1.创建namespace gpu

2.增加限制

[root@tensorflow1 gpu-namespace]# cat compute-resources.yaml

apiVersion: v1

kind: ResourceQuota

metadata:

name: compute-resources

namespace: gpu

spec:

hard:

pods: "5"

requests.cpu: "1"

requests.memory: 1Gi

limits.cpu: "2"

limits.memory: 2Gi

[root@tensorflow1 gpu-namespace]# kubectl describe namespace gpu

Name: gpu

Labels: <none>

Annotations: <none>

Status: Active

Resource Quotas

Name: compute-resources

Resource Used Hard

-------- --- ---

limits.cpu 0 2

limits.memory 0 2Gi

pods 4 5

requests.cpu 0 1

requests.memory 0 1Gi

No resource limits.

3.检查限制情况

在已经创建好容器的情况下再增加限制，发现限 制并没有起作用，预期是memory限制到2g，结果是 从容器内仍然能看到8g内存

容器内：

root@tensorflow-ps-rc-cm9c8:/notebooks# free -m

total used free shared buff/cache available

Mem: 7783 1615 274 251 5893 5383

Swap: 0 0 0

宿主机：

[root@tensorflow0 ~]# free -m

total used free shared buff/cache available

Mem: 7783 1616 272 251 5894 5382

Swap: 0 0 0

4.杀掉容器

容器启动失败，要求对容器添加限制

[root@tensorflow1 gpu-namespace]# kubectl describe rc/tensorflow-ps-rc -n gpu

...

Warning FailedCreate 2m replication-controller Error creating: pods "tensorflow-ps-rc-jrxxl" is forbidden: failed quota: compute-resources: must specify limits.cpu,limits.memory,requests.cpu,requests.memory

Warning FailedCreate 23s (x9 over 2m) replication-controller (combined from similar events): Error creating: pods "tensorflow-ps-rc-sw9wx" is forbidden: failed quota: compute-resources: must specify limits.cpu,limits.memory,requests.cpu,requests.memory

5.配置好限制，重启启动

增加配置：

resources:

requests:

memory: "1024Mi"

cpu: "250m"

limits:

memory: "1024Mi"

cpu: "500m"

只启动了一个[root@tensorflow1 tf_gpu]# kubectl get all -o wide -n gpu

NAME READY STATUS RESTARTS AGE IP NODE

po/tensorflow-ps-rc-9m8zj 1/1 Running 0 1h 10.244.2.91 tensorflow0

po/tensorflow-worker-rc-5zq9q 1/1 Running 0 11d 10.244.2.61 tensorflow0

po/tensorflow-worker-rc-mhncr 1/1 Running 0 11d 10.244.1.87 tensorflow2

NAME DESIRED CURRENT READY AGE CONTAINERS IMAGES SELECTOR

rc/tensorflow-ps-rc 2 1 1 1h ps nfs:5000/tensorflow/tensorflow:nightly name=tensorflow-ps

rc/tensorflow-worker-rc 2 2 2 11d worker nfs:5000/tensorflow/tensorflow:nightly-gpu name=tensorflow-worker

NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE SELECTOR

svc/tensorflow-ps-service ClusterIP 10.99.156.187 <none> 2222/TCP 11d name=tensorflow-ps

svc/tensorflow-wk-service ClusterIP 10.102.251.161 <none> 2222/TCP 11d name=tensorflow-worker

第二个不满足条件了

[root@tensorflow1 tf_gpu]# kubectl describe namespace gpu

Name: gpu

Labels: <none>

Annotations: <none>

Status: Active

Resource Quotas

Name: compute-resources

Resource Used Hard

-------- --- ---

limits.cpu 500m 2

limits.memory 1Gi 2Gi

pods 3 5

requests.cpu 250m 1

requests.memory 1Gi 1Gi

No resource limits.

[root@tensorflow1 tf_gpu]# kubectl describe rc/tensorflow-ps-rc -n gpu

Warning FailedCreate 3m replication-controller Error creating: pods "tensorflow-ps-rc-cbt6c" is forbidden: exceeded quota: compute-resources, requested: requests.memory=1Gi, used: requests.memory=1Gi, limited: requests.memory=1Gi

6.进入启动成功的那个容器

宿主机内存

[root@tensorflow0 ~]# free -m

total used free shared buff/cache available

Mem: 7783 1433 450 251 5899 5567

Swap: 0 0 0

容器内存，与外面看到的一致。

root@tensorflow-ps-rc-9m8zj:/notebooks# free -m

total used free shared buff/cache available

Mem: 7783 1433 450 251 5899 5567

Swap: 0 0 0

虽然限制了1G内存，但是仍能看到8G内存

本文转自CSDN-k8s namespace限制调研