Comments (5)
It seems I misunderstood... According to the section 2 in installation guide [1], it means to add those things in 2.1 and 2.2 to the default kube-scheduler and replace the default kube-scheduler, right?
If yes, I tried to modify /etc/kubernetes/manifests/kube-scheduler.yaml (then "$kubectl get po -n kube-system" will see the pod "kube-scheduler-leadtek-gs4820" restarts. Here "leadtek-gs4820" is the node name), but then I examine the running config (by "kubectl edit po -n kube-system kube-scheduler-leadtek-gs4820"), there's no update.
[1] https://github.com/AliyunContainerService/gpushare-scheduler-extender/blob/master/docs/install.md
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Hello, it is indeed to modify the default kube-scheduler file, but I deployed the GPU shared pod after the configuration is completed, found no effect, then I tried to restart the machine, to achieve the desired effect
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@Vae1997 , why can't we create one more scheduler instead of modifying the default one?
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@ide8
Hello, I am just a beginner, the specific principle is not very clear, so I can only say my thoughts:
If you modify the default scheduler, k8s should be re-introduced by internal related mechanisms without affecting the overall environment of the cluster. Deploy the modified scheduler so that your changes take effect.
Conversely, if you deploy a scheduler yourself, you may conflict with the default scheduling mechanism. On the other hand, your changes will not take effect. The worst case is that the original scheduling mechanism of the cluster will not work.
Of course, there should be a way to delete the default scheduler and redeploy as needed. It should be the same as when building a cluster with binary files. (But I haven't tried to build a k8s cluster from a binary file. It's not clear if this operation will affect the existing cluster.)
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Hello, it is indeed to modify the default kube-scheduler file, but I deployed the GPU shared pod after the configuration is completed, found no effect, then I tried to restart the machine, to achieve the desired effect
Similarly to @Vae1997 that restarting the machine, for some reason I happened to re-install OS, then the problem just disappeared...
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Related Issues (20)
- plugin does not evenly distribute the pods. 这个插件无法均匀分配Pod。 HOT 2
- GPU cores scheduling / GPU核心调度
- 你好,kubectl logs这个命令在gpu容器上无效,在普通容器上却可以
- Back-off restarting failed container: gpushare-device-plugin-ds-xxxxx HOT 1
- 这个GPU共享插件支持使用dcgm-exporter做监控吗 HOT 4
- 如果想要指定使用两张显卡多卡训练 该怎么做 HOT 1
- 如果一个机器上有两张卡,第一张卡的内存使满了,之后的任务会调度到另一张卡上吗
- Support for Horizontal Pod Autoscaling (HPA) with GPU Pods? 是否支持使用GPU Pods的水平Pod自动扩展(HPA)? HOT 1
- 该项目还在维护吗
- 关于显存申请基本单位改为MiB但不起作用的问题
- 调度层有bug吧,请求8G,实际设备最大7G,居然最终能创建成功pod
- 这个项目目前在使用过程中存在的问题
- ALIYUN_COM_GPU_MEM_IDX in the annotation is different than ALIYUN_COM_GPU_MEM_IDX inside the pod
- 方案只能在阿里云上的机器里使用吗 HOT 1
- kubelet版本問題
- [AKS] kube-scheduler static POD not running for Aliyun GPU Scheduler Extender HOT 1
- 版本兼容性问题
- k8s上安装好插件,无法识别到集群GPU资源 HOT 1
- 副本问题
- 节点上有多个GPU时,无法正常分配GPU HOT 1
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