Deploying to the Maivin

Now that you have validated your Vision Model, this guide will walk you through deploying Vision models in a Maivin Platform.

Maivin 1	Maivin 2

This guide will showcase two methods of deploying the model.

Live View (Segmentation App)
MCAP Recording

Download the Model

First download the model from EdgeFirst Studio into the Maivin Platform. There are two methods for downloading the model. The first method is to download the model from EdgeFirst Studio and then SCP the model file to the Maivin Platform. The second method is to use the EdgeFirst Client to download the model directly in the device.

Download and SCP

As mentioned under the Trained Models section, the trained models can be downloaded by clicking the "View Additional Details" button on the training session card in EdgeFirst Studio.

This will open the session details and the models are listed under the "Artifacts" tab as shown below. Click on the downward arrow indicated in red to download the models to your PC. In this example, you will be deploying the TFLite model in the Maivin.

Session Details	Artifacts

Once the model is downloaded in your PC, you can SCP the model to the Maivin by using this command template.

scp <path to the downloaded TFLite model> <destination path>

An example command is shown below.

scp modelpack.tflite torizon@verdin-imx8mp-15140753:~

For more information, please visit Secure Copy.

Download using the Client

This method expects you to have already connected to the Maivin via SSH. The EdgeFirst Client can be installed via pip3 install edgefirst-client. You can verify the installation with the client version command.

$ edgefirst-client version
EdgeFirst Studio Server: 3.7.5-def7735 Client: 1.3.4

Next login to the client with the command.

$ edgefirst-client login
Username: user
Password: ****

You can download the model on the device using the download-artifact command as shown below.

edgefirst-client download-artifact <session ID> <model name>

The download-artifact expects three arguments.

session ID: Pass the integer trainer or validation session ID associated with the models.
model name: Pass the specific model that will be downloaded to the device. Usually this is modelpack.tflite.
download path (optional): Specify the path to download the model. If not provided, it will download to the current working directory.

You can find more information on using the EdgeFirst Client in the command line.

Visit the Web UI Service

Visit the Web UI service by entering the URL https://<hostname>/ in your browser.

Note

Replace <hostname> with the hostname of your device.

You should be greeted with the following page.

For more information, please see the Web UI Walkthrough.

Update the Model Path

Next you will need to specify the path to the model in the device. You can either update the model path in the Web UI or via the command line.

Web UI

Once you are in the Web UI main page, you can specify the path to the model by following the steps below.

Click the settings icon on the top right corner of the page.

Select "Model Settings".

Configure the path to the model in your device as specified under "MODEL:". Once configured, click "Save Configuration" to save your changes.

Command Line

To update the model path using the command line in the device, edit the following file using sudo vi /etc/default/model.

Next, you will see the file with the following contents.

# This is the configuration file for the model systemd service file.  When
# running systemctl start detect the service will use these configurations.
# If running model directly, you must continue to use the command-line options.

# A model is required for the model application. This can be a segmentation model
# and/or a detection model.
MODEL = "path/to/mymodel.tflite"

Edit the following line MODEL = "path/to/mymodel.tflite" to point to the specific path to your model. To edit, press i to enter into "Insert Mode". You should now be able to edit the lines. To exit "Insert Mode", press the ESC key on your keyboard. Next save and exit the file by typing :wq on your keyboard. More examples for using vim can be found here.

Once the path to the model has been updated, restart the model service using sudo systemctl restart model.

Enable and Start the Services

Once the model path in the device is specifed, ensure that the Camera, Model, and Recorder services are enabled. To verify, go back to the settings and click on the "Service Status" button.

You will be greeted with the "Service Overview" page. Ensure that the "camera" and "model" services are enabled and running by toggling the "Enable" and "Start" buttons as shown. Only "Enable" the "recorder" service as shown. You will be using the recorder service in MCAP Recording.

Live View (Segmentation App)

Now you will see live inference of the model in the device. Once the model and camera services are enabled, go back to the main page and then select the "Segmentation" application as shown.

This will run inference on the model specified to generate segmentation masks on the detected objects. In this case, the model is identifying people in the video feed. Examples are shown below.

MCAP Recording

Now you will run a recording on the device, saving the model inferences, and then visualizing the recording using Foxglove Studio. Once the model, camera, and recorder services are enabled, go back to the main page and then select the "MCAP" application as shown.

You will be greeted with the MCAP recording page.

Toggle the "Recording" button as shown to start recording the video feed. To stop the recording, toggle the same button and then the recording will be stored as an MCAP file.

For more information on MCAP recordings, please see the MCAP Recording Service.

Inference Visualization in Foxglove

The MCAP recordings are listed under the list of "MCAP Files" which can then be downloaded to your PC.

Once the MCAP recording has been downloaded, you can use Foxglove Studio to see the playback of MCAP recordings and the model inference. The following preview is a frame from the MCAP with the model inference masks overlaid on top of the video.

More information on the MCAP playback is provided in Foxglove Studio.

In this tutorial, you have fetched the trained and validated model from EdgeFirst Studio, copied the model in the Maivin, configured the Maivin model services, and ran inference on the model in the device. You have seen the model running live using the Maivin's camera and ran a Maivin MCAP recording to capture the model inference in the frames that can be visualized using Foxglove Studio.

For examples on deploying ModelPack in other platforms, see Model Deployment.