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

* 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>

Author: tengqm

content/en/docs/concepts/configuration/manage-compute-resources-container.md

@@ -143,9 +143,7 @@ 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
@@ -208,12 +206,10 @@ $ 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
@@ -299,10 +295,10 @@ 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`,
@@ -544,6 +540,4 @@ 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

@@ -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

@@ -143,68 +143,3 @@ 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.