mirror of
https://github.com/intel/intel-device-plugins-for-kubernetes.git
synced 2025-06-03 03:59:37 +00:00
vpu: refactor the vpu plugin readme
Just follow the standard format to fix the vpu plugin readme. Also added the ubuntu OpenVINO demo job long logs. Signed-off-by: Alek Du <alek.du@intel.com>
This commit is contained in:
Alek Du
parent
7e6e053349
commit
6321c424ca
@ -1,75 +1,249 @@
|
||||
# Build and test Intel VPU device plugin for Kubernetes
|
||||
# Intel VPU device plugin for Kubernetes
|
||||
|
||||
### Get source code:
|
||||
```
|
||||
$ mkdir -p $GOPATH/src/github.com/intel/
|
||||
$ cd $GOPATH/src/github.com/intel/
|
||||
$ git clone https://github.com/intel/intel-device-plugins-for-kubernetes.git
|
||||
```
|
||||
Note: to get VCAC-A card running hddl, please refer to:
|
||||
https://github.com/OpenVisualCloud/Dockerfiles/blob/master/VCAC-A/script/setup_hddl.sh
|
||||
# Table of Contents
|
||||
|
||||
### Verify kubelet socket exists in /var/lib/kubelet/device-plugins/ directory:
|
||||
* [Introduction](#introduction)
|
||||
* [Installation](#installation)
|
||||
* [Getting the source code](#getting-the-source-code)
|
||||
* [Verify node kubelet config](#verify-node-kubelet-config)
|
||||
* [Deploying as a DaemonSet](#deploying-as-a-daemonset)
|
||||
* [Build the plugin image](#build-the-plugin-image)
|
||||
* [Deploy plugin DaemonSet](#deploy-plugin-daemonset)
|
||||
* [Deploy by hand](#deploy-by-hand)
|
||||
* [Build the plugin](#build-the-plugin)
|
||||
* [Run the plugin as administrator](#run-the-plugin-as-administrator)
|
||||
* [Verify plugin registration](#verify-plugin-registration)
|
||||
* [Testing the plugin](#testing-the-plugin)
|
||||
|
||||
# Introduction
|
||||
|
||||
The VPU device plugin supports below cards:
|
||||
|
||||
[Intel VCAC-A](https://www.intel.com/content/dam/www/public/us/en/documents/datasheets/media-analytics-vcac-a-accelerator-card-by-celestica-datasheet.pdf).
|
||||
This card has:
|
||||
- 1 Intel Core i3-7100U processor
|
||||
- 12 MyriadX VPUs
|
||||
- 8GB DDR4 memory
|
||||
- PCIe interface to Xeon E3/E5 server
|
||||
|
||||
[Intel Mustang V100](https://software.intel.com/en-us/articles/introducing-the-iei-tank-aiot-developer-kit-and-mustang-v100-mx8-pcie-accelerator-card).
|
||||
This card has:
|
||||
- 8 MyriadX VPUs
|
||||
- PCIe interface to 6th+ Generation Core PC or Xeon E3/E5 server
|
||||
|
||||
> **Note:** This device plugin need HDDL daemon service to be running either natively or from a container.
|
||||
> To get VCAC-A or Mustang card running hddl, please refer to:
|
||||
> https://github.com/OpenVisualCloud/Dockerfiles/blob/master/VCAC-A/script/setup_hddl.sh
|
||||
|
||||
# Installation
|
||||
|
||||
The following sections detail how to obtain, build, deploy and test the VPU device plugin.
|
||||
|
||||
Examples are provided showing how to deploy the plugin either using a DaemonSet or by hand on a per-node basis.
|
||||
|
||||
## Getting the source code
|
||||
|
||||
> **Note:** It is presumed you have a valid and configured [golang](https://golang.org/) environment
|
||||
> that meets the minimum required version.
|
||||
|
||||
```bash
|
||||
$ go get -d -u github.com/intel/intel-device-plugins-for-kubernetes
|
||||
```
|
||||
|
||||
## Verify node kubelet config
|
||||
|
||||
Every node that will be running the vpu plugin must have the
|
||||
[kubelet device-plugins](https://kubernetes.io/docs/concepts/extend-kubernetes/compute-storage-net/device-plugins/)
|
||||
configured. For each node, check that the kubelet device plugin socket exists:
|
||||
|
||||
```bash
|
||||
$ ls /var/lib/kubelet/device-plugins/kubelet.sock
|
||||
/var/lib/kubelet/device-plugins/kubelet.sock
|
||||
```
|
||||
|
||||
### Deploy VPU device plugin as host process for development purposes
|
||||
## Deploying as a DaemonSet
|
||||
|
||||
#### Build VPU device plugin:
|
||||
To deploy the vpu plugin as a daemonset, you first need to build a container image for the
|
||||
plugin and ensure that is visible to your nodes.
|
||||
|
||||
### Build the plugin image
|
||||
|
||||
The following will use `docker` to build a local container image called
|
||||
`intel/intel-vpu-plugin` with the tag `devel`.
|
||||
|
||||
The image build tool can be changed from the default `docker` by setting the `BUILDER` argument
|
||||
to the [`Makefile`](Makefile).
|
||||
|
||||
```bash
|
||||
$ cd $GOPATH/src/github.com/intel/intel-device-plugins-for-kubernetes
|
||||
$ make intel-vpu-plugin
|
||||
...
|
||||
Successfully tagged intel/intel-vpu-plugin:devel
|
||||
```
|
||||
|
||||
### Deploy plugin DaemonSet
|
||||
|
||||
You can then use the example DaemonSet YAML file provided to deploy the plugin.
|
||||
|
||||
```bash
|
||||
$ kubectl create -f ./deployments/vpu_plugin/vpu_plugin.yaml
|
||||
daemonset.apps/intel-vpu-plugin created
|
||||
```
|
||||
|
||||
> **Note**: It is also possible to run the VPU device plugin using a non-root user. To do this,
|
||||
the nodes' DAC rules must be configured to device plugin socket creation and kubelet registration.
|
||||
Furthermore, the deployments `securityContext` must be configured with appropriate `runAsUser/runAsGroup`.
|
||||
|
||||
## Deploy by hand
|
||||
|
||||
For development purposes, it is sometimes convenient to deploy the plugin 'by hand' on a node.
|
||||
In this case, you do not need to build the complete container image, and can build just the plugin.
|
||||
|
||||
### Build the plugin
|
||||
|
||||
First we build the plugin:
|
||||
|
||||
> **Note:** this vpu plugin has dependency of libusb-1.0-0-dev, you need install it before building vpu plugin
|
||||
|
||||
```bash
|
||||
$ cd $GOPATH/src/github.com/intel/intel-device-plugins-for-kubernetes
|
||||
$ make vpu_plugin
|
||||
```
|
||||
|
||||
#### Run VPU device plugin as administrator:
|
||||
```
|
||||
### Run the plugin as administrator
|
||||
|
||||
Now we can run the plugin directly on the node:
|
||||
|
||||
```bash
|
||||
$ sudo $GOPATH/src/github.com/intel/intel-device-plugins-for-kubernetes/cmd/vpu_plugin/vpu_plugin
|
||||
device-plugin start server at: /var/lib/kubelet/device-plugins/vpu.intel.com-hddl.sock
|
||||
device-plugin registered
|
||||
VPU device plugin started
|
||||
```
|
||||
|
||||
### Deploy VPU device plugin as a DaemonSet:
|
||||
## Verify plugin registration
|
||||
|
||||
#### Build plugin image
|
||||
```
|
||||
$ make intel-vpu-plugin
|
||||
You can verify the plugin has been registered with the expected nodes by searching for the relevant
|
||||
resource allocation status on the nodes:
|
||||
|
||||
```bash
|
||||
$ kubectl get nodes -o=jsonpath="{range .items[*]}{.metadata.name}{'\n'}{' hddl: '}{.status.allocatable.vpu\.intel\.com/hddl}{'\n'}"
|
||||
vcaanode00
|
||||
hddl: 12
|
||||
```
|
||||
|
||||
#### Create plugin DaemonSet
|
||||
```
|
||||
$ kubectl create -f ./deployments/vpu_plugin/vpu_plugin.yaml
|
||||
daemonset.apps/intel-vpu-plugin created
|
||||
```
|
||||
**Note**: It is also possible to run the VPU device plugin using a non-root user. To do this,
|
||||
the nodes' DAC rules must be configured to allow USB device descriptor detection, device plugin
|
||||
socket creation and kubelet registration. Furthermore, the deployments `securityContext` must
|
||||
be configured with appropriate `runAsUser/runAsGroup`.
|
||||
## Testing the plugin
|
||||
|
||||
### Verify VPU device plugin is registered on master:
|
||||
```
|
||||
$ kubectl describe node <node name> | grep vpu.intel.com
|
||||
vpu.intel.com/hddl: 1
|
||||
vpu.intel.com/hddl: 1
|
||||
We can test the plugin is working by deploying the provided example OpenVINO image with HDDL plugin enabled.
|
||||
|
||||
### Build a Docker image with an classification example
|
||||
|
||||
```bash
|
||||
$ cd demo
|
||||
$ ./build-image.sh ubuntu-demo-openvino
|
||||
...
|
||||
Successfully tagged ubuntu-demo-openvino:devel
|
||||
```
|
||||
|
||||
### Test VPU device plugin:
|
||||
### Create a job running unit tests off the local Docker image
|
||||
|
||||
1. Build a Docker image with an example openvino to VPU:
|
||||
```
|
||||
$ cd demo
|
||||
$ ./build-image.sh ubuntu-demo-openvino
|
||||
```
|
||||
```bash
|
||||
$ cd $GOPATH/src/github.com/intel/intel-device-plugins-for-kubernetes
|
||||
$ kubectl apply -f demo/intelvpu-job.yaml
|
||||
job.batch/intelvpu-demo-job created
|
||||
```
|
||||
|
||||
This command produces a Docker image named `ubuntu-demo-openvino`.
|
||||
### Review the job's logs
|
||||
|
||||
2. Create a pod running unit tests off the local Docker image:
|
||||
```
|
||||
$ kubectl apply -f demo/intelvpu-job.yaml
|
||||
```
|
||||
```bash
|
||||
$ kubectl get pods | fgrep intelvpu
|
||||
# substitute the 'xxxxx' below for the pod name listed in the above
|
||||
$ kubectl logs intelvpu-demo-job-xxxxx
|
||||
+ export HDDL_INSTALL_DIR=/root/hddl
|
||||
+ HDDL_INSTALL_DIR=/root/hddl
|
||||
+ export LD_LIBRARY_PATH=/root/inference_engine_samples_build/intel64/Release/lib/
|
||||
+ LD_LIBRARY_PATH=/root/inference_engine_samples_build/intel64/Release/lib/
|
||||
+ /root/inference_engine_samples_build/intel64/Release/classification_sample_async -m /root/openvino_models/ir/FP16/classification/squeezenet/1.1/caffe/squeezenet1.1.xml -i /root/car.png -d HDDL
|
||||
[ INFO ] InferenceEngine:
|
||||
API version ............ 2.0
|
||||
Build .................. custom_releases/2019/R2_f5827d4773ebbe727c9acac5f007f7d94dd4be4e
|
||||
Description ....... API
|
||||
[ INFO ] Parsing input parameters
|
||||
[ INFO ] Parsing input parameters
|
||||
[ INFO ] Files were added: 1
|
||||
[ INFO ] /root/car.png
|
||||
[ INFO ] Creating Inference Engine
|
||||
HDDL
|
||||
HDDLPlugin version ......... 2.0
|
||||
Build ........... 27579
|
||||
|
||||
3. Review the pod's logs:
|
||||
```
|
||||
$ kubectl logs intelvpu-demo-job-xxxx
|
||||
```
|
||||
[ INFO ] Loading network files
|
||||
[ INFO ] Preparing input blobs
|
||||
[ WARNING ] Image is resized from (787, 259) to (227, 227)
|
||||
[ INFO ] Batch size is 1
|
||||
[ INFO ] Loading model to the device
|
||||
[07:49:01.0427][6]I[ServiceStarter.cpp:40] Info: Waiting for HDDL Service getting ready ...
|
||||
[07:49:01.0428][6]I[ServiceStarter.cpp:45] Info: Found HDDL Service is running.
|
||||
[HDDLPlugin] [07:49:01.0429][6]I[HddlClient.cpp:256] Hddl api version: 2.2
|
||||
[HDDLPlugin] [07:49:01.0429][6]I[HddlClient.cpp:259] Info: Create Dispatcher2.
|
||||
[HDDLPlugin] [07:49:01.0432][10]I[Dispatcher2.cpp:148] Info: SenderRoutine starts.
|
||||
[HDDLPlugin] [07:49:01.0432][6]I[HddlClient.cpp:270] Info: RegisterClient HDDLPlugin.
|
||||
[HDDLPlugin] [07:49:01.0435][6]I[HddlClient.cpp:275] Client Id: 3
|
||||
[ INFO ] Create infer request
|
||||
[HDDLPlugin] [07:49:01.7235][6]I[HddlBlob.cpp:166] Info: HddlBlob initialize ion ...
|
||||
[HDDLPlugin] [07:49:01.7237][6]I[HddlBlob.cpp:176] Info: HddlBlob initialize ion successfully.
|
||||
[ INFO ] Start inference (10 asynchronous executions)
|
||||
[ INFO ] Completed 1 async request execution
|
||||
[ INFO ] Completed 2 async request execution
|
||||
[ INFO ] Completed 3 async request execution
|
||||
[ INFO ] Completed 4 async request execution
|
||||
[ INFO ] Completed 5 async request execution
|
||||
[ INFO ] Completed 6 async request execution
|
||||
[ INFO ] Completed 7 async request execution
|
||||
[ INFO ] Completed 8 async request execution
|
||||
[ INFO ] Completed 9 async request execution
|
||||
[ INFO ] Completed 10 async request execution
|
||||
[ INFO ] Processing output blobs
|
||||
|
||||
Top 10 results:
|
||||
|
||||
Image /root/car.png
|
||||
|
||||
classid probability label
|
||||
------- ----------- -----
|
||||
817 0.8295898 sports car, sport car
|
||||
511 0.0961304 convertible
|
||||
479 0.0439453 car wheel
|
||||
751 0.0101318 racer, race car, racing car
|
||||
436 0.0074234 beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon
|
||||
656 0.0042267 minivan
|
||||
586 0.0029869 half track
|
||||
717 0.0018148 pickup, pickup truck
|
||||
864 0.0013924 tow truck, tow car, wrecker
|
||||
581 0.0006595 grille, radiator grille
|
||||
|
||||
[HDDLPlugin] [07:49:01.9231][11]I[Dispatcher2.cpp:212] Info: Listen Thread wake up and to exit.
|
||||
[HDDLPlugin] [07:49:01.9232][6]I[Dispatcher2.cpp:81] Info: Client dispatcher exit.
|
||||
[HDDLPlugin] [07:49:01.9235][6]I[HddlClient.cpp:203] Info: Hddl client unregistered.
|
||||
[ INFO ] Execution successful
|
||||
|
||||
[ INFO ] This sample is an API example, for any performance measurements please use the dedicated benchmark_app tool
|
||||
```
|
||||
|
||||
If the pod did not successfully launch, possibly because it could not obtain the vpu HDDL
|
||||
resource, it will be stuck in the `Pending` status:
|
||||
|
||||
```bash
|
||||
$ kubectl get pods
|
||||
NAME READY STATUS RESTARTS AGE
|
||||
intelvpu-demo-job-xxxxx 0/1 Pending 0 8s
|
||||
```
|
||||
|
||||
This can be verified by checking the Events of the pod:
|
||||
|
||||
```bash
|
||||
$ kubectl describe pod intelvpu-demo-job-xxxxx
|
||||
...
|
||||
Events:
|
||||
Type Reason Age From Message
|
||||
---- ------ ---- ---- -------
|
||||
Warning FailedScheduling <unknown> default-scheduler 0/1 nodes are available: 1 Insufficient vpu.intel.com/hddl.
|
||||
```
|
||||
|
||||
Loading…
Reference in New Issue
Block a user