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SPECIFICTION
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Voda Scheduler

github.com

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Spec score 6.5 #18 of 21 in GPU Cluster Management Software

Spec sheet

Purpose
Voda Scheduler is a GPU scheduler for elastic deep learning workloads built on Kubernetes, Kubeflow Training Operator, and Horovod.Read 8 Oct 2026 · github.com
Intended users
The stated use case is elastic or distributed deep learning workloads running in a Kubernetes cluster.Read 8 Oct 2026 · github.com
Deployment
The project says Voda Scheduler is designed to deploy in any Kubernetes cluster, on-cloud or on-premise, that can schedule GPUs.Read 8 Oct 2026 · github.com
API
The project documentation links to API endpoints as part of its getting-started steps.Read 8 Oct 2026 · github.com
License
The GitHub repository identifies an Apache-2.0 license.Read 8 Oct 2026 · github.com
Changing nodes
The scheduler is aware of node additions and deletions, works with an existing autoscaler, and is designed to tolerate failing nodes and spot instances that may come and go.Read 8 Oct 2026 · github.com
Compatibility
The project says it was tested with Kubernetes v1.20.Read 8 Oct 2026 · github.com
Elastic training
It supports dynamically scaling distributed training jobs up or down at runtime without interrupting training.Read 8 Oct 2026 · github.com
Fault tolerance
The project lists fault tolerance as a feature.Read 8 Oct 2026 · github.com
License and maturity
The repository lists an Apache-2.0 license and warns that everything is experimental and may change significantly at any time.Read 8 Oct 2026 · github.com
Maturity
The README warns that everything is experimental and may change significantly at any time.Read 8 Oct 2026 · github.com
Monitoring
The README links to documentation for Prometheus metrics exposed by the scheduler.Read 8 Oct 2026 · github.com
Node changes
It is aware of node additions and deletions, works with an existing autoscaler, and tolerates failing nodes.Read 8 Oct 2026 · github.com
Prerequisite
The README says the scheduler was tested with Kubernetes v1.20.Read 8 Oct 2026 · github.com
Related tools
The README names Kubeflow Training Operator, Kubeflow MPI Operator, Horovod, and a Prometheus NVIDIA SMI exporter among related projects.Read 8 Oct 2026 · github.com
Resource allocation
The scheduler aims to use idle resources and allocate resources efficiently when a cluster is heavily loaded.Read 8 Oct 2026 · github.com
Scheduling algorithms
The listed algorithms include FIFO, Elastic-FIFO (the default), SRJF, Elastic-SRJF, Tiresias, Elastic-Tiresias, FfDL Optimizer, and AFS-L.Read 8 Oct 2026 · github.com
Topology awareness
Topology-aware scheduling and worker migration can consolidate resources to maximize cluster throughput.Read 8 Oct 2026 · github.com

Where Specifiction places it

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