NVIDIA GPU feature discovery

NVIDIA GPU feature discovery

Overview

NVIDIA GPU Feature Discovery for Kubernetes is a software component that allows you to automatically generate labels for the set of GPUs available on a node. It leverages the Node Feature Discovery to perform this labeling.

Beta Version

This tool should be considered beta until it reaches v1.0.0. As such, we may break the API before reaching v1.0.0, but we will setup a deprecation policy to ease the transition.

Prerequisites

The list of prerequisites for running the NVIDIA GPU Feature Discovery is described below:

nvidia-docker version > 2.0 (see how to install and it's prerequisites)
docker configured with nvidia as the default runtime.
Kubernetes version >= 1.10
NVIDIA device plugin for Kubernetes (see how to setup)
NFD deployed on each node you want to label with the local source configured
- When deploying GPU feature discovery with helm (as described below) we provide a way to automatically deploy NFD for you
- To deploy NFD yourself, please see https://github.com/kubernetes-sigs/node-feature-discovery

Quick Start

The following assumes you have at least one node in your cluster with GPUs and the standard NVIDIA drivers have already been installed on it.

Node Feature Discovery (NFD)

The first step is to make sure that Node Feature Discovery is running on every node you want to label. NVIDIA GPU Feature Discovery use the local source so be sure to mount volumes. See https://github.com/kubernetes-sigs/node-feature-discovery for more details.

You also need to configure the Node Feature Discovery to only expose vendor IDs in the PCI source. To do so, please refer to the Node Feature Discovery documentation.

The following command will deploy NFD with the minimum required set of parameters to run gpu-feature-discovery.

kubectl apply -f https://raw.githubusercontent.com/NVIDIA/gpu-feature-discovery/v0.3.0/deployments/static/nfd.yaml

Note: This is a simple static daemonset meant to demonstrate the basic features required of node-feature-discovery in order to successfully run gpu-feature-discovery. Please see the instructions below for Deployment via helm when deploying in a production setting.

Preparing your GPU Nodes

Be sure that nvidia-docker2 is installed on your GPU nodes and Docker default runtime is set to nvidia. See https://github.com/NVIDIA/nvidia-docker/wiki/Advanced-topics#default-runtime.

Deploy NVIDIA GPU Feature Discovery (GFD)

The next step is to run NVIDIA GPU Feature Discovery on each node as a Daemonset or as a Job.

Daemonset

kubectl apply -f https://raw.githubusercontent.com/NVIDIA/gpu-feature-discovery/v0.3.0/deployments/static/gpu-feature-discovery-daemonset.yaml

Note: This is a simple static daemonset meant to demonstrate the basic features required of gpu-feature-discovery. Please see the instructions below for Deployment via helm when deploying in a production setting.

Job

You must change the NODE_NAME value in the template to match the name of the node you want to label:

$ export NODE_NAME=<your-node-name>
$ curl https://raw.githubusercontent.com/NVIDIA/gpu-feature-discovery/v0.3.0/deployments/static/gpu-feature-discovery-job.yaml.template \
    | sed "s/NODE_NAME/${NODE_NAME}/" > gpu-feature-discovery-job.yaml
$ kubectl apply -f gpu-feature-discovery-job.yaml

Note: This method should only be used for testing and not deployed in a productions setting.

Verifying Everything Works

With both NFD and GFD deployed and running, you should now be able to see GPU related labels appearing on any nodes that have GPUs installed on them.

$ kubectl get nodes -o yaml
apiVersion: v1
items:
- apiVersion: v1
  kind: Node
  metadata:
    ...

    labels:
      nvidia.com/cuda.driver.major: "455"
      nvidia.com/cuda.driver.minor: "06"
      nvidia.com/cuda.driver.rev: ""
      nvidia.com/cuda.runtime.major: "11"
      nvidia.com/cuda.runtime.minor: "1"
      nvidia.com/gfd.timestamp: "1594644571"
      nvidia.com/gpu.compute.major: "8"
      nvidia.com/gpu.compute.minor: "0"
      nvidia.com/gpu.count: "1"
      nvidia.com/gpu.family: ampere
      nvidia.com/gpu.machine: NVIDIA DGX-2H
      nvidia.com/gpu.memory: "39538"
      nvidia.com/gpu.product: A100-SXM4-40GB
      ...
...

The GFD Command line interface

Available options:

gpu-feature-discovery:
Usage:
  gpu-feature-discovery [--fail-on-init-error=<bool>] [--mig-strategy=<strategy>] [--oneshot | --sleep-interval=<seconds>] [--output-file=<file> | -o <file>]
  gpu-feature-discovery -h | --help
  gpu-feature-discovery --version

Options:
  -h --help                       Show this help message and exit
  --version                       Display version and exit
  --oneshot                       Label once and exit
  --fail-on-init-error=<bool>     Fail if there is an error during initialization of any label sources [Default: true]
  --sleep-interval=<seconds>      Time to sleep between labeling [Default: 60s]
  --mig-strategy=<strategy>       Strategy to use for MIG-related labels [Default: none]
  -o <file> --output-file=<file>  Path to output file
                                  [Default: /etc/kubernetes/node-feature-discovery/features.d/gfd]

You can also use environment variables:

Env Variable	Option	Example
GFD_FAIL_ON_INIT_ERROR	--fail-on-init-error	true
GFD_MIG_STRATEGY	--mig-strategy	none
GFD_ONESHOT	--oneshot	TRUE
GFD_OUTPUT_FILE	--output-file	output
GFD_SLEEP_INTERVAL	--sleep-interval	10s

Environment variables override the command line options if they conflict.

Generated Labels

This is the list of the labels generated by NVIDIA GPU Feature Discovery and their meaning:

Label Name	Value Type	Meaning	Example
nvidia.com/cuda.driver.major	Integer	Major of the version of NVIDIA driver	418
nvidia.com/cuda.driver.minor	Integer	Minor of the version of NVIDIA driver	30
nvidia.com/cuda.driver.rev	Integer	Revision of the version of NVIDIA driver	40
nvidia.com/cuda.runtime.major	Integer	Major of the version of CUDA	10
nvidia.com/cuda.runtime.minor	Integer	Minor of the version of CUDA	1
nvidia.com/gfd.timestamp	Integer	Timestamp of the generated labels	1555019244
nvidia.com/gpu.compute.major	Integer	Major of the compute capabilities	3
nvidia.com/gpu.compute.minor	Integer	Minor of the compute capabilities	3
nvidia.com/gpu.count	Integer	Number of GPUs	2
nvidia.com/gpu.family	String	Architecture family of the GPU	kepler
nvidia.com/gpu.machine	String	Machine type	DGX-1
nvidia.com/gpu.memory	Integer	Memory of the GPU in Mb	2048
nvidia.com/gpu.product	String	Model of the GPU	GeForce-GT-710

Depending on the MIG strategy used, the following set of labels may also be available (or override the default values for some of the labels listed above):

MIG 'single' strategy

With this strategy, the single nvidia.com/gpu label is overloaded to provide information about MIG devices on the node, rather than full GPUs. This assumes all GPUs on the node have been divided into identical partitions of the same size. The example below shows info for a system with 8 full GPUs, each of which is partitioned into 7 equal sized MIG devices (56 total).

Label Name	Value Type	Meaning	Example
nvidia.com/mig.strategy	String	MIG strategy in use	single
nvidia.com/gpu.product (overridden)	String	Model of the GPU (with MIG info added)	A100-SXM4-40GB-MIG-1g.5gb
nvidia.com/gpu.count (overridden)	Integer	Number of MIG devices	56
nvidia.com/gpu.memory (overridden)	Integer	Memory of each MIG device in Mb	5120
nvidia.com/gpu.multiprocessors	Integer	Number of Multiprocessors for MIG device	14
nvidia.com/gpu.slices.gi	Integer	Number of GPU Instance slices	1
nvidia.com/gpu.slices.ci	Integer	Number of Compute Instance slices	1
nvidia.com/gpu.engines.copy	Integer	Number of DMA engines for MIG device	1
nvidia.com/gpu.engines.decoder	Integer	Number of decoders for MIG device	1
nvidia.com/gpu.engines.encoder	Integer	Number of encoders for MIG device	1
nvidia.com/gpu.engines.jpeg	Integer	Number of JPEG engines for MIG device	0
nvidia.com/gpu.engines.ofa	Integer	Number of OfA engines for MIG device	0

MIG 'mixed' strategy

With this strategy, a separate set of labels for each MIG device type is generated. The name of each MIG device type is defines as follows:

MIG_TYPE=mig-<slice_count>g.<memory_size>.gb
e.g.  MIG_TYPE=mig-3g.20gb

Label Name	Value Type	Meaning	Example
nvidia.com/mig.strategy	String	MIG strategy in use	mixed
nvidia.com/MIG_TYPE.count	Integer	Number of MIG devices of this type	2
nvidia.com/MIG_TYPE.memory	Integer	Memory of MIG device type in Mb	10240
nvidia.com/MIG_TYPE.multiprocessors	Integer	Number of Multiprocessors for MIG device	14
nvidia.com/MIG_TYPE.slices.ci	Integer	Number of GPU Instance slices	1
nvidia.com/MIG_TYPE.slices.gi	Integer	Number of Compute Instance slices	1
nvidia.com/MIG_TYPE.engines.copy	Integer	Number of DMA engines for MIG device	1
nvidia.com/MIG_TYPE.engines.decoder	Integer	Number of decoders for MIG device	1
nvidia.com/MIG_TYPE.engines.encoder	Integer	Number of encoders for MIG device	1
nvidia.com/MIG_TYPE.engines.jpeg	Integer	Number of JPEG engines for MIG device	0
nvidia.com/MIG_TYPE.engines.ofa	Integer	Number of OfA engines for MIG device	0

Deployment via `helm`

The preferred method to deploy gpu-feature-discovery is as a daemonset using helm. Instructions for installing helm can be found here.

The helm chart for the latest release of GFD (v0.3.0) includes a number of customizable values. The most commonly overridden ones are:

  failOnInitError:
      Fail if there is an error during initialization of any label sources (default: true)
  sleepInterval:
      time to sleep between labeling (default "60s")
  migStrategy:
      pass the desired strategy for labeling MIG devices on GPUs that support it
      [none | single | mixed] (default "none)
  nfd.deploy:
      When set to true, deploy NFD as a subchart with all of the proper
      parameters set for it (default "true")

Note: The following document provides more information on the available MIG strategies and how they should be used Supporting Multi-Instance GPUs (MIG) in Kubernetes.

Please take a look in the following values.yaml files to see the full set of overridable parameters for both the top-level gpu-feature-discovery chart and the node-feature-discovery subchart.

Installing via `helm install` from the `gpu-feature-discovery` `helm` repository

The preferred method of deployment is with helm install via the gpu-feature-discovery helm repository.

This repository can be installed as follows:

$ helm repo add nvgfd https://nvidia.github.io/gpu-feature-discovery
$ helm repo update

Once this repo is updated, you can begin installing packages from it to depoloy the gpu-feature-discovery daemonset and (optionally) the node-feature-discovery daemonset. Below are some examples of deploying these components with the various flags from above.

Note: Since this is a pre-release version, you will need to pass the --devel flag to helm search repo in order to see this release listed.

Using the default values for all flags:

$ helm install \
    --version=0.3.0 \
    --generate-name \
    nvgfd/gpu-feature-discovery

Disabling auto-deployment of NFD and running with a MIG strategy of 'mixed' in the default namespace.

$ helm install \
    --version=0.3.0 \
    --generate-name \
    --set nfd.deploy=false \
    --set migStrategy=mixed
    --set namespace=default \
    nvgfd/gpu-feature-discovery

Deploying via `helm install` with a direct URL to the `helm` package

If you prefer not to install from the gpu-feature-discovery helm repo, you can run helm install directly against the tarball of the components helm package. The examples below install the same daemonsets as the method above, except that they use direct URLs to the helm package instead of the helm repo.

Using the default values for the flags:

$ helm install \
    --generate-name \
    https://nvidia.github.com/gpu-feature-discovery/stable/gpu-feature-discovery-0.3.0.tgz

Disabling auto-deployment of NFD and running with a MIG strategy of 'mixed' in the default namespace.

$ helm install \
    --generate-name \
    --set nfd.deploy=false \
    --set migStrategy=mixed
    --set namespace=default \
    https://nvidia.github.com/gpu-feature-discovery/stable/gpu-feature-discovery-0.3.0.tgz

Building and running locally with Docker

Download the source code:

git clone https://github.com/NVIDIA/gpu-feature-discovery

Build the docker image:

export GFD_VERSION=$(git describe --tags --dirty --always)
docker build . --build-arg GFD_VERSION=$GFD_VERSION -t nvidia/gpu-feature-discovery:${GFD_VERSION}

Run it:

mkdir -p output-dir
docker run -v ${PWD}/output-dir:/etc/kubernetes/node-feature-discovery/features.d nvidia/gpu-feature-discovery:${GFD_VERSION}

You should have set the default runtime of Docker to nvidia on your host or you can also use the --runtime=nvidia option:

docker run --runtime=nvidia nvidia/gpu-feature-discovery:${GFD_VERSION}

Building and running locally on your native machine

Download the source code:

git clone https://github.com/NVIDIA/gpu-feature-discovery

Get dependies:

dep ensure

Build it:

export GFD_VERSION=$(git describe --tags --dirty --always)
go build -ldflags "-X main.Version=${GFD_VERSION}"

You can also use the Dockerfile.devel:

docker build . -f Dockerfile.devel -t gfd-devel
docker run -it gfd-devel
go build -ldflags "-X main.Version=devel"

Run it:

./gpu-feature-discovery --output=$(pwd)/gfd

shaardie/gpu-feature-discovery

NVIDIA GPU feature discovery

Table of Contents

Overview

Beta Version

Prerequisites

Quick Start

Node Feature Discovery (NFD)

Preparing your GPU Nodes

Deploy NVIDIA GPU Feature Discovery (GFD)

Daemonset

Job

Verifying Everything Works

The GFD Command line interface

Generated Labels

MIG 'single' strategy

MIG 'mixed' strategy

Deployment via `helm`

Installing via `helm install` from the `gpu-feature-discovery` `helm` repository

Deploying via `helm install` with a direct URL to the `helm` package

Building and running locally with Docker

Building and running locally on your native machine

shaardie/gpu-feature-discovery

NVIDIA GPU feature discovery

Table of Contents

Overview

Beta Version

Prerequisites

Quick Start

Node Feature Discovery (NFD)

Preparing your GPU Nodes

Deploy NVIDIA GPU Feature Discovery (GFD)

Daemonset

Job

Verifying Everything Works

The GFD Command line interface

Generated Labels

MIG 'single' strategy

MIG 'mixed' strategy

Deployment via helm

Installing via helm install from the gpu-feature-discovery helm repository

Deploying via helm install with a direct URL to the helm package

Building and running locally with Docker

Building and running locally on your native machine

Deployment via `helm`

Installing via `helm install` from the `gpu-feature-discovery` `helm` repository

Deploying via `helm install` with a direct URL to the `helm` package