Deploying to the Maivin

Now that you have validated your Vision Model from either a managed or user-managed session, 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): Displays the live camera feed using the default model provided.
MCAP Recording: Allows control of the recording options and provides download file options to replay the 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 Training Outcomes 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:~

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: 3.7.8-a50429e Client: 1.3.3

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 mymodel.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

Use your browser to connect to the Web UI of the remote device, enter the following URL https://<hostname>/.

Note

Replace <hostname> with the hostname of your device.

You will be greeted with the Maivin Web UI Main Page 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.

Configure Model Settings

Whenever a new model has been updated, ensure that the model settings such as the score and the IoU thresholds are ideally set for this model.

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 vi 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 specified, 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 coffee cups in the video feed. An example is shown below.

Now that the model has been updated, you can make new recordings using the model's inference and then visualizing the recording using Foxglove Studio.

Record MCAP

MCAP recordings can be started and stopped using the Recording Button at the device's top navbar. It is to the left of the MCAP Details hamburger button, which is used to open the MCAP Details Modal.

Both of these buttons are available on every page of the Maivin, Raivin, and other edge devices running the EdgeFirst middleware.

Note

You must close all modals to be able to click the "Recording" and "Details" Buttons.

For more information about recording MCAPs, please read the MCAP Recording section.

Start Recording

To start a recording, simply click the "Recording" button to begin capturing data.

Note

It may take up to 30 seconds for a recording to start, depending on topic tracked.

Low Disk Space

If there is not enough room on the drive to record an MCAP, recording will automatically stop and you will get a "Low Disk Space" error.

If you were to open the MCAP Details Modal while recording, you would see a new MCAP file in the MCAP list.

Stop Recording

To stop recording, click the "Recording" button a second time.

Download MCAP

To download an MCAP recording from the device, click the MCAP Details hamburger button "MCAP Details Button" to open the MCAP Details Modal.

Use the "Download" button "Download Button" in the row containing the name of the MCAP file you want to download, which will download the MCAP file to your local machine.

You can also use an SSH client to copy files off the Raivin.

Inference Visualization in Foxglove

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.

Next Steps

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