address comments

Signed-off-by: chipzoller <[email protected]>

Author: chipzoller

README.md

@@ -50,7 +50,7 @@ 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,
+As of v0.16.1 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:
@@ -134,7 +134,7 @@ Once you have configured the options above on all the GPU nodes in your
 cluster, you can enable GPU support by deploying the following Daemonset:
 
 ```shell
-$ kubectl create -f https://raw.githubusercontent.com/NVIDIA/k8s-device-plugin/v0.16.2/deployments/static/nvidia-device-plugin.yml
+$ kubectl create -f https://raw.githubusercontent.com/NVIDIA/k8s-device-plugin/v0.16.1/deployments/static/nvidia-device-plugin.yml
 ```
 
 **Note:** This is a simple static daemonset meant to demonstrate the basic
@@ -179,7 +179,7 @@ Done
 ```
 
 > [!WARNING]
-> If you don't request GPUs when using the device plugin with NVIDIA images, all the GPUs on the machine will be exposed inside your container.
+> If you do not request GPUs when you use the device plugin, the plugin exposes all the GPUs on the machine inside your container.
 
 ## Configuring the NVIDIA device plugin binary
 
@@ -337,7 +337,7 @@ extended options in its configuration file. There are two flavors of sharing
 available: Time-Slicing and MPS.
 
 > [!NOTE]
-> The use of time-slicing and MPS are mutually exclusive.
+> Time-slicing and MPS are mutually exclusive.
 
 In the case of time-slicing, CUDA time-slicing is used to allow workloads sharing a GPU to
 interleave with each other. However, nothing special is done to isolate workloads that are
@@ -350,9 +350,8 @@ In contrast to time-slicing, MPS does space partitioning and allows memory and
 compute resources to be explicitly partitioned and enforces these limits per
 workload.
 
-With both time-slicing and MPS the same sharing method is applied to all GPUs on
-a node. Sharing cannot be configured on a per-GPU basis but applies uniformly at
-the node level.
+With both time-slicing and MPS, the same sharing method is applied to all GPUs on
+a node. You cannot configure sharing on a per-GPU basis.
 
 #### With CUDA Time-Slicing
 
@@ -583,11 +582,11 @@ $ helm repo add nvdp https://nvidia.github.io/k8s-device-plugin
 $ helm repo update
 ```
 
-Then verify that the latest release (`v0.16.2`) of the plugin is available:
+Then verify that the latest release (`v0.16.1`) of the plugin is available:
 ```
 $ helm search repo nvdp --devel
 NAME                     	  CHART VERSION  APP VERSION	DESCRIPTION
-nvdp/nvidia-device-plugin	  0.16.2	 0.16.2		A Helm chart for ...
+nvdp/nvidia-device-plugin	  0.16.1	 0.16.1		A Helm chart for ...
 ```
 
 Once this repo is updated, you can begin installing packages from it to deploy
@@ -598,7 +597,7 @@ 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 0.16.2
+  --version 0.16.1
 ```
 
 **Note:** You only need the to pass the `--devel` flag to `helm search repo`
@@ -607,7 +606,7 @@ 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 (`v0.16.2`) includes
+The `helm` chart for the latest release of the plugin (`v0.16.1`) includes
 a number of customizable values.
 
 Prior to `v0.12.0` the most commonly used values were those that had direct
@@ -617,7 +616,7 @@ 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/v0.16.2/deployments/helm/nvidia-device-plugin/values.yaml).
+[here](https://github.com/NVIDIA/k8s-device-plugin/blob/v0.16.1/deployments/helm/nvidia-device-plugin/values.yaml).
 
 #### Passing configuration to the plugin via a `ConfigMap`.
 
@@ -657,7 +656,7 @@ 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=0.16.2 \
+    --version=0.16.1 \
     --namespace nvidia-device-plugin \
     --create-namespace \
     --set-file config.map.config=/tmp/dp-example-config0.yaml
@@ -679,7 +678,7 @@ $ kubectl create cm -n nvidia-device-plugin nvidia-plugin-configs \
 ```
 ```
 $ helm upgrade -i nvdp nvdp/nvidia-device-plugin \
-    --version=0.16.2 \
+    --version=0.16.1 \
     --namespace nvidia-device-plugin \
     --create-namespace \
     --set config.name=nvidia-plugin-configs
@@ -708,7 +707,7 @@ 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=0.16.2 \
+    --version=0.16.1 \
     --namespace nvidia-device-plugin \
     --create-namespace \
     --set config.default=config0 \
@@ -727,7 +726,7 @@ $ kubectl create cm -n nvidia-device-plugin nvidia-plugin-configs \
 ```
 ```
 $ helm upgrade -i nvdp nvdp/nvidia-device-plugin \
-    --version=0.16.2 \
+    --version=0.16.1 \
     --namespace nvidia-device-plugin \
     --create-namespace \
     --set config.default=config0 \
@@ -811,7 +810,7 @@ runtimeClassName:
 ```
 
 Please take a look in the
-[`values.yaml`](https://github.com/NVIDIA/k8s-device-plugin/blob/v0.16.2/deployments/helm/nvidia-device-plugin/values.yaml)
+[`values.yaml`](https://github.com/NVIDIA/k8s-device-plugin/blob/v0.16.1/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:
@@ -820,7 +819,7 @@ 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=0.16.2 \
+    --version=0.16.1 \
     --namespace nvidia-device-plugin \
     --create-namespace \
     --set compatWithCPUManager=true \
@@ -831,7 +830,7 @@ $ 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=0.16.2 \
+    --version=0.16.1 \
     --namespace nvidia-device-plugin \
     --create-namespace \
     --set compatWithCPUManager=true \
@@ -843,14 +842,14 @@ $ helm upgrade -i nvdp nvdp/nvidia-device-plugin \
 As of `v0.12.0`, the device plugin's helm chart has integrated support to
 deploy
 [`gpu-feature-discovery`](https://github.com/NVIDIA/gpu-feature-discovery)
-(GFD). One can use GFD to automatically generate labels for the
+(GFD). You can use GFD to automatically generate labels for the
 set of GPUs available on a node. Under the hood, it leverages Node Feature
 Discovery to perform this labeling.
 
 To enable it, simply set `gfd.enabled=true` during helm install.
 ```shell
 helm upgrade -i nvdp nvdp/nvidia-device-plugin \
-    --version=0.16.2 \
+    --version=0.16.1 \
     --namespace nvidia-device-plugin \
     --create-namespace \
     --set gfd.enabled=true
@@ -895,7 +894,7 @@ nvidia.com/gpu.product = A100-SXM4-40GB-MIG-1g.5gb-SHARED
 
 #### Deploying gpu-feature-discovery in standalone mode
 
-As of v0.16.2, the device plugin's helm chart has integrated support to deploy
+As of v0.16.1, the device plugin's helm chart has integrated support to deploy
 [`gpu-feature-discovery`](https://gitlab.com/nvidia/kubernetes/gpu-feature-discovery/-/tree/main)
 
 When gpu-feature-discovery in deploying standalone, begin by setting up the
@@ -906,13 +905,13 @@ $ helm repo add nvdp https://nvidia.github.io/k8s-device-plugin
 $ helm repo update
 ```
 
-Then verify that the latest release (`v0.16.2`) of the plugin is available
+Then verify that the latest release (`v0.16.1`) of the plugin is available
 (Note that this includes the GFD chart):
 
 ```shell
 $ helm search repo nvdp --devel
 NAME                     	  CHART VERSION  APP VERSION	DESCRIPTION
-nvdp/nvidia-device-plugin	  0.16.2	 0.16.2		A Helm chart for ...
+nvdp/nvidia-device-plugin	  0.16.1	 0.16.1		A Helm chart for ...
 ```
 
 Once this repo is updated, you can begin installing packages from it to deploy
@@ -922,7 +921,7 @@ The most basic installation command without any options is then:
 
 ```shell
 $ helm upgrade -i nvdp nvdp/nvidia-device-plugin \
-  --version 0.16.2 \
+  --version 0.16.1 \
   --namespace gpu-feature-discovery \
   --create-namespace \
   --set devicePlugin.enabled=false
@@ -933,7 +932,7 @@ the default namespace.
 
 ```shell
 $ helm upgrade -i nvdp nvdp/nvidia-device-plugin \
-    --version=0.16.2 \
+    --version=0.16.1 \
     --set allowDefaultNamespace=true \
     --set nfd.enabled=false \
     --set migStrategy=mixed \
@@ -956,39 +955,39 @@ 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-0.16.2.tgz
+    https://nvidia.github.io/k8s-device-plugin/stable/nvidia-device-plugin-0.16.1.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. `v0.16.2`), but can
+It assumes you are pinning to the latest release tag (i.e. `v0.16.1`), 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:v0.16.2
-$ docker tag nvcr.io/nvidia/k8s-device-plugin:v0.16.2 nvcr.io/nvidia/k8s-device-plugin:devel
+$ docker pull nvcr.io/nvidia/k8s-device-plugin:v0.16.1
+$ docker tag nvcr.io/nvidia/k8s-device-plugin:v0.16.1 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 \
-    https://github.com/NVIDIA/k8s-device-plugin.git#v0.16.2
+    -f deployments/container/Dockerfile.ubuntu \
+    https://github.com/NVIDIA/k8s-device-plugin.git#v0.16.1
 ```
 
 Option 3, if you want to modify the code:
 ```shell
 $ git clone https://github.com/NVIDIA/k8s-device-plugin.git && cd k8s-device-plugin
 $ docker build \
     -t nvcr.io/nvidia/k8s-device-plugin:devel \
-    -f deployments/container/Dockerfile \
+    -f deployments/container/Dockerfile.ubuntu \
     .
 ```