Custom deployment blocks
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Custom deployment blocks are a way to extend the capabilities of Edge Impulse beyond the deployment options built into the platform. If none of the existing blocks created by Edge Impulse fit your needs, you can create custom deployment blocks to build and export your own libraries or firmware binaries for unique project requirements.
Ready to dive in and start building? Jump to the examples!
The deployment block structure is shown below. Please see the custom blocks overview page for more details.
The sections below define the required and optional inputs and the expected outputs for custom deployment blocks.
Deployment blocks have access to command line arguments and input files.
The following arguments will be automatically passed to your custom deployment block.
--metadata <file>
Always
Provides the file path for deployment-metadata.json as a string. The deployment-metadata.json file contains details about the impulse being deployed. See deployment-metadata.json.
CLI arguments can also be specified using the cliArguments property in the parameters.json file. Alternatively, these arguments can be added (or changed) by editing the block in Studio.
Your deployment block will be passed an input directory that contains all the information required for a deployment, including: deployment metadata, the Edge Impulse SDK, the trained model (in multiple formats), and all supporting source code to run the impulse.
The input directory path is stored in the input property under the folders property in the deployment-metadata.json file, which can be loaded using the --metadata <file> argument that is passed to the deployment block.
The input directory structure is shown below.
The expected output from your custom deployment block is a ZIP file named deploy.zip located in the output directory. This archive is what will be downloaded for the user after your block has finished building.
The output directory path is stored in the output property under the folders property in the deployment-metadata.json file, which can be loaded using the --metadata <file> argument that is passed to the deployment block.
The input and output directories listed in the deployment-metadata.json file are located on network storage. Therefore to improve the speed of your deployment block, it is best practice to create a build directory, copy in the required items for your build, then write the output archive to the output directory.
In the example below, the app_dir contained the build instructions and files required to compile a Linux application.
If your custom deployment block requires access to the data used to train your model, you can mount the learning block by setting the mountLearnBlock property to true. This will mount all files for the learning block, including the training data, under a /data directory within your block.
The training data is already split into train and test (validation) sets. Please refer to the Data section under Inputs in the custom learning block documentation for additional details.
Deployment blocks do not have access to the internet by default. If you need to access information outside of your block, such as project items through the Edge Impulse API, you will need to set the privileged property to true.
This will enable internet access and pass in the project API key in the deployment-metadata.json file (if a project development API key is set) that can be used to authenticate with the Edge Impulse API. Note that if you also require the project ID, this can be retrieved using the list active projects API endpoint.
Setting the showOptimizations property to true will present the user with additional optimization options on the Deployment page in Studio.
Firstly, if the supportsEonCompiler property is set to true (see below), the user will be presented with a dropdown to select between building the deployment using the EON Compiler or standard TFLite file inputs.
Secondly, the user will be presented with quantization options, if applicable. If the user selects the quantized model option, the trained.tflite file will be the int8 version of the model; otherwise it will be the float32 version.
If the supportsEonCompiler property is set to true, the inputs for the deployment block will be the EON Compiler version of the files; otherwise the inputs will be the TFLite version of the files.
However, if the showOptimizations property is set to true (see above), the user will have the option on the Deployment page in Studio to select between the EON Compiler or standard TFLite file inputs.
The default image that will appear for your block in the dropdown in Studio on the Deployment page is the Edge Impulse logo. If you would like to change this, you can do so by editing the block after it has been pushed to Studio.
Testing locally does not mount the learning block
If your custom deployment block requires access to the learning block files after it has been mounted, testing locally will not work as the methods to download data described below do not include the learning block data.
To speed up your development process, you can test your custom deployment block locally. There are two ways to achieve this. You will need to have Docker installed on your machine for either approach.
For the first method, you can use the CLI edge-impulse-blocks runner tool. See Block runner for additional details. The runner does not expect any command line arguments for deployment blocks. However, if your deployment block requires arguments, you can pass them as a single string using the --extra-args <args> argument.
The first time you enter the above command, you will be asked some questions to configure the runner. Follow the prompts to complete this. If you would like to change the configuration in future, you can execute the runner command with the --clean flag.
Using the above approach will create an ei-block-data directory within your custom block directory. It will contain several subdirectories.
download/
Download directory for the archive of required input files for the deployment block.
<project-id>/input/
The input files archive will be automatically extracted to this location.
<project-id>/output/
Where the output from your build script is expected to be written.
For the second method, you can use the CLI block runner or Studio to download the required data from your project, then build the Docker image and run the container directly.
You can download the data by calling the block runner with the --download-data <dir> argument. The directory specifies the location where the downloaded data should be extracted. To make this work properly the directory needs to be named input/. Before extraction, the data archive will first be downloaded to ei-block-data/download/.
Alternatively, you can go to the Deployment page for your project in Studio and select Custom block as your deployment option. This will allow you to download a ZIP file of the required input files for you deployment block. Extract this archive to a directory called input/ within your custom deployment block directory.
After downloading the required input files for your block, you can then build the Docker image and run the container.
After you have pushed your block to Edge Impluse, it can be used in the same way as any other built-in block.
Edge Impulse has developed several examples of custom deployment blocks. The code for these blocks can be found in public repositories under the Edge Impulse GitHub account. Unfortunately, the repository names don't follow a naming convention. However, they can be found by going to the Edge Impulse account and searching the repositories for deploy.
Below are direct links to a some examples:
When you are finished developing your block locally, you will want to initialize it. The procedure to initialize your block is described in the overview page. Please refer to that documentation for details.
When you have initalized and finished testing your block locally, you will want to push it to Edge Impulse. The procedure to push your block to Edge Impulse is described in the overview page. Please refer to that documentation for details.
