary programming language of kube-batch is Go.