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committedFeb 24, 2025·
Application note content update
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‎content/hardware/04.pro/boards/portenta-x8/tutorials/16.edge-ai-docker-container/content.md

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@@ -247,21 +247,21 @@ edge-impulse-data-forwarder --frequency 1
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When prompted, select the Edge Impulse project to which you want to send data.
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![Machine Learning Model blocks](assets/edge-impulse-data-forward-setup.png)
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![Data forwarder initialization](assets/edge-impulse-data-forward-setup.png)
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The data forwarder automatically detects the flow sensor and prompts for naming the data channels.
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![Machine Learning Model blocks](assets/edge-impulse-device-connected.png)
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![Data forwarder connected with Edge Impulse](assets/edge-impulse-device-connected.png)
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![Machine Learning Model blocks](assets/edge-impulse-data-forward-set.png)
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![Data acquisition with Edge Impulse](assets/edge-impulse-data-forward-set.png)
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Once configured, Edge Impulse begins receiving real time flow rate data.
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![Machine Learning Model blocks](assets/edge-impulse-data-forward-running.png)
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![Continuous data acquisition with Edge Impulse](assets/edge-impulse-data-forward-running.png)
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The collected flow rate samples are labeled and stored in the Edge Impulse dataset.
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![Machine Learning Model blocks](assets/edge-impulse-data-acquisction.png)
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![Initial Edge Impulse dataset](assets/edge-impulse-data-acquisction.png)
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***For more detailed information on data forwarding, please refer to [Edge Impulse’s official guide](https://docs.edgeimpulse.com/docs/tools/edge-impulse-cli/cli-data-forwarder).***
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For deployment on the Portenta X8, we use the provided container image, arguments and ports to build a custom container that integrates with the system’s existing services.
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![Docker container deployemnt parameters](assets/edge-impulse-deployment-docker2.png)
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![Docker container deployment parameters](assets/edge-impulse-deployment-docker2.png)
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The inference workflow consists of the following steps:
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