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: 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.
Only available for enterprise customers
Organizational features are only available for enterprise customers. View our pricing for more information.


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 (
? 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: {
'': {
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:
DSP Blocks in organization

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:
Custom processing block available in your organization's projects

Other resources


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 ( [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