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Remove docs related to in-tree support to GPU (#8294)

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* Remove docs related to in-tree support to GPU

The in-tree support to GPU is completely removed in release 1.11.
This PR removes the related docs in release-1.11 branch.

xref: kubernetes/kubernetes#61498

* Update content updated by PR to Hugo syntax

Signed-off-by: Misty Stanley-Jones <mistyhacks@google.com>
This commit is contained in:
Qiming
2018-05-25 06:43:32 +08:00
committed by Misty Linville
parent 88e0fbaf06
commit fbf5c26fe5
3 changed files with 4 additions and 82 deletions
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content/en/docs/concepts/configuration/manage-compute-resources-container.md
+4 -10
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@@ -144,9 +144,7 @@ When using Docker:
multiplied by 100. The resulting value is the total amount of CPU time that a container can use
every 100ms. A container cannot use more than its share of CPU time during this interval.
{{< note >}}
**Note**: The default quota period is 100ms. The minimum resolution of CPU quota is 1ms.
{{< /note >}}
{{< note >}}**Note**: The default quota period is 100ms. The minimum resolution of CPU quota is 1ms.{{ {{</ note >}}}
- The `spec.containers[].resources.limits.memory` is converted to an integer, and
used as the value of the
@@ -209,12 +207,10 @@ $ kubectl describe nodes e2e-test-minion-group-4lw4
Name: e2e-test-minion-group-4lw4
[ ... lines removed for clarity ...]
Capacity:
alpha.kubernetes.io/nvidia-gpu: 0
cpu: 2
memory: 7679792Ki
pods: 110
Allocatable:
alpha.kubernetes.io/nvidia-gpu: 0
cpu: 1800m
memory: 7474992Ki
pods: 110
@@ -300,10 +296,10 @@ Container in the Pod was terminated and restarted five times.
You can call `kubectl get pod` with the `-o go-template=...` option to fetch the status
of previously terminated Containers:
```shell
```shell{% raw %}
[13:59:01] $ kubectl get pod -o go-template='{{range.status.containerStatuses}}{{"Container Name: "}}{{.name}}{{"\r\nLastState: "}}{{.lastState}}{{end}}' simmemleak-hra99
Container Name: simmemleak
LastState: map[terminated:map[exitCode:137 reason:OOM Killed startedAt:2015-07-07T20:58:43Z finishedAt:2015-07-07T20:58:43Z containerID:docker://0e4095bba1feccdfe7ef9fb6ebffe972b4b14285d5acdec6f0d3ae8a22fad8b2]]
LastState: map[terminated:map[exitCode:137 reason:OOM Killed startedAt:2015-07-07T20:58:43Z finishedAt:2015-07-07T20:58:43Z containerID:docker://0e4095bba1feccdfe7ef9fb6ebffe972b4b14285d5acdec6f0d3ae8a22fad8b2]]{% endraw %}
```
You can see that the Container was terminated because of `reason:OOM Killed`,
@@ -545,6 +541,4 @@ consistency across providers and platforms.
* [ResourceRequirements](/docs/reference/generated/kubernetes-api/{{< param "version" >}}/#resourcerequirements-v1-core)
{{% /capture %}}
{{% /capture %}}
content/en/docs/tasks/administer-cluster/extended-resource-node.md
-5
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@@ -83,7 +83,6 @@ The output shows that the Node has a capacity of 4 dongles:
```
"capacity": {
"alpha.kubernetes.io/nvidia-gpu": "0",
"cpu": "2",
"memory": "2049008Ki",
"example.com/dongle": "4",
@@ -99,7 +98,6 @@ Once again, the output shows the dongle resource:
```yaml
Capacity:
alpha.kubernetes.io/nvidia-gpu: 0
cpu: 2
memory: 2049008Ki
example.com/dongle: 4
@@ -205,6 +203,3 @@ kubectl describe node <your-node-name> | grep dongle
{{% /capture %}}
content/en/docs/tasks/manage-gpus/scheduling-gpus.md
-67
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@@ -152,70 +152,3 @@ spec:
This will ensure that the pod will be scheduled to a node that has the GPU type
you specified.
## v1.6 and v1.7
To enable GPU support in 1.6 and 1.7, a special **alpha** feature gate
`Accelerators` has to be set to true across the system:
`--feature-gates="Accelerators=true"`. It also requires using the Docker
Engine as the container runtime.
Further, the Kubernetes nodes have to be pre-installed with NVIDIA drivers.
Kubelet will not detect NVIDIA GPUs otherwise.
When you start Kubernetes components after all the above conditions are true,
Kubernetes will expose `alpha.kubernetes.io/nvidia-gpu` as a schedulable
resource.
You can consume these GPUs from your containers by requesting
`alpha.kubernetes.io/nvidia-gpu` just like you request `cpu` or `memory`.
However, there are some limitations in how you specify the resource requirements
when using GPUs:
- GPUs are only supposed to be specified in the `limits` section, which means:
* You can specify GPU `limits` without specifying `requests` because
Kubernetes will use the limit as the request value by default.
* You can specify GPU in both `limits` and `requests` but these two values
must be equal.
* You cannot specify GPU `requests` without specifying `limits`.
- Containers (and pods) do not share GPUs. There's no overcommitting of GPUs.
- Each container can request one or more GPUs. It is not possible to request a
fraction of a GPU.
When using `alpha.kubernetes.io/nvidia-gpu` as the resource, you also have to
mount host directories containing NVIDIA libraries (libcuda.so, libnvidia.so
etc.) to the container.
Here's an example:
```yaml
apiVersion: v1
kind: Pod
metadata:
name: cuda-vector-add
spec:
restartPolicy: OnFailure
containers:
- name: cuda-vector-add
# https://github.com/kubernetes/kubernetes/blob/v1.7.11/test/images/nvidia-cuda/Dockerfile
image: "k8s.gcr.io/cuda-vector-add:v0.1"
resources:
limits:
alpha.kubernetes.io/nvidia-gpu: 1 # requesting 1 GPU
volumeMounts:
- name: "nvidia-libraries"
mountPath: "/usr/local/nvidia/lib64"
volumes:
- name: "nvidia-libraries"
hostPath:
path: "/usr/lib/nvidia-375"
```
The `Accelerators` feature gate and `alpha.kubernetes.io/nvidia-gpu` resource
works on 1.8 and 1.9 as well. It will be deprecated in 1.10 and removed in
1.11.
## Future
- Support for hardware accelerators in Kubernetes is still in alpha.
- Better APIs will be introduced to provision and consume accelerators in a scalable manner.
- Kubernetes will automatically ensure that applications consuming GPUs get the best possible performance.
{{% /capture %}}