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Openvino 2022 - using multiple NCS2 asynchrounously

gaudibert
Novice
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Hello,

 

I'm trying to update my stack to Openvino 2022 to benefit from the latest news. And I'm using this update to gain a little performance by trying to use multiple NCS2 asynchrounously.

 

However I'm a bit stuck despite reading quite some documentation on your website and the community. I'm finding a lot of examples of this on 2021 version but I can't manage to pass it to 2022 2.0 API.

 

The closest example I found is this : https://github.com/openvinotoolkit/openvino/tree/master/samples/cpp/classification_sample_async but it doesn't manage multiple devices.

Would you have a very basic example of a model loaded and called by batch on more than 1 NCS2 ? That would be immensely helpful for me.

 

Best regards,

Greg

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Wan_Intel
Moderator
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Hi Gaudibert,

Thanks for reaching out to us.

 

For your information, you can use multiple Intel® Neural Compute Stick 2 asynchronously as shown as follows:

 

1.   Run Hello Query Device Python Sample script which is located in the following directory:

 

cd <INSTALL_DIR>\samples\python\hello_query_device

python hello_query_device.py

 

2 device.png 

 

2.   Use Multi-device plugin with the following command:

 

python classification_sample_async.py -m "<path_to_model>\alexnet.xml" -i "path_to_input_1" "path_to_input_2" -d MULTI:MYRIAD.5.2-ma2480,MYRIAD.5.3-ma2480

 

multi.png

 

For more information, please refer to Running on multiple devices simultaneously and Multi-Device Plugin with the Intel® Neural Compute Stick 2.

 

 

Regards,

Wan

gaudibert
Novice
342 Views

Hello Wan,

 

Thank you for your quick reply. That is exactly what I needed.

 

I should have taken a look inside the openvino repository and not only online, sorry about that !

 

Cheers,
Greg

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Wan_Intel
Moderator
310 Views

Hi Gaudibert,

Thanks for your question.

This thread will no longer be monitored since we have provided information. 

If you need any additional information from Intel, please submit a new question.

 

 

Best regards,

Wan


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