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We may be able to include pravega for our on-device training projects as well: https://github.com/nnstreamer/nntrainer . We are working on on-device training (nntrainer) to enable neural network personalization on devices without exporting personal data to clouds. Both nnstreamer and nntrainer are standard machine learning frameworks of Tizen for TV, CE, and IoT devices.
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Thank you for your offer. I remember I had trouble getting nnstreamer to work with Tensorflow on a CPU and this was before I attempted to integrate it with Pravega. I will need to retest with your latest release but my time is quite limited right now. It would be great if someone in the community can experiment with this. If changes need to be made to pravegasrc or pravegasink to accommodate this, I can help.
If you share your implementation of nnstreamer support (as discussed in https://lists.lfaidata.foundation/g/nnstreamer-technical-discuss/topic/pravega_io_nnstreamer/82135550?p=,,,20,0,0,0::recentpostdate%2Fsticky,,,20,2,0,82135550 ) along with some error-logs, reproducible test cases, and such, we (nnstreamer contributors at https://github.com/nnstreamer ) might be able to help.
We may be able to include pravega for our on-device training projects as well: https://github.com/nnstreamer/nntrainer . We are working on on-device training (nntrainer) to enable neural network personalization on devices without exporting personal data to clouds. Both nnstreamer and nntrainer are standard machine learning frameworks of Tizen for TV, CE, and IoT devices.
The text was updated successfully, but these errors were encountered: