mirror of
https://github.com/intel/intel-device-plugins-for-kubernetes.git
synced 2025-06-03 03:59:37 +00:00
1b3accacc2
Currently, each individual plugin README documents roughly the same
daily development steps to git clone, build, and deploy. Re-purpose
the plugin READMEs more towards cluster admin type of documentation
and start moving all development related documentation to DEVEL.md.
The same is true for e2e testing documentation which is scattered
in places where they don't belong to. Having all day-to-day
development Howtos is good to have in a centralized place.
Finally, the cleanup includes some harmonization to plugins'
table of contents which now follows the pattern:
* [Introduction](#introduction)
(* [Modes and Configuration Options](#modes-and-configuration-options))
* [Installation](#installation)
(* [Prerequisites](#prerequisites))
* [Pre-built Images](#pre-built-images)
* [Verify Plugin Registration](#verify-plugin-registration)
* [Testing and Demos](#testing-and-demos)
* ...
Signed-off-by: Mikko Ylinen <mikko.ylinen@intel.com>
200 lines
8.0 KiB
Markdown
200 lines
8.0 KiB
Markdown
# Intel VPU device plugin for Kubernetes
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Table of Contents
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* [Introduction](#introduction)
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* [Installation](#installation)
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* [Pre-built Images](#pre-built-images)
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* [Verify Plugin Registration](#verify-plugin-registration)
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* [Testing and Demos](#testing-and-demos)
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## Introduction
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The VPU device plugin supports below cards:
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[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).
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This card has:
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- 1 Intel Core i3-7100U processor
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- 12 MyriadX VPUs
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- 8GB DDR4 memory
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- PCIe interface to Xeon E3/E5 server
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[Intel Mustang V100](https://software.intel.com/en-us/articles/introducing-the-iei-tank-aiot-developer-kit-and-mustang-v100-mx8-pcie-accelerator-card).
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This card has:
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- 8 MyriadX VPUs
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- PCIe interface to 6th+ Generation Core PC or Xeon E3/E5 server
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[Gen 3 Intel® Movidius™ VPU HDDL VE3](https://www.intel.com/content/www/us/en/products/details/processors/movidius-vpu.html)
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This card has:
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- 3 Intel® Movidius Gen 3 Intel® Movidius™ VPU SoCs
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[Intel® Movidius™ S VPU](https://www.intel.com/content/www/us/en/products/details/processors/movidius-vpu.html)
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This card has:
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- 6 Intel® Movidius Gen 3 Intel® Movidius™ VPU SoCs
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> **Note:** This device plugin need HDDL daemon service to be running either natively or from a container.
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> To get VCAC-A or Mustang card running hddl, please refer to:
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> https://github.com/OpenVisualCloud/Dockerfiles/blob/master/VCAC-A/script/setup_hddl.sh
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## Installation
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The following sections detail how to use the VPU device plugin.
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### Pre-built Images
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[Pre-built images](https://hub.docker.com/r/intel/intel-vpu-plugin)
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of this component are available on the Docker hub. These images are automatically built and uploaded
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to the hub from the latest main branch of this repository.
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Release tagged images of the components are also available on the Docker hub, tagged with their
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release version numbers in the format `x.y.z`, corresponding to the branches and releases in this
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repository. Thus the easiest way to deploy the plugin in your cluster is to run this command
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```bash
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$ kubectl apply -k https://github.com/intel/intel-device-plugins-for-kubernetes/deployments/vpu_plugin?ref=<RELEASE_VERSION>
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daemonset.apps/intel-vpu-plugin created
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```
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Where `<RELEASE_VERSION>` needs to be substituted with the desired [release tag](https://github.com/intel/intel-device-plugins-for-kubernetes/tags) or `main` to get `devel` images.
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For xlink device, deploy DaemonSet as
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```bash
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$ kubectl apply -k https://github.com/intel/intel-device-plugins-for-kubernetes/deployments/vpu_plugin/overlays/xlink
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daemonset.apps/intel-vpu-plugin created
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```
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Nothing else is needed. See [the development guide](../../DEVEL.md) for details if you want to deploy a customized version of the plugin.
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> **Note**: It is also possible to run the VPU device plugin using a non-root user. To do this,
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the nodes' DAC rules must be configured to device plugin socket creation and kubelet registration.
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Furthermore, the deployments `securityContext` must be configured with appropriate `runAsUser/runAsGroup`.
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### Verify Plugin Registration
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You can verify the plugin has been registered with the expected nodes by searching for the relevant
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resource allocation status on the nodes:
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```bash
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$ kubectl get nodes -o=jsonpath="{range .items[*]}{.metadata.name}{'\n'}{' hddl: '}{.status.allocatable.vpu\.intel\.com/hddl}{'\n'}"
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vcaanode00
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hddl: 12
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```
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## Testing and Demos
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We can test the plugin is working by deploying the provided example OpenVINO image with HDDL plugin enabled.
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### Build a Docker image with an classification example
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```bash
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$ cd $(go env GOPATH)/src/github.com/intel/intel-device-plugins-for-kubernetes
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$ make ubuntu-demo-openvino
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...
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Successfully tagged intel/ubuntu-demo-openvino:devel
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```
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### Create a job running unit tests off the local Docker image
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```bash
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$ cd $(go env GOPATH)/src/github.com/intel/intel-device-plugins-for-kubernetes
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$ kubectl apply -f demo/intelvpu-job.yaml
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job.batch/intelvpu-demo-job created
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```
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### Review the job logs
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```bash
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$ kubectl get pods | fgrep intelvpu
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# substitute the 'xxxxx' below for the pod name listed in the above
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$ kubectl logs intelvpu-demo-job-xxxxx
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+ export HDDL_INSTALL_DIR=/root/hddl
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+ HDDL_INSTALL_DIR=/root/hddl
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+ export LD_LIBRARY_PATH=/root/inference_engine_samples_build/intel64/Release/lib/
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+ LD_LIBRARY_PATH=/root/inference_engine_samples_build/intel64/Release/lib/
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+ /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
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[ INFO ] InferenceEngine:
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API version ............ 2.0
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Build .................. custom_releases/2019/R2_f5827d4773ebbe727c9acac5f007f7d94dd4be4e
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Description ....... API
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[ INFO ] Parsing input parameters
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[ INFO ] Parsing input parameters
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[ INFO ] Files were added: 1
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[ INFO ] /root/car.png
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[ INFO ] Creating Inference Engine
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HDDL
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HDDLPlugin version ......... 2.0
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Build ........... 27579
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[ INFO ] Loading network files
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[ INFO ] Preparing input blobs
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[ WARNING ] Image is resized from (787, 259) to (227, 227)
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[ INFO ] Batch size is 1
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[ INFO ] Loading model to the device
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[07:49:01.0427][6]I[ServiceStarter.cpp:40] Info: Waiting for HDDL Service getting ready ...
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[07:49:01.0428][6]I[ServiceStarter.cpp:45] Info: Found HDDL Service is running.
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[HDDLPlugin] [07:49:01.0429][6]I[HddlClient.cpp:256] Hddl api version: 2.2
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[HDDLPlugin] [07:49:01.0429][6]I[HddlClient.cpp:259] Info: Create Dispatcher2.
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[HDDLPlugin] [07:49:01.0432][10]I[Dispatcher2.cpp:148] Info: SenderRoutine starts.
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[HDDLPlugin] [07:49:01.0432][6]I[HddlClient.cpp:270] Info: RegisterClient HDDLPlugin.
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[HDDLPlugin] [07:49:01.0435][6]I[HddlClient.cpp:275] Client Id: 3
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[ INFO ] Create infer request
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[HDDLPlugin] [07:49:01.7235][6]I[HddlBlob.cpp:166] Info: HddlBlob initialize ion ...
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[HDDLPlugin] [07:49:01.7237][6]I[HddlBlob.cpp:176] Info: HddlBlob initialize ion successfully.
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[ INFO ] Start inference (10 asynchronous executions)
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[ INFO ] Completed 1 async request execution
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[ INFO ] Completed 2 async request execution
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[ INFO ] Completed 3 async request execution
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[ INFO ] Completed 4 async request execution
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[ INFO ] Completed 5 async request execution
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[ INFO ] Completed 6 async request execution
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[ INFO ] Completed 7 async request execution
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[ INFO ] Completed 8 async request execution
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[ INFO ] Completed 9 async request execution
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[ INFO ] Completed 10 async request execution
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[ INFO ] Processing output blobs
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Top 10 results:
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Image /root/car.png
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classid probability label
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------- ----------- -----
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817 0.8295898 sports car, sport car
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511 0.0961304 convertible
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479 0.0439453 car wheel
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751 0.0101318 racer, race car, racing car
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436 0.0074234 beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon
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656 0.0042267 minivan
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586 0.0029869 half track
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717 0.0018148 pickup, pickup truck
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864 0.0013924 tow truck, tow car, wrecker
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581 0.0006595 grille, radiator grille
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[HDDLPlugin] [07:49:01.9231][11]I[Dispatcher2.cpp:212] Info: Listen Thread wake up and to exit.
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[HDDLPlugin] [07:49:01.9232][6]I[Dispatcher2.cpp:81] Info: Client dispatcher exit.
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[HDDLPlugin] [07:49:01.9235][6]I[HddlClient.cpp:203] Info: Hddl client unregistered.
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[ INFO ] Execution successful
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[ INFO ] This sample is an API example, for any performance measurements please use the dedicated benchmark_app tool
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```
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If the pod did not successfully launch, possibly because it could not obtain the vpu HDDL
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resource, it will be stuck in the `Pending` status:
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```bash
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$ kubectl get pods
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NAME READY STATUS RESTARTS AGE
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intelvpu-demo-job-xxxxx 0/1 Pending 0 8s
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```
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This can be verified by checking the Events of the pod:
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```bash
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$ kubectl describe pod intelvpu-demo-job-xxxxx
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...
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Events:
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Type Reason Age From Message
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---- ------ ---- ---- -------
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Warning FailedScheduling <unknown> default-scheduler 0/1 nodes are available: 1 Insufficient vpu.intel.com/hddl.
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```
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