User Managed Validation

This tutorial will describe the steps to validate the performance of ModelPack Vision models as user-managed sessions in EdgeFirst Studio that have been trained through the end-to-end workflows or Training ModelPack. A user-managed validation session is hosted in an embedded platform for a proper measurement of the model inference times when deployed on target. A managed validation session creates an EC2 server to deploy the model for validation.

Specify Project Experiments

From the projects page, choose the project that contains the training session with the models you want to validate. In this example, the project chosen is the "Object Detection" project that was created in the Getting Started. Next click the "Model Experiments" button as indicated in red.

Model Experiments

Create Validation Session

In the experiment card, click the "Validate Sessions" button as indicated in red below.

Validate Sessions

You will be greeted to the "Validate Sessions" page as shown below.

Validate Sessions Page

Start a validation session by clicking on the "New Session" button on the top right corner of the page.

New Session Button

You will be greeted with a validation session dialog. In this dialog, check the "User Managed Validator" checkbox. Next specify the name of the validation session, the model to validate, and the dataset to deploy. In this example, the TFLite model will be validated and the "Coffee Cup" dataset with the validation partition will be used. Next specify, the validation parameters on the right. Additional information on these parameters are provided by hovering over the info button .

Validation Session Fields

Once the configurations have been made, go ahead and click on the "Start Session" button on the bottom right of the window. This will create the validation session to track validation progress that will run in the embedded platform.

Session Progress

Once the validation session has been created, SSH into the platform and install the following dependencies.

Virtual Environment

To avoid re-installation of existing system packages, we recommend setting up a python virtual environment prior to running the pip installations below.

pip install edgefirst-validator

Next login to your account in EdgeFirst Studio by using the EdgeFirst Client which comes installed with the validator package. The command below will prompt you to enter your EdgeFirst Studio credentials.

edgefirst-client --server <> login

Server Specification

Specify the EdgeFirst Studio server among these variations: "test", "stage", "saas". This is an optional parameter as the default is set to "saas".

Once the validator is installed and authenticated, run validation using the following command.

edgefirst-validator --session-id v-c1f

Note

Replace the session ID parameter specific to the validation session ID in your project.

Once entered, the following validation progress should now be indicated in EdgeFirst Studio as shown below.

Validation Session

Completed Session

The completed session will look as follows with the status set to "Complete".

Completed Session

The attributes of the validation sessions in EdgeFirst Studio are labeled below.

Validation Session Attributes

Validation Metrics

Once the validation session completes, you can view the validation metrics by clicking the "View Validation Charts" button on the top of the session card.

Validation Charts

Info

See detection and segmentation metrics for further details.

You can go back to the validation session card by pressing the "Back" button as indicated in red below on the top left corner of the page.

Back to the Session Card

Comparing Metrics

It is also possible to compare validation metrics for multiple sessions. See Validation Sessions in the Model Experiments Dashboard.

Next Steps

Now that you have validated your Vision model, you can find examples for deploying your model in the PC, EVK, and Maivin Platform.