whentimeslows/website
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

Add support information for AMD GPUs (#10436)

Browse Source
This commit is contained in:
Kenny Ho
2018-10-03 23:55:57 -04:00
committed by k8s-ci-robot
parent ba3615dd22
commit 11e937be26
1 changed files with 30 additions and 8 deletions
Download Patch File Download Diff File Expand all files Collapse all files
content/en/docs/tasks/manage-gpus/scheduling-gpus.md
+30 -8
View File
@@ -7,10 +7,13 @@ title: Schedule GPUs
{{% capture overview %}} {{% capture overview %}}
Kubernetes includes **experimental** support for managing NVIDIA GPUs spread Kubernetes includes **experimental** support for managing AMD and NVIDIA GPUs spread
across nodes. The support for NVIDIA GPUs was added in v1.6 and has gone through across nodes. The support for NVIDIA GPUs was added in v1.6 and has gone through
multiple backwards incompatible iterations. This page describes how users can multiple backwards incompatible iterations. The support for AMD GPUs was added in
consume GPUs across different Kubernetes versions and the current limitations. v1.9 via [device plugin](#deploying-amd-gpu-device-plugin).
This page describes how users can consume GPUs across different Kubernetes versions
and the current limitations.
{{% /capture %}} {{% /capture %}}
@@ -28,14 +31,15 @@ feature gate has to be explicitly set to true across the system:
`--feature-gates="DevicePlugins=true"`. This is no longer required starting `--feature-gates="DevicePlugins=true"`. This is no longer required starting
from 1.10. from 1.10.
Then you have to install NVIDIA drivers on the nodes and run an NVIDIA GPU device Then you have to install GPU drivers from the corresponding vendor on the nodes
plugin ([see below](#deploying-nvidia-gpu-device-plugin)). and run the corresponding device plugin from the GPU vendor
([AMD](#deploying-amd-gpu-device-plugin), [NVIDIA](#deploying-nvidia-gpu-device-plugin)).
When the above conditions are true, Kubernetes will expose `nvidia.com/gpu` as When the above conditions are true, Kubernetes will expose `nvidia.com/gpu` or
a schedulable resource. `amd.com/gpu` as a schedulable resource.
You can consume these GPUs from your containers by requesting You can consume these GPUs from your containers by requesting
`nvidia.com/gpu` just like you request `cpu` or `memory`. `<vendor>.com/gpu` just like you request `cpu` or `memory`.
However, there are some limitations in how you specify the resource requirements However, there are some limitations in how you specify the resource requirements
when using GPUs: when using GPUs:
@@ -67,6 +71,24 @@ spec:
nvidia.com/gpu: 1 # requesting 1 GPU nvidia.com/gpu: 1 # requesting 1 GPU
``` ```
### Deploying AMD GPU device plugin
The [official AMD GPU device plugin](https://github.com/RadeonOpenCompute/k8s-device-plugin)
has the following requirements:
- Kubernetes nodes have to be pre-installed with AMD GPU Linux driver.
To deploy the AMD device plugin once your cluster is running and the above
requirements are satisfied:
```
# For Kubernetes v1.9
kubectl create -f https://raw.githubusercontent.com/RadeonOpenCompute/k8s-device-plugin/r1.9/k8s-ds-amdgpu-dp.yaml
# For Kubernetes v1.10
kubectl create -f https://raw.githubusercontent.com/RadeonOpenCompute/k8s-device-plugin/r1.10/k8s-ds-amdgpu-dp.yaml
```
Report issues with this device plugin to [RadeonOpenCompute/k8s-device-plugin](https://github.com/RadeonOpenCompute/k8s-device-plugin).
### Deploying NVIDIA GPU device plugin ### Deploying NVIDIA GPU device plugin
There are currently two device plugin implementations for NVIDIA GPUs: There are currently two device plugin implementations for NVIDIA GPUs: