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Signed-off-by: Carlos Eduardo Arango Gutierrez <[email protected]>
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ArangoGutierrez committed Apr 16, 2024
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108 changes: 58 additions & 50 deletions README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -25,10 +25,7 @@
- [Updating Per-Node Configuration With a Node Label](#updating-per-node-configuration-with-a-node-label)
+ [Setting other helm chart values](#setting-other-helm-chart-values)
+ [Deploying with gpu-feature-discovery for automatic node labels](#deploying-with-gpu-feature-discovery-for-automatic-node-labels)
<!--
TODO: We are still in the process of migrating GFD to this repo. Once this is ready we can uncomment this section.
+ [Deploying gpu-feature-discovery in standalone mode](#deploying-gpu-feature-discovery-in-standalone-mode)
-->
* [Deploying via `helm install` with a direct URL to the `helm` package](#deploying-via-helm-install-with-a-direct-url-to-the-helm-package)
- [Building and Running Locally](#building-and-running-locally)
- [Changelog](#changelog)
Expand All @@ -42,6 +39,8 @@ The NVIDIA device plugin for Kubernetes is a Daemonset that allows you to automa
- Run GPU enabled containers in your Kubernetes cluster.

This repository contains NVIDIA's official implementation of the [Kubernetes device plugin](https://kubernetes.io/docs/concepts/extend-kubernetes/compute-storage-net/device-plugins/).
As of v0.15.0 this repository also holds the implementation for GPU Feature Discovery labels,
for further information on GPU Feature Discovery see [here](docs/gpu-feature-discovery/README.md).

Please note that:
- The NVIDIA device plugin API is beta as of Kubernetes v1.10.
Expand Down Expand Up @@ -559,11 +558,11 @@ $ helm repo add nvdp https://nvidia.github.io/k8s-device-plugin
$ helm repo update
```

Then verify that the latest release (`v0.14.5`) of the plugin is available:
Then verify that the latest release (`v0.15.0`) of the plugin is available:
```
$ helm search repo nvdp --devel
NAME CHART VERSION APP VERSION DESCRIPTION
nvdp/nvidia-device-plugin 0.14.5 0.14.5 A Helm chart for ...
nvdp/nvidia-device-plugin 0.15.0 0.15.0 A Helm chart for ...
```

Once this repo is updated, you can begin installing packages from it to deploy
Expand All @@ -574,7 +573,7 @@ The most basic installation command without any options is then:
helm upgrade -i nvdp nvdp/nvidia-device-plugin \
--namespace nvidia-device-plugin \
--create-namespace \
--version 0.14.5
--version 0.15.0
```

**Note:** You only need the to pass the `--devel` flag to `helm search repo`
Expand All @@ -583,7 +582,7 @@ version (e.g. `<version>-rc.1`). Full releases will be listed without this.

### Configuring the device plugin's `helm` chart

The `helm` chart for the latest release of the plugin (`v0.14.5`) includes
The `helm` chart for the latest release of the plugin (`v0.15.0`) includes
a number of customizable values.

Prior to `v0.12.0` the most commonly used values were those that had direct
Expand All @@ -593,7 +592,7 @@ case of the original values is then to override an option from the `ConfigMap`
if desired. Both methods are discussed in more detail below.

The full set of values that can be set are found here:
[here](https://github.com/NVIDIA/k8s-device-plugin/blob/v0.14.5/deployments/helm/nvidia-device-plugin/values.yaml).
[here](https://github.com/NVIDIA/k8s-device-plugin/blob/v0.15.0/deployments/helm/nvidia-device-plugin/values.yaml).

#### Passing configuration to the plugin via a `ConfigMap`.

Expand Down Expand Up @@ -632,7 +631,7 @@ EOF
And deploy the device plugin via helm (pointing it at this config file and giving it a name):
```
$ helm upgrade -i nvdp nvdp/nvidia-device-plugin \
--version=0.14.5 \
--version=0.15.0 \
--namespace nvidia-device-plugin \
--create-namespace \
--set-file config.map.config=/tmp/dp-example-config0.yaml
Expand All @@ -654,7 +653,7 @@ $ kubectl create cm -n nvidia-device-plugin nvidia-plugin-configs \
```
```
$ helm upgrade -i nvdp nvdp/nvidia-device-plugin \
--version=0.14.5 \
--version=0.15.0 \
--namespace nvidia-device-plugin \
--create-namespace \
--set config.name=nvidia-plugin-configs
Expand Down Expand Up @@ -682,7 +681,7 @@ EOF
And redeploy the device plugin via helm (pointing it at both configs with a specified default).
```
$ helm upgrade -i nvdp nvdp/nvidia-device-plugin \
--version=0.14.5 \
--version=0.15.0 \
--namespace nvidia-device-plugin \
--create-namespace \
--set config.default=config0 \
Expand All @@ -701,7 +700,7 @@ $ kubectl create cm -n nvidia-device-plugin nvidia-plugin-configs \
```
```
$ helm upgrade -i nvdp nvdp/nvidia-device-plugin \
--version=0.14.5 \
--version=0.15.0 \
--namespace nvidia-device-plugin \
--create-namespace \
--set config.default=config0 \
Expand Down Expand Up @@ -784,7 +783,7 @@ chart values that are commonly overridden are:
```

Please take a look in the
[`values.yaml`](https://github.com/NVIDIA/k8s-device-plugin/blob/v0.14.5/deployments/helm/nvidia-device-plugin/values.yaml)
[`values.yaml`](https://github.com/NVIDIA/k8s-device-plugin/blob/v0.15.0/deployments/helm/nvidia-device-plugin/values.yaml)
file to see the full set of overridable parameters for the device plugin.

Examples of setting these options include:
Expand All @@ -793,7 +792,7 @@ Enabling compatibility with the `CPUManager` and running with a request for
100ms of CPU time and a limit of 512MB of memory.
```shell
$ helm upgrade -i nvdp nvdp/nvidia-device-plugin \
--version=0.14.5 \
--version=0.15.0 \
--namespace nvidia-device-plugin \
--create-namespace \
--set compatWithCPUManager=true \
Expand All @@ -804,7 +803,7 @@ $ helm upgrade -i nvdp nvdp/nvidia-device-plugin \
Enabling compatibility with the `CPUManager` and the `mixed` `migStrategy`
```shell
$ helm upgrade -i nvdp nvdp/nvidia-device-plugin \
--version=0.14.5 \
--version=0.15.0 \
--namespace nvidia-device-plugin \
--create-namespace \
--set compatWithCPUManager=true \
Expand All @@ -823,7 +822,7 @@ Discovery to perform this labeling.
To enable it, simply set `gfd.enabled=true` during helm install.
```
helm upgrade -i nvdp nvdp/nvidia-device-plugin \
--version=0.14.5 \
--version=0.15.0 \
--namespace nvidia-device-plugin \
--create-namespace \
--set gfd.enabled=true
Expand Down Expand Up @@ -865,8 +864,7 @@ product name, e.g.:
```
nvidia.com/gpu.product = A100-SXM4-40GB-MIG-1g.5gb-SHARED
```
<!--
TODO: We are still in the process of migrating GFD to this repo. Once this is ready we can uncomment this section.

#### Deploying gpu-feature-discovery in standalone mode

As of `v0.15.0`, the device plugin's helm chart has integrated support to deploy
Expand All @@ -893,6 +891,7 @@ Once this repo is updated, you can begin installing packages from it to deploy
the `gpu-feature-discovery` component in standalone mode.

The most basic installation command without any options is then:

```
$ helm upgrade -i nvdp nvdp/nvidia-device-plugin \
--version 0.15.0 \
Expand Down Expand Up @@ -929,31 +928,31 @@ Using the default values for the flags:
$ helm upgrade -i nvdp \
--namespace nvidia-device-plugin \
--create-namespace \
https://nvidia.github.io/k8s-device-plugin/stable/nvidia-device-plugin-0.14.5.tgz
https://nvidia.github.io/k8s-device-plugin/stable/nvidia-device-plugin-0.15.0.tgz
```
-->

## Building and Running Locally

The next sections are focused on building the device plugin locally and running it.
It is intended purely for development and testing, and not required by most users.
It assumes you are pinning to the latest release tag (i.e. `v0.14.5`), but can
It assumes you are pinning to the latest release tag (i.e. `v0.15.0`), but can
easily be modified to work with any available tag or branch.

### With Docker

#### Build
Option 1, pull the prebuilt image from [Docker Hub](https://hub.docker.com/r/nvidia/k8s-device-plugin):
```shell
$ docker pull nvcr.io/nvidia/k8s-device-plugin:v0.14.5
$ docker tag nvcr.io/nvidia/k8s-device-plugin:v0.14.5 nvcr.io/nvidia/k8s-device-plugin:devel
$ docker pull nvcr.io/nvidia/k8s-device-plugin:v0.15.0
$ docker tag nvcr.io/nvidia/k8s-device-plugin:v0.15.0 nvcr.io/nvidia/k8s-device-plugin:devel
```

Option 2, build without cloning the repository:
```shell
$ docker build \
-t nvcr.io/nvidia/k8s-device-plugin:devel \
-f deployments/container/Dockerfile.ubuntu \
https://github.com/NVIDIA/k8s-device-plugin.git#v0.14.5
https://github.com/NVIDIA/k8s-device-plugin.git#v0.15.0
```

Option 3, if you want to modify the code:
Expand Down Expand Up @@ -1015,29 +1014,38 @@ See the [changelog](CHANGELOG.md)
* You can report a bug by [filing a new issue](https://github.com/NVIDIA/k8s-device-plugin/issues/new)
* You can contribute by opening a [pull request](https://help.github.com/articles/using-pull-requests/)

## Documentation

- [Quick Start](docs/quick_start.md)
* [Prerequisites](docs/quick_start.md#prerequisites)
* [Preparing your GPU Nodes](docs/quick_start.md#preparing-your-gpu-nodes)
* [Node Feature Discovery (NFD)](docs/quick_start.md#node-feature-discovery-nfd)
* [Enabling GPU Support in Kubernetes](docs/quick_start.md#enabling-gpu-support-in-kubernetes)
* [Running GPU Jobs](docs/quick_start.md#running-gpu-jobs)
- [Configuring the NVIDIA device plugin binary](docs/customizing.md)
* [As command line flags or envvars](docs/customizing.md#as-command-line-flags-or-envvars)
* [As a configuration file](docs/customizing.md#as-a-configuration-file)
* [Configuration Option Details](docs/customizing.md#configuration-option-details)
* [Shared Access to GPUs with CUDA Time-Slicing](docs/customizing.md#shared-access-to-gpus-with-cuda-time-slicing)
- [Deployment via `helm`](docs/deployment_via_helm.md)
* [Configuring the device plugin's `helm` chart](docs/deployment_via_helm.md#configuring-the-device-plugins-helm-chart)
+ [Passing configuration to the plugin via a `ConfigMap`.](docs/deployment_via_helm.md#passing-configuration-to-the-plugin-via-a-configmap)
- [Single Config File Example](docs/deployment_via_helm.md#single-config-file-example)
- [Multiple Config File Example](docs/deployment_via_helm.md#multiple-config-file-example)
- [Updating Per-Node Configuration With a Node Label](docs/deployment_via_helm.md#updating-per-node-configuration-with-a-node-label)
+ [Setting other helm chart values](docs/deployment_via_helm.md#setting-other-helm-chart-values)
+ [Deploying with gpu-feature-discovery for automatic node labels](docs/deployment_via_helm.md#deploying-with-gpu-feature-discovery-for-automatic-node-labels)
* [Deploying via `helm install` with a direct URL to the `helm` package](docs/deployment_via_helm.md#deploying-via-helm-install-with-a-direct-url-to-the-helm-package)
- [Building and Running Locally](docs/building_and_running.md)
- [GPU Feature Discovery CMD](docs/gfd_cmd.md)
- [GPU Feature Discovery Labels](docs/gfd_labels.md)
- [Changelog](CHANGELOG.md)
### Versioning

Before v1.10 the versioning scheme of the device plugin had to match exactly the version of Kubernetes.
After the promotion of device plugins to beta this condition was was no longer required.
We quickly noticed that this versioning scheme was very confusing for users as they still expected to see
a version of the device plugin for each version of Kubernetes.

This versioning scheme applies to the tags `v1.8`, `v1.9`, `v1.10`, `v1.11`, `v1.12`.

We have now changed the versioning to follow [SEMVER](https://semver.org/). The
first version following this scheme has been tagged `v0.0.0`.

Going forward, the major version of the device plugin will only change
following a change in the device plugin API itself. For example, version
`v1beta1` of the device plugin API corresponds to version `v0.x.x` of the
device plugin. If a new `v2beta2` version of the device plugin API comes out,
then the device plugin will increase its major version to `1.x.x`.

As of now, the device plugin API for Kubernetes >= v1.10 is `v1beta1`. If you
have a version of Kubernetes >= 1.10 you can deploy any device plugin version >
`v0.0.0`.

### Upgrading Kubernetes with the Device Plugin

Upgrading Kubernetes when you have a device plugin deployed doesn't require you
to do any, particular changes to your workflow. The API is versioned and is
pretty stable (though it is not guaranteed to be non breaking). Starting with
Kubernetes version 1.10, you can use `v0.3.0` of the device plugin to perform
upgrades, and Kubernetes won't require you to deploy a different version of the
device plugin. Once a node comes back online after the upgrade, you will see
GPUs re-registering themselves automatically.

Upgrading the device plugin itself is a more complex task. It is recommended to
drain GPU tasks as we cannot guarantee that GPU tasks will survive a rolling
upgrade. However we make best efforts to preserve GPU tasks during an upgrade.
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