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# Node feature discovery for [Kubernetes](https://kubernetes.io)
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[![Build Status](https://api.travis-ci.org/kubernetes-sigs/node-feature-discovery.svg?branch=master)](https://travis-ci.org/kubernetes-sigs/node-feature-discovery)
[![Go Report Card](https://goreportcard.com/badge/github.com/kubernetes-sigs/node-feature-discovery)](https://goreportcard.com/report/github.com/kubernetes-sigs/node-feature-discovery)
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- [Overview](#overview)
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- [Command line interface](#command-line-interface)
- [Feature discovery](#feature-discovery)
- [Feature sources](#feature-sources)
- [Feature labels](#feature-labels)
- [Extended resources (experimental)](#extended-resources-experimental)
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- [Getting started](#getting-started)
- [System requirements](#system-requirements)
- [Usage](#usage)
- [Building from source](#building-from-source)
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- [Targeting nodes with specific features](#targeting-nodes-with-specific-features)
- [References](#references)
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- [License](#license)
- [Demo](#demo)
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## Overview
This software enables node feature discovery for Kubernetes. It detects
hardware features available on each node in a Kubernetes cluster, and advertises
those features using node labels.
NFD consists of two software components:
1. **nfd-master** is responsible for labeling Kubernetes node objects
2. **nfd-worker** is detects features and communicates them to nfd-master.
One instance of nfd-worker is supposed to be run on each node of the cluster
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## Command line interface
You can run NFD in stand-alone Docker containers e.g. for testing
purposes. This is useful for checking features-detection.
### NFD-Master
When running as a standalone container labeling is expected to fail because
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Kubernetes API is not available. Thus, it is recommended to use `--no-publish`
command line flag. E.g.
```
$ docker run --rm --name=nfd-test <NFD_CONTAINER_IMAGE> nfd-master --no-publish
2019/02/01 14:48:21 Node Feature Discovery Master <NFD_VERSION>
2019/02/01 14:48:21 gRPC server serving on port: 8080
```
Command line flags of nfd-master:
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```
$ docker run --rm <NFD_CONTAINER_IMAGE> nfd-master --help
...
nfd-master.
Usage:
nfd-master [--no-publish] [--label-whitelist=<pattern>] [--port=<port>]
[--ca-file=<path>] [--cert-file=<path>] [--key-file=<path>]
[--verify-node-name] [--extra-label-ns=<list>] [--resource-labels=<list>]
nfd-master -h | --help
nfd-master --version
Options:
-h --help Show this screen.
--version Output version and exit.
--port=<port> Port on which to listen for connections.
[Default: 8080]
--ca-file=<path> Root certificate for verifying connections
[Default: ]
--cert-file=<path> Certificate used for authenticating connections
[Default: ]
--key-file=<path> Private key matching --cert-file
[Default: ]
--verify-node-name Verify worker node name against CN from the TLS
certificate. Only has effect when TLS authentication
has been enabled.
--no-publish Do not publish feature labels
--label-whitelist=<pattern> Regular expression to filter label names to
publish to the Kubernetes API server.
NB: the label namespace is omitted i.e. the filter
is only applied to the name part after '/'.
[Default: ]
--extra-label-ns=<list> Comma separated list of allowed extra label namespaces
[Default: ]
--resource-labels=<list> Comma separated list of labels to be exposed as extended resources.
[Default: ]
```
### NFD-Worker
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In order to run nfd-worker as a "stand-alone" container against your
standalone nfd-master you need to run them in the same network namespace:
```
$ docker run --rm --network=container:nfd-test <NFD_CONTAINER_IMAGE> nfd-worker
2019/02/01 14:48:56 Node Feature Discovery Worker <NFD_VERSION>
...
```
If you just want to try out feature discovery without connecting to nfd-master,
pass the `--no-publish` flag to nfd-worker.
Command line flags of nfd-worker:
```
$ docker run --rm <CONTAINER_IMAGE_ID> nfd-worker --help
...
nfd-worker.
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Usage:
nfd-worker [--no-publish] [--sources=<sources>] [--label-whitelist=<pattern>]
[--oneshot | --sleep-interval=<seconds>] [--config=<path>]
[--options=<config>] [--server=<server>] [--server-name-override=<name>]
[--ca-file=<path>] [--cert-file=<path>] [--key-file=<path>]
nfd-worker -h | --help
nfd-worker --version
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Options:
-h --help Show this screen.
--version Output version and exit.
--config=<path> Config file to use.
[Default: /etc/kubernetes/node-feature-discovery/nfd-worker.conf]
--options=<config> Specify config options from command line. Config
options are specified in the same format as in the
config file (i.e. json or yaml). These options
will override settings read from the config file.
[Default: ]
--ca-file=<path> Root certificate for verifying connections
[Default: ]
--cert-file=<path> Certificate used for authenticating connections
[Default: ]
--key-file=<path> Private key matching --cert-file
[Default: ]
--server=<server> NFD server address to connecto to.
[Default: localhost:8080]
--server-name-override=<name> Name (CN) expect from server certificate, useful
in testing
[Default: ]
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--sources=<sources> Comma separated list of feature sources.
[Default: cpu,custom,iommu,kernel,local,memory,network,pci,storage,system,usb]
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--no-publish Do not publish discovered features to the
cluster-local Kubernetes API server.
--label-whitelist=<pattern> Regular expression to filter label names to
publish to the Kubernetes API server.
NB: the label namespace is omitted i.e. the filter
is only applied to the name part after '/'.
[Default: ]
--oneshot Label once and exit.
--sleep-interval=<seconds> Time to sleep between re-labeling. Non-positive
value implies no re-labeling (i.e. infinite
sleep). [Default: 60s]
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```
**NOTE** Some feature sources need certain directories and/or files from the
host mounted inside the NFD container. Thus, you need to provide Docker with the
correct `--volume` options in order for them to work correctly when run
stand-alone directly with `docker run`. See the
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[template spec](https://github.com/kubernetes-sigs/node-feature-discovery/blob/master/nfd-worker-daemonset.yaml.template)
for up-to-date information about the required volume mounts.
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## Feature discovery
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### Feature sources
The current set of feature sources are the following:
- CPU
- Custom
- IOMMU
- Kernel
- Memory
- Network
- PCI
- Storage
- System
usb: Add support for USB device discovery This builds on the PCI support to enable the discovery of USB devices. This is primarily intended to be used for the discovery of Edge-based heterogeneous accelerators that are connected via USB, such as the Coral USB Accelerator and the Intel NCS2 - our main motivation for adding this capability to NFD, and as part of our work in the SODALITE H2020 project. USB devices may define their base class at either the device or interface levels. In the case where no device class is set, the per-device interfaces are enumerated instead. USB devices may furthermore have multiple interfaces, which may or may not use the identical class across each interface. We therefore report device existence for each unique class definition to enable more fine-grained labelling and node selection. The default labelling format includes the class, vendor and device (product) IDs, as follows: feature.node.kubernetes.io/usb-fe_1a6e_089a.present=true As with PCI, a subset of device classes are whitelisted for matching. By default, there are only a subset of device classes under which accelerators tend to be mapped, which is used as the basis for the whitelist. These are: - Video - Miscellaneous - Application Specific - Vendor Specific For those interested in matching other classes, this may be extended by using the UsbId rule provided through the custom source. A full list of class codes is provided by the USB-IF at: https://www.usb.org/defined-class-codes For the moment, owing to a lack of a demonstrable use case, neither the subclass nor the protocol information are exposed. If this becomes necessary, support for these attributes can be trivially added. Signed-off-by: Paul Mundt <paul.mundt@adaptant.io>
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- USB
- Local (hooks for user-specific features)
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### Feature labels
The published node labels encode a few pieces of information:
- Namespace, i.e. `feature.node.kubernetes.io`
- The source for each label (e.g. `cpu`).
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- The name of the discovered feature as it appears in the underlying
source, (e.g. `cpuid.AESNI` from cpu).
- The value of the discovered feature.
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Feature label names adhere to the following pattern:
```
<namespace>/<source name>-<feature name>[.<attribute name>]
```
The last component (i.e. `attribute-name`) is optional, and only used if a
feature logically has sub-hierarchy, e.g. `sriov.capable` and
`sriov.configure` from the `network` source.
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```json
{
"feature.node.kubernetes.io/cpu-<feature-name>": "true",
"feature.node.kubernetes.io/custom-<feature-name>": "true",
"feature.node.kubernetes.io/iommu-<feature-name>": "true",
"feature.node.kubernetes.io/kernel-<feature name>": "<feature value>",
"feature.node.kubernetes.io/memory-<feature-name>": "true",
"feature.node.kubernetes.io/network-<feature-name>": "true",
"feature.node.kubernetes.io/pci-<device label>.present": "true",
"feature.node.kubernetes.io/storage-<feature-name>": "true",
"feature.node.kubernetes.io/system-<feature name>": "<feature value>",
usb: Add support for USB device discovery This builds on the PCI support to enable the discovery of USB devices. This is primarily intended to be used for the discovery of Edge-based heterogeneous accelerators that are connected via USB, such as the Coral USB Accelerator and the Intel NCS2 - our main motivation for adding this capability to NFD, and as part of our work in the SODALITE H2020 project. USB devices may define their base class at either the device or interface levels. In the case where no device class is set, the per-device interfaces are enumerated instead. USB devices may furthermore have multiple interfaces, which may or may not use the identical class across each interface. We therefore report device existence for each unique class definition to enable more fine-grained labelling and node selection. The default labelling format includes the class, vendor and device (product) IDs, as follows: feature.node.kubernetes.io/usb-fe_1a6e_089a.present=true As with PCI, a subset of device classes are whitelisted for matching. By default, there are only a subset of device classes under which accelerators tend to be mapped, which is used as the basis for the whitelist. These are: - Video - Miscellaneous - Application Specific - Vendor Specific For those interested in matching other classes, this may be extended by using the UsbId rule provided through the custom source. A full list of class codes is provided by the USB-IF at: https://www.usb.org/defined-class-codes For the moment, owing to a lack of a demonstrable use case, neither the subclass nor the protocol information are exposed. If this becomes necessary, support for these attributes can be trivially added. Signed-off-by: Paul Mundt <paul.mundt@adaptant.io>
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"feature.node.kubernetes.io/usb-<device label>.present": "<feature value>",
"feature.node.kubernetes.io/<file name>-<feature name>": "<feature value>"
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}
```
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The `--sources` flag controls which sources to use for discovery.
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_Note: Consecutive runs of nfd-worker will update the labels on a
given node. If features are not discovered on a consecutive run, the corresponding
label will be removed. This includes any restrictions placed on the consecutive run,
such as restricting discovered features with the --label-whitelist option._
### CPU Features
| Feature name | Attribute | Description |
| ----------------------- | ------------------ | ----------------------------- |
| cpuid | &lt;cpuid flag&gt; | CPU capability is supported
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| hardware_multithreading | <br> | Hardware multithreading, such as Intel HTT, enabled (number of logical CPUs is greater than physical CPUs)
| power | sst_bf.enabled | Intel SST-BF ([Intel Speed Select Technology][intel-sst] - Base frequency) enabled
| [pstate][intel-pstate] | turbo | Set to 'true' if turbo frequencies are enabled in Intel pstate driver, set to 'false' if they have been disabled.
| [rdt][intel-rdt] | RDTMON | Intel RDT Monitoring Technology
| <br> | RDTCMT | Intel Cache Monitoring (CMT)
| <br> | RDTMBM | Intel Memory Bandwidth Monitoring (MBM)
| <br> | RDTL3CA | Intel L3 Cache Allocation Technology
| <br> | RDTL2CA | Intel L2 Cache Allocation Technology
| <br> | RDTMBA | Intel Memory Bandwidth Allocation (MBA) Technology
The (sub-)set of CPUID attributes to publish is configurable via the
`attributeBlacklist` and `attributeWhitelist` cpuid options of the cpu source.
If whitelist is specified, only whitelisted attributes will be published. With
blacklist, only blacklisted attributes are filtered out. `attributeWhitelist`
has priority over `attributeBlacklist`. For examples and more information
about configurability, see [Configuration Options](#configuration-options).
By default, the following CPUID flags have been blacklisted:
BMI1, BMI2, CLMUL, CMOV, CX16, ERMS, F16C, HTT, LZCNT, MMX, MMXEXT, NX, POPCNT,
RDRAND, RDSEED, RDTSCP, SGX, SSE, SSE2, SSE3, SSE4.1, SSE4.2 and SSSE3.
**NOTE** The cpuid features advertise *supported* CPU capabilities, that is, a
capability might be supported but not enabled.
#### X86 CPUID Attributes (Partial List)
| Attribute | Description |
| --------- | ---------------------------------------------------------------- |
| ADX | Multi-Precision Add-Carry Instruction Extensions (ADX)
| AESNI | Advanced Encryption Standard (AES) New Instructions (AES-NI)
| AVX | Advanced Vector Extensions (AVX)
| AVX2 | Advanced Vector Extensions 2 (AVX2)
#### Arm CPUID Attribute (Partial List)
| Attribute | Description |
| --------- | ---------------------------------------------------------------- |
| IDIVA | Integer divide instructions available in ARM mode
| IDIVT | Integer divide instructions available in Thumb mode
| THUMB | Thumb instructions
| FASTMUL | Fast multiplication
| VFP | Vector floating point instruction extension (VFP)
| VFPv3 | Vector floating point extension v3
| VFPv4 | Vector floating point extension v4
| VFPD32 | VFP with 32 D-registers
| HALF | Half-word loads and stores
| EDSP | DSP extensions
| NEON | NEON SIMD instructions
| LPAE | Large Physical Address Extensions
#### Arm64 CPUID Attribute (Partial List)
| Attribute | Description |
| --------- | ---------------------------------------------------------------- |
| AES | Announcing the Advanced Encryption Standard
| EVSTRM | Event Stream Frequency Features
| FPHP | Half Precision(16bit) Floating Point Data Processing Instructions
| ASIMDHP | Half Precision(16bit) Asimd Data Processing Instructions
| ATOMICS | Atomic Instructions to the A64
| ASIMRDM | Support for Rounding Double Multiply Add/Subtract
| PMULL | Optional Cryptographic and CRC32 Instructions
| JSCVT | Perform Conversion to Match Javascript
| DCPOP | Persistent Memory Support
### Custom Features
The Custom feature source allows the user to define features based on a mix of predefined rules.
A rule is provided input witch affects its process of matching for a defined feature.
To aid in making Custom Features clearer, we define a general and a per rule nomenclature, keeping things as
consistent as possible.
#### General Nomenclature & Definitions
```
Rule :Represents a matching logic that is used to match on a feature.
Rule Input :The input a Rule is provided. This determines how a Rule performs the match operation.
Matcher :A composition of Rules, each Matcher may be composed of at most one instance of each Rule.
```
#### Custom Features Format (using the Nomenclature defined above)
```yaml
- name: <feature name>
matchOn:
- <Rule-1>: <Rule-1 Input>
[<Rule-2>: <Rule-2 Input>]
- <Matcher-2>
- ...
- ...
- <Matcher-N>
- <custom feature 2>
- ...
- ...
- <custom feature M>
```
#### Matching process
Specifying Rules to match on a feature is done by providing a list of Matchers.
Each Matcher contains one or more Rules.
Logical _OR_ is performed between Matchers and logical _AND_ is performed between Rules
of a given Matcher.
#### Rules
##### PciId Rule
###### Nomenclature
```
Attribute :A PCI attribute.
Element :An identifier of the PCI attribute.
```
The PciId Rule allows matching the PCI devices in the system on the following Attributes: `class`,`vendor` and
`device`. A list of Elements is provided for each Attribute.
###### Format
```yaml
pciId :
class: [<class id>, ...]
vendor: [<vendor id>, ...]
device: [<device id>, ...]
```
Matching is done by performing a logical _OR_ between Elements of an Attribute and logical _AND_ between the specified Attributes for
each PCI device in the system.
At least one Attribute must be specified. Missing attributes will not partake in the matching process.
usb: Add support for USB device discovery This builds on the PCI support to enable the discovery of USB devices. This is primarily intended to be used for the discovery of Edge-based heterogeneous accelerators that are connected via USB, such as the Coral USB Accelerator and the Intel NCS2 - our main motivation for adding this capability to NFD, and as part of our work in the SODALITE H2020 project. USB devices may define their base class at either the device or interface levels. In the case where no device class is set, the per-device interfaces are enumerated instead. USB devices may furthermore have multiple interfaces, which may or may not use the identical class across each interface. We therefore report device existence for each unique class definition to enable more fine-grained labelling and node selection. The default labelling format includes the class, vendor and device (product) IDs, as follows: feature.node.kubernetes.io/usb-fe_1a6e_089a.present=true As with PCI, a subset of device classes are whitelisted for matching. By default, there are only a subset of device classes under which accelerators tend to be mapped, which is used as the basis for the whitelist. These are: - Video - Miscellaneous - Application Specific - Vendor Specific For those interested in matching other classes, this may be extended by using the UsbId rule provided through the custom source. A full list of class codes is provided by the USB-IF at: https://www.usb.org/defined-class-codes For the moment, owing to a lack of a demonstrable use case, neither the subclass nor the protocol information are exposed. If this becomes necessary, support for these attributes can be trivially added. Signed-off-by: Paul Mundt <paul.mundt@adaptant.io>
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##### UsbId Rule
###### Nomenclature
```
Attribute :A USB attribute.
Element :An identifier of the USB attribute.
```
The UsbId Rule allows matching the USB devices in the system on the following Attributes: `class`,`vendor` and
`device`. A list of Elements is provided for each Attribute.
###### Format
```yaml
usbId :
class: [<class id>, ...]
vendor: [<vendor id>, ...]
device: [<device id>, ...]
```
Matching is done by performing a logical _OR_ between Elements of an Attribute and logical _AND_ between the specified Attributes for
each USB device in the system.
At least one Attribute must be specified. Missing attributes will not partake in the matching process.
##### LoadedKMod Rule
###### Nomenclature
```
Element :A kernel module
```
The LoadedKMod Rule allows matching the loaded kernel modules in the system against a provided list of Elements.
###### Format
```yaml
loadedKMod : [<kernel module>, ...]
```
Matching is done by performing logical _AND_ for each provided Element, i.e the Rule will match if all provided Elements (kernel modules) are loaded
in the system.
#### Example
```yaml
custom:
- name: "my.kernel.feature"
matchOn:
- loadedKMod: ["kmod1", "kmod2"]
- name: "my.pci.feature"
matchOn:
- pciId:
vendor: ["15b3"]
device: ["1014", "1017"]
usb: Add support for USB device discovery This builds on the PCI support to enable the discovery of USB devices. This is primarily intended to be used for the discovery of Edge-based heterogeneous accelerators that are connected via USB, such as the Coral USB Accelerator and the Intel NCS2 - our main motivation for adding this capability to NFD, and as part of our work in the SODALITE H2020 project. USB devices may define their base class at either the device or interface levels. In the case where no device class is set, the per-device interfaces are enumerated instead. USB devices may furthermore have multiple interfaces, which may or may not use the identical class across each interface. We therefore report device existence for each unique class definition to enable more fine-grained labelling and node selection. The default labelling format includes the class, vendor and device (product) IDs, as follows: feature.node.kubernetes.io/usb-fe_1a6e_089a.present=true As with PCI, a subset of device classes are whitelisted for matching. By default, there are only a subset of device classes under which accelerators tend to be mapped, which is used as the basis for the whitelist. These are: - Video - Miscellaneous - Application Specific - Vendor Specific For those interested in matching other classes, this may be extended by using the UsbId rule provided through the custom source. A full list of class codes is provided by the USB-IF at: https://www.usb.org/defined-class-codes For the moment, owing to a lack of a demonstrable use case, neither the subclass nor the protocol information are exposed. If this becomes necessary, support for these attributes can be trivially added. Signed-off-by: Paul Mundt <paul.mundt@adaptant.io>
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- name: "my.usb.feature"
matchOn:
- usbId:
vendor: ["1d6b"]
device: ["0003"]
- name: "my.combined.feature"
matchOn:
- loadedKMod : ["vendor_kmod1", "vendor_kmod2"]
pciId:
vendor: ["15b3"]
device: ["1014", "1017"]
- name: "my.accumulated.feature"
matchOn:
- loadedKMod : ["some_kmod1", "some_kmod2"]
- pciId:
vendor: ["15b3"]
device: ["1014", "1017"]
```
__In the example above:__
- A node would contain the label: `feature.node.kubernetes.io/custom-my.kernel.feature=true`
if the node has `kmod1` _AND_ `kmod2` kernel modules loaded.
- A node would contain the label: `feature.node.kubernetes.io/custom-my.pci.feature=true`
if the node contains a PCI device with a PCI vendor ID of `15b3` _AND_ PCI device ID of `1014` _OR_ `1017`.
usb: Add support for USB device discovery This builds on the PCI support to enable the discovery of USB devices. This is primarily intended to be used for the discovery of Edge-based heterogeneous accelerators that are connected via USB, such as the Coral USB Accelerator and the Intel NCS2 - our main motivation for adding this capability to NFD, and as part of our work in the SODALITE H2020 project. USB devices may define their base class at either the device or interface levels. In the case where no device class is set, the per-device interfaces are enumerated instead. USB devices may furthermore have multiple interfaces, which may or may not use the identical class across each interface. We therefore report device existence for each unique class definition to enable more fine-grained labelling and node selection. The default labelling format includes the class, vendor and device (product) IDs, as follows: feature.node.kubernetes.io/usb-fe_1a6e_089a.present=true As with PCI, a subset of device classes are whitelisted for matching. By default, there are only a subset of device classes under which accelerators tend to be mapped, which is used as the basis for the whitelist. These are: - Video - Miscellaneous - Application Specific - Vendor Specific For those interested in matching other classes, this may be extended by using the UsbId rule provided through the custom source. A full list of class codes is provided by the USB-IF at: https://www.usb.org/defined-class-codes For the moment, owing to a lack of a demonstrable use case, neither the subclass nor the protocol information are exposed. If this becomes necessary, support for these attributes can be trivially added. Signed-off-by: Paul Mundt <paul.mundt@adaptant.io>
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- A node would contain the label: `feature.node.kubernetes.io/custom-my.usb.feature=true`
if the node contains a USB device with a USB vendor ID of `1d6b` _AND_ USB device ID of `0003`.
- A node would contain the label: `feature.node.kubernetes.io/custom-my.combined.feature=true`
if `vendor_kmod1` _AND_ `vendor_kmod2` kernel modules are loaded __AND__ the node contains a PCI device
with a PCI vendor ID of `15b3` _AND_ PCI device ID of `1014` _or_ `1017`.
- A node would contain the label: `feature.node.kubernetes.io/custom-my.accumulated.feature=true`
if `some_kmod1` _AND_ `some_kmod2` kernel modules are loaded __OR__ the node contains a PCI device
with a PCI vendor ID of `15b3` _AND_ PCI device ID of `1014` _OR_ `1017`.
#### Statically defined features
Some feature labels which are common and generic are defined statically in the `custom` feature source.
A user may add additional Matchers to these feature labels by defining them in the `nfd-worker` configuration file.
| Feature | Attribute | Description |
| ------- | --------- | -----------|
| rdma | capable | The node has an RDMA capable Network adapter |
| rdma | enabled | The node has the needed RDMA modules loaded to run RDMA traffic |
### IOMMU Features
| Feature name | Description |
| :------------: | :---------------------------------------------------------------------------------: |
| enabled | IOMMU is present and enabled in the kernel
### Kernel Features
| Feature | Attribute | Description |
| ------- | ------------------- | -------------------------------------------- |
| config | &lt;option name&gt; | Kernel config option is enabled (set 'y' or 'm').<br> Default options are `NO_HZ`, `NO_HZ_IDLE`, `NO_HZ_FULL` and `PREEMPT`
| selinux | enabled | Selinux is enabled on the node
| version | full | Full kernel version as reported by `/proc/sys/kernel/osrelease` (e.g. '4.5.6-7-g123abcde')
| <br> | major | First component of the kernel version (e.g. '4')
| <br> | minor | Second component of the kernel version (e.g. '5')
| <br> | revision | Third component of the kernel version (e.g. '6')
Kernel config file to use, and, the set of config options to be detected are
configurable.
See [configuration options](#configuration-options) for more information.
### Memory Features
| Feature | Attribute | Description |
| ------- | --------- | ------------------------------------------------------ |
| numa | <br> | Multiple memory nodes i.e. NUMA architecture detected
| nv | present | NVDIMM device(s) are present
| nv | dax | NVDIMM region(s) configured in DAX mode are present
### Network Features
| Feature | Attribute | Description |
| ------- | ---------- | ----------------------------------------------------- |
| sriov | capable | [Single Root Input/Output Virtualization][sriov] (SR-IOV) enabled Network Interface Card(s) present
| <br> | configured | SR-IOV virtual functions have been configured
### PCI Features
| Feature | Attribute | Description |
| -------------------- | ------------- | ----------------------------------------- |
| &lt;device label&gt; | present | PCI device is detected
| &lt;device label&gt; | sriov.capable | [Single Root Input/Output Virtualization][sriov] (SR-IOV) enabled PCI device present
`<device label>` is composed of raw PCI IDs, separated by underscores.
The set of fields used in `<device label>` is configurable, valid fields being
`class`, `vendor`, `device`, `subsystem_vendor` and `subsystem_device`.
Defaults are `class` and `vendor`. An example label using the default
label fields:
```
feature.node.kubernetes.io/pci-1200_8086.present=true
```
Also the set of PCI device classes that the feature source detects is
configurable. By default, device classes (0x)03, (0x)0b40 and (0x)12, i.e.
GPUs, co-processors and accelerator cards are detected.
usb: Add support for USB device discovery This builds on the PCI support to enable the discovery of USB devices. This is primarily intended to be used for the discovery of Edge-based heterogeneous accelerators that are connected via USB, such as the Coral USB Accelerator and the Intel NCS2 - our main motivation for adding this capability to NFD, and as part of our work in the SODALITE H2020 project. USB devices may define their base class at either the device or interface levels. In the case where no device class is set, the per-device interfaces are enumerated instead. USB devices may furthermore have multiple interfaces, which may or may not use the identical class across each interface. We therefore report device existence for each unique class definition to enable more fine-grained labelling and node selection. The default labelling format includes the class, vendor and device (product) IDs, as follows: feature.node.kubernetes.io/usb-fe_1a6e_089a.present=true As with PCI, a subset of device classes are whitelisted for matching. By default, there are only a subset of device classes under which accelerators tend to be mapped, which is used as the basis for the whitelist. These are: - Video - Miscellaneous - Application Specific - Vendor Specific For those interested in matching other classes, this may be extended by using the UsbId rule provided through the custom source. A full list of class codes is provided by the USB-IF at: https://www.usb.org/defined-class-codes For the moment, owing to a lack of a demonstrable use case, neither the subclass nor the protocol information are exposed. If this becomes necessary, support for these attributes can be trivially added. Signed-off-by: Paul Mundt <paul.mundt@adaptant.io>
2020-05-14 20:32:55 +00:00
### USB Features
| Feature | Attribute | Description |
| -------------------- | ------------- | ----------------------------------------- |
| &lt;device label&gt; | present | USB device is detected
`<device label>` is composed of raw USB IDs, separated by underscores.
The set of fields used in `<device label>` is configurable, valid fields being
`class`, `vendor`, and `device`.
Defaults are `class`, `vendor` and `device`. An example label using the default
label fields:
```
feature.node.kubernetes.io/usb-fe_1a6e_089a.present=true
```
See [configuration options](#configuration-options)
for more information on NFD config.
### Storage Features
| Feature name | Description |
| :--------------: | :---------------------------------------------------------------------------------: |
| nonrotationaldisk | Non-rotational disk, like SSD, is present in the node
### System Features
| Feature | Attribute | Description |
| ----------- | ---------------- | --------------------------------------------|
| os_release | ID | Operating system identifier
| <br> | VERSION_ID | Operating system version identifier (e.g. '6.7')
| <br> | VERSION_ID.major | First component of the OS version id (e.g. '6')
| <br> | VERSION_ID.minor | Second component of the OS version id (e.g. '7')
### Feature Detector Hooks (User-specific Features)
NFD has a special feature source named *local* which is designed for getting the
labels from user-specific feature detector. It provides a mechanism for users to
implement custom feature sources in a pluggable way, without modifying nfd
source code or Docker images. The local feature source can be used to advertise
new user-specific features, and, for overriding labels created by the other
feature sources.
The *local* feature source gets its labels by two different ways:
* It tries to execute files found under `/etc/kubernetes/node-feature-discovery/source.d/`
directory. The hook files must be executable and they are supposed to print all
discovered features in `stdout`, one per line. With ELF binaries static
linking is recommended as the selection of system libraries available in the
NFD release image is very limited. Other runtimes currently supported by the
NFD stock image are bash and perl.
* It reads files found under `/etc/kubernetes/node-feature-discovery/features.d/`
directory. The file content is expected to be similar to the hook output (described above).
These directories must be available inside the Docker image so Volumes and
VolumeMounts must be used if standard NFD images are used. The given template
files mount by default the `source.d` and the `features.d` directories
respectively from `/etc/kubernetes/node-feature-discovery/source.d/` and
`/etc/kubernetes/node-feature-discovery/features.d/` from the host. You should
update them to match your needs.
In both cases, the labels can be binary or non binary, using either `<name>` or
`<name>=<value>` format.
Unlike the other feature sources, the name of the file, instead of the name of
the feature source (that would be `local` in this case), is used as a prefix in
the label name, normally. However, if the `<name>` of the label starts with a
slash (`/`) it is used as the label name as is, without any additional prefix.
This makes it possible for the user to fully control the feature label names,
e.g. for overriding labels created by other feature sources.
You can also override the default namespace of your labels using this format:
`<namespace>/<name>[=<value>]`. You must whitelist your namespace using the
`--extra-label-ns` option on the master. In this case, the name of the
file will not be added to the label name. For example, if you want to add the
label `my.namespace.org/my-label=value`, your hook output or file must contains
`my.namespace.org/my-label=value` and you must add
`--extra-label-ns=my.namespace.org` on the master command line.
`stderr` output of the hooks is propagated to NFD log so it can be used for
debugging and logging.
#### Injecting Labels from Other Pods
One use case for the hooks and/or feature files is detecting features in other
Pods outside NFD, e.g. in Kubernetes device plugins. It is possible to mount
the `source.d` and/or `features.d` directories common with the NFD Pod and
deploy the custom hooks/features there. NFD will periodically scan the
directories and run any hooks and read any feature files it finds. The
[example nfd-worker deployment template](https://github.com/kubernetes-sigs/node-feature-discovery/blob/master/nfd-worker-daemonset.yaml.template#L69)
contains `hostPath` mounts for `sources.d` and `features.d` directories. By
using the same mounts in the secondary Pod (e.g. device plugin) you have
created a shared area for delivering hooks and feature files to NFD.
#### A Hook Example
User has a shell script
`/etc/kubernetes/node-feature-discovery/source.d/my-source` which has the
following `stdout` output:
```
MY_FEATURE_1
MY_FEATURE_2=myvalue
/override_source-OVERRIDE_BOOL
/override_source-OVERRIDE_VALUE=123
override.namespace/value=456
```
which, in turn, will translate into the following node labels:
```
feature.node.kubernetes.io/my-source-MY_FEATURE_1=true
feature.node.kubernetes.io/my-source-MY_FEATURE_2=myvalue
feature.node.kubernetes.io/override_source-OVERRIDE_BOOL=true
feature.node.kubernetes.io/override_source-OVERRIDE_VALUE=123
override.namespace/value=456
```
#### A File Example
User has a file
`/etc/kubernetes/node-feature-discovery/features.d/my-source` which contains the
following lines:
```
MY_FEATURE_1
MY_FEATURE_2=myvalue
/override_source-OVERRIDE_BOOL
/override_source-OVERRIDE_VALUE=123
override.namespace/value=456
```
which, in turn, will translate into the following node labels:
```
feature.node.kubernetes.io/my-source-MY_FEATURE_1=true
feature.node.kubernetes.io/my-source-MY_FEATURE_2=myvalue
feature.node.kubernetes.io/override_source-OVERRIDE_BOOL=true
feature.node.kubernetes.io/override_source-OVERRIDE_VALUE=123
override.namespace/value=456
```
NFD tries to run any regular files found from the hooks directory. Any
additional data files your hook might need (e.g. a configuration file) should
be placed in a separate directory in order to avoid NFD unnecessarily trying to
execute these. You can use a subdirectory under the hooks directory, for
example `/etc/kubernetes/node-feature-discovery/source.d/conf/`.
**NOTE!** NFD will blindly run any executables placed/mounted in the hooks
directory. It is the user's responsibility to review the hooks for e.g.
possible security implications.
**NOTE!** Be careful when creating and/or updating hook or feature files while
NFD is running. In order to avoid race conditions you should write into a
temporary file (outside the `source.d` and `features.d` directories), and,
atomically create/update the original file by doing a filesystem move
operation.
## Extended resources (experimental)
This feature is experimental and by no means a replacement for the usage of
device plugins.
Labels which have integer values, can be promoted to Kubernetes extended
resources by listing them to the master `--resource-labels` command line flag.
These labels won't then show in the node label section, they will appear only
as extended resources.
An example use-case for the extended resources could be based on a hook which
creates a label for the node SGX EPC memory section size. By giving the name of
that label in the `--resource-labels` flag, that value will then turn into an
extended resource of the node, allowing PODs to request that resource and the
Kubernetes scheduler to schedule such PODs to only those nodes which have a
sufficient capacity of said resource left.
Similar to labels, the default namespace `feature.node.kubernetes.io` is
automatically prefixed to the extended resource, if the promoted label doesn't
have a namespace.
Example usage of the command line arguments, using a new namespace:
`nfd-master --resource-labels=my_source-my.feature,sgx.some.ns/epc --extra-label-ns=sgx.some.ns`
The above would result in following extended resources provided that related
labels exist:
```
sgx.some.ns/epc: <label value>
feature.node.kubernetes.io/my_source-my.feature: <label value>
```
## Getting started
For a stable version with ready-built images see the
[latest released version](https://github.com/kubernetes-sigs/node-feature-discovery/tree/v0.6.0) ([release notes](https://github.com/kubernetes-sigs/node-feature-discovery/releases/latest)).
If you want to use the latest development version (master branch) you need to
[build your own custom image](#building-from-source).
### System requirements
1. Linux (x86_64/Arm64/Arm)
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1. [kubectl][kubectl-setup] (properly set up and configured to work with your
Kubernetes cluster)
2019-05-08 11:44:16 +00:00
1. [Docker][docker-down] (only required to build and push docker images)
### Usage
#### nfd-master
Nfd-master runs as a deployment (with a replica count of 1), by default
it prefers running on the cluster's master nodes but will run on worker
nodes if no master nodes are found.
For High Availability, you should simply increase the replica count of
the deployment object. You should also look into adding [inter-pod](https://kubernetes.io/docs/concepts/configuration/assign-pod-node/#affinity-and-anti-affinity)
affinity to prevent masters from running on the same node.
However note that inter-pod affinity is costly and is not recommended
in bigger clusters.
You can use the template spec provided to deploy nfd-master, or
use `nfd-master.yaml` generated by `Makefile`. The latter includes
`image:` and `namespace:` definitions that match the latest built
image. Example:
```
make IMAGE_TAG=<IMAGE_TAG>
docker push <IMAGE_TAG>
kubectl create -f nfd-master.yaml
```
Nfd-master listens for connections from nfd-worker(s) and connects to the
Kubernetes API server to add node labels advertised by them.
If you have RBAC authorization enabled (as is the default e.g. with clusters
initialized with kubeadm) you need to configure the appropriate ClusterRoles,
ClusterRoleBindings and a ServiceAccount in order for NFD to create node
labels. The provided template will configure these for you.
#### nfd-worker
Nfd-worker is preferably run as a Kubernetes DaemonSet. There is an
example spec (`nfd-worker-daemonset.yaml.template`) that can be used
as a template, or, as is when just trying out the service. Similarly
to nfd-master above, the `Makefile` also generates
`nfd-worker-daemonset.yaml` from the template that you can use to
deploy the latest image. Example:
```
make IMAGE_TAG=<IMAGE_TAG>
docker push <IMAGE_TAG>
kubectl create -f nfd-worker-daemonset.yaml
```
Nfd-worker connects to the nfd-master service to advertise hardware features.
When run as a daemonset, nodes are re-labeled at an interval specified using
the `--sleep-interval` option. In the
[template](https://github.com/kubernetes-sigs/node-feature-discovery/blob/master/nfd-worker-daemonset.yaml.template#L26)
the default interval is set to 60s which is also the default when no
`--sleep-interval` is specified. Also, the configuration file is re-read on
each iteration providing a simple mechanism of run-time reconfiguration.
Feature discovery can alternatively be configured as a one-shot job. There is
an example script in this repo that demonstrates how to deploy the job in the cluster.
```
./label-nodes.sh [<IMAGE_TAG>]
```
The label-nodes.sh script tries to launch as many jobs as there are Ready nodes.
Note that this approach does not guarantee running once on every node.
For example, if some node is tainted NoSchedule or fails to start a job for some other reason, then some other node will run extra job instance(s) to satisfy the request and the tainted/failed node does not get labeled.
#### nfd-master and nfd-worker in the same Pod
You can also run nfd-master and nfd-worker inside a single pod (skip the `sed`
part if running the latest released version):
```
sed -E s',^(\s*)image:.+$,\1image: <YOUR_IMAGE_REPO>:<YOUR_IMAGE_TAG>,' nfd-daemonset-combined.yaml.template > nfd-daemonset-combined.yaml
kubectl apply -f nfd-daemonset-combined.yaml
```
Similar to the nfd-worker setup above, this creates a DaemonSet that schedules
an NFD Pod an all worker nodes, with the difference that the Pod also also
contains an nfd-master instance. In this case no nfd-master service is run on
the master node(s), but, the worker nodes are able to label themselves.
This may be desirable e.g. in single-node setups.
#### TLS authentication
NFD supports mutual TLS authentication between the nfd-master and nfd-worker
instances. That is, nfd-worker and nfd-master both verify that the other end
presents a valid certificate.
TLS authentication is enabled by specifying `--ca-file`, `--key-file` and
`--cert-file` args, on both the nfd-master and nfd-worker instances.
The template specs provided with NFD contain (commented out) example
configuration for enabling TLS authentication.
The Common Name (CN) of the nfd-master certificate must match the DNS name of
the nfd-master Service of the cluster. By default, nfd-master only check that
the nfd-worker has been signed by the specified root certificate (--ca-file).
Additional hardening can be enabled by specifying --verify-node-name in
nfd-master args, in which case nfd-master verifies that the NodeName presented
by nfd-worker matches the Common Name (CN) of its certificate. This means that
each nfd-worker requires a individual node-specific TLS certificate.
#### Usage demo
2019-05-20 15:02:39 +00:00
[![asciicast](https://asciinema.org/a/247316.svg)](https://asciinema.org/a/247316)
### Configuration options
Nfd-worker supports a configuration file. The default location is
`/etc/kubernetes/node-feature-discovery/nfd-worker.conf`, but,
this can be changed by specifying the`--config` command line flag.
Configuration file is re-read on each labeling pass (determined by
`--sleep-interval`) which makes run-time re-configuration of nfd-worker
possible.
Worker configuration file is read inside the container, and thus, Volumes and
VolumeMounts are needed to make your configuration available for NFD. The
preferred method is to use a ConfigMap which provides easy deployment and
re-configurability. For example, create a config map using the example config
as a template:
```
cp nfd-worker.conf.example nfd-worker.conf
vim nfd-worker.conf # edit the configuration
kubectl create configmap nfd-worker-config --from-file=nfd-worker.conf
```
Then, configure Volumes and VolumeMounts in the Pod spec (just the relevant
snippets shown below):
```
...
containers:
volumeMounts:
- name: nfd-worker-config
mountPath: "/etc/kubernetes/node-feature-discovery/"
...
volumes:
- name: nfd-worker-config
configMap:
name: nfd-worker-config
...
```
You could also use other types of volumes, of course. That is, hostPath if
different config for different nodes would be required, for example.
The (empty-by-default)
[example config](https://github.com/kubernetes-sigs/node-feature-discovery/blob/master/nfd-worker.conf.example)
is used as a config in the NFD Docker image. Thus, this can be used as a default
configuration in custom-built images.
Configuration options can also be specified via the `--options` command line
flag, in which case no mounts need to be used. The same format as in the config
file must be used, i.e. JSON (or YAML). For example:
```
--options='{"sources": { "pci": { "deviceClassWhitelist": ["12"] } } }'
```
Configuration options specified from the command line will override those read
from the config file.
Currently, the only available configuration options are related to the
[CPU](#cpu-features), [PCI](#pci-features) and [Kernel](#kernel-features)
feature sources.
## Building from source
**Download the source code:**
```
git clone https://github.com/kubernetes-sigs/node-feature-discovery
```
**Build the container image:**<br>
See [customizing the build](#customizing-the-build) below for altering the
container image registry, for example.
```
cd <project-root>
make
```
**Push the container image:**<br>
Optional, this example with Docker.
```
docker push <IMAGE_TAG>
```
**Change the job spec to use your custom image (optional):**
To use your published image from the step above instead of the
`k8s.gcr.io/nfd/node-feature-discovery` image, edit `image`
attribute in the spec template(s) to the new location
(`<registry-name>/<image-name>[:<version>]`).
### Customizing the Build
There are several Makefile variables that control the build process and the
name of the resulting container image.
| Variable | Description | Default value
| -------------------------- | ----------------------------------------------------------------- | ----------- |
| HOSTMOUNT_PREFIX | Prefix of system directories for feature discovery (local builds) | /
| CONTAINER_HOSTMOUNT_PREFIX | Prefix of system directories for feature discovery (container builds) | &lt;HOSTMOUNT_PREFIX&gt; (*if specified*) /host- (*otherwise*)
| IMAGE_BUILD_CMD | Command to build the image | docker build
| IMAGE_BUILD_EXTRA_OPTS | Extra options to pass to build command | *empty*
| IMAGE_PUSH_CMD | Command to push the image to remote registry | docker push
| IMAGE_REGISTRY | Container image registry to use | k8s.gcr.io/nfd
| IMAGE_NAME | Container image name | node-feature-discovery
| IMAGE_TAG_NAME | Container image tag name | &lt;nfd version&gt;
| IMAGE_EXTRA_TAG_NAMES | Additional container image tag(s) to create when building image | *empty*
| IMAGE_REPO | Container image repository to use | &lt;IMAGE_REGISTRY&gt;/&lt;IMAGE_NAME&gt;
| IMAGE_TAG | Full image:tag to tag the image with | &lt;IMAGE_REPO&gt;/&lt;IMAGE_NAME&gt;
| K8S_NAMESPACE | nfd-master and nfd-worker namespace | kube-system
| KUBECONFIG | Kubeconfig for running e2e-tests | *empty*
| E2E_TEST_CONFIG | Parameterization file of e2e-tests (see [example](test/e2e/e2e-test-config.exapmle.yaml)) | *empty*
For example, to use a custom registry:
```
make IMAGE_REGISTRY=<my custom registry uri>
```
Or to specify a build tool different from Docker:
```
make IMAGE_BUILD_CMD="buildah bud"
```
### Testing
Unit tests are automatically run as part of the container image build. You can
also run them manually in the source code tree by simply running:
```
make test
```
End-to-end tests are built on top of the e2e test framework of Kubernetes, and,
they required a cluster to run them on. For running the tests on your test
cluster you need to specify the kubeconfig to be used:
```
make e2e-test KUBECONFIG=$HOME/.kube/config
```
## Targeting Nodes with Specific Features
Nodes with specific features can be targeted using the `nodeSelector` field. The
following example shows how to target nodes with Intel TurboBoost enabled.
```yaml
apiVersion: v1
kind: Pod
metadata:
labels:
env: test
name: golang-test
spec:
containers:
- image: golang
name: go1
nodeSelector:
feature.node.kubernetes.io/cpu-pstate.turbo: 'true'
```
For more details on targeting nodes, see [node selection][node-sel].
## Node Annotations
NFD annotates nodes it is running on:
| Annotation | Description
| ----------------------------------------- | -----------
| nfd.node.kubernetes.io/master.version | Version of the nfd-master instance running on the node. Informative use only.
| nfd.node.kubernetes.io/worker.version | Version of the nfd-worker instance running on the node. Informative use only.
| nfd.node.kubernetes.io/feature-labels | Comma-separated list of node labels managed by NFD. NFD uses this internally so must not be edited by users.
| nfd.node.kubernetes.io/extended-resources | Comma-separated list of node extended resources managed by NFD. NFD uses this internally so must not be edited by users.
Unapplicable annotations are not created, i.e. for example master.version is only created on nodes running nfd-master.
2016-08-31 00:04:43 +00:00
## References
2016-08-01 04:35:52 +00:00
Github issues
- [#28310](https://github.com/kubernetes/kubernetes/issues/28310)
- [#28311](https://github.com/kubernetes/kubernetes/issues/28311)
- [#28312](https://github.com/kubernetes/kubernetes/issues/28312)
[Design proposal](https://docs.google.com/document/d/1uulT2AjqXjc_pLtDu0Kw9WyvvXm-WAZZaSiUziKsr68/edit)
## Governance
This is a [SIG-node](https://github.com/kubernetes/community/blob/master/sig-node/README.md)
subproject, hosted under the
[Kubernetes SIGs](https://github.com/kubernetes-sigs) organization in
Github. The project was established in 2016 as a
[Kubernetes Incubator](https://github.com/kubernetes/community/blob/master/incubator.md)
project and migrated to Kubernetes SIGs in 2018.
2016-07-23 05:13:48 +00:00
## License
This is open source software released under the [Apache 2.0 License](LICENSE).
2016-07-26 05:44:45 +00:00
## Demo
A demo on the benefits of using node feature discovery can be found in [demo](demo/).
2016-07-26 05:44:45 +00:00
<!-- Links -->
[cpuid]: http://man7.org/linux/man-pages/man4/cpuid.4.html
[intel-rdt]: http://www.intel.com/content/www/us/en/architecture-and-technology/resource-director-technology.html
[intel-pstate]: https://www.kernel.org/doc/Documentation/cpu-freq/intel-pstate.txt
[intel-sst]: https://www.intel.com/content/www/us/en/architecture-and-technology/speed-select-technology-article.html
[sriov]: http://www.intel.com/content/www/us/en/pci-express/pci-sig-sr-iov-primer-sr-iov-technology-paper.html
2019-05-08 11:44:16 +00:00
[docker-down]: https://docs.docker.com/install
[golang-down]: https://golang.org/dl
[gcc-down]: https://gcc.gnu.org
2019-05-08 11:44:16 +00:00
[kubectl-setup]: https://kubernetes.io/docs/tasks/tools/install-kubectl
[node-sel]: http://kubernetes.io/docs/user-guide/node-selection