Hosting custom DSP blocks
Building custom processing blocks is available for everyone but has to be self-hosted. If you want to host it on Edge Impulse infrastructures, you can do that within your organization interface.
In this tutorial, you'll learn how to use Edge Impulse CLI to push your custom DSP block to your organisation and how to make this processing block available in the Studio for all users in the organization.
The Custom Processing block we are using for this tutorial can be found here: https://github.com/edgeimpulse/edge-detection-processing-block. It is written in Python. Please note that one of the beauties with custom blocks is that you can write them in any language as we will host a Docker container and we are not tied to a specific runtime.
Prerequisites
You'll need:
The Edge Impulse CLI. If you receive any warnings that's fine. Run
edge-impulse-blocks
afterwards to verify that the CLI was installed correctly.Docker desktop installed on your machine. Custom blocks use Docker containers, a virtualization technique which lets developers package up an application with all dependencies in a single package. If you want to test your blocks locally you'll also need (this is not a requirement):
A Custom Processing block running with Docker.
Init and upload your custom DSP block
Inside your Custom DSP block folder, run the following command:
edge-impulse-blocks init --clean
The output will look like this:
? What is your user name or e-mail address (edgeimpulse.com)?
? What is your password? [hidden]
Edge Impulse Blocks v1.14.3
Attaching block to organization 'Demo Team'
? Choose a type of block
Transformation block
Deployment block
❯ DSP block
Transfer learning block
? Enter the name of your block Edge: Detection
? Enter the description of your block: Edge Detection processing block using Canny filters in images
Creating block with config: {
version: 1,
config: {
'edgeimpulse.com': {
name: 'Edge Detection',
type: 'dsp',
description: 'Edge Detection processing block using Canny filters in images',
organizationId: XXX,
operatesOn: undefined,
tlObjectDetectionLastLayer: undefined,
tlOperatesOn: undefined
}
}
}
Your new block 'Edge Detection' has been created in '<PATH>'.
When you have finished building your dsp block, run 'edge-impulse-blocks push' to update the block in Edge Impulse.
Modify or update your custom code if needed and run the following command:
edge-impulse-blocks push
The output will look similar to this:
Edge Impulse Blocks v1.14.3
? What port is your block listening on? 4446
Archiving 'edge-detection-processing-block'...
Archiving 'edge-detection-processing-block' OK (476 KB) /var/folders/7f/pfcmh61s3hg9c59qd0dkkw5w0000gn/T/ei-dsp-block-c729b4a3ff761b64629617c869e9d934.tar.gz
Uploading block 'Edge Detection' to organization 'Demo Team'...
Uploading block 'Edge Detection' to organization 'Demo Team' OK
Building dsp block 'Edge Detection'...
Job started
...
Building dsp block 'Edge Detection' OK
That's it, now your custom DSP block is hosted on your organization. To make sure it is up and running, in your organisation, go to Custom blocks->DSP and you will see the following screen:

Use your custom hosted DSP block in your projects
To use your DSP block, simply add it as a processing block in the Create impulse view:

Other resources
Full instruction on how to build processing blocks: Building custom processing blocks
Troubleshooting
Deploy block types are hidden
When running edge-impulse-blocks init
for hosting a custom DSP block, ensure you log into an Edge Impulse account that is a member of an Organization. If you are logged into a personal account, you will be presented with the following CLI output:
$ edge-impulse-blocks init
Edge Impulse Blocks v1.16.0
? What is your user name or e-mail address (edgeimpulse.com)? [email protected]
? What is your password? [hidden]
[CFG] Creating developer profile...
[CFG] Creating developer profile OK
Attaching block to organization 'Jenny Plunkett'
? Choose a type of block (transform, DSP and deploy block types are hidden because you are pushing to a personal profile)
❯ Machine learning block
Last updated