LogoLogo
HomeAPI & SDKsProjectsForumStudio
  • Getting started
    • For beginners
    • For ML practitioners
    • For embedded engineers
  • Frequently asked questions (FAQ)
  • Tutorials
    • End-to-end tutorials
      • Computer vision
        • Image classification
        • Object detection
          • Object detection with bounding boxes
          • Detect objects with centroid (FOMO)
        • Visual anomaly detection
        • Visual regression
      • Audio
        • Sound recognition
        • Keyword spotting
      • Time-series
        • Motion recognition + anomaly detection
        • Regression + anomaly detection
        • HR/HRV
        • Environmental (Sensor fusion)
    • Data
      • Data ingestion
        • Collecting image data from the Studio
        • Collecting image data with your mobile phone
        • Collecting image data with the OpenMV Cam H7 Plus
        • Using the Edge Impulse Python SDK to upload and download data
        • Trigger connected board data sampling
        • Ingest multi-labeled data using the API
      • Synthetic data
        • Generate audio datasets using Eleven Labs
        • Generate image datasets using Dall-E
        • Generate keyword spotting datasets using Google TTS
        • Generate physics simulation datasets using PyBullet
        • Generate timeseries data with MATLAB
      • Labeling
        • Label audio data using your existing models
        • Label image data using GPT-4o
      • Edge Impulse Datasets
    • Feature extraction
      • Building custom processing blocks
      • Sensor fusion using embeddings
    • Machine learning
      • Classification with multiple 2D input features
      • Visualize neural networks decisions with Grad-CAM
      • Sensor fusion using embeddings
      • FOMO self-attention
    • Inferencing & post-processing
      • Count objects using FOMO
      • Continuous audio sampling
      • Multi-impulse (C++)
      • Multi-impulse (Python)
    • Lifecycle management
      • CI/CD with GitHub Actions
      • Data aquisition from S3 object store - Golioth on AI
      • OTA model updates
        • with Arduino IDE (for ESP32)
        • with Arduino IoT Cloud
        • with Blues Wireless
        • with Docker on Allxon
        • with Docker on Balena
        • with Docker on NVIDIA Jetson
        • with Espressif IDF
        • with Nordic Thingy53 and the Edge Impulse app
        • with Particle Workbench
        • with Zephyr on Golioth
    • API examples
      • Customize the EON Tuner
      • Ingest multi-labeled data using the API
      • Python API bindings example
      • Running jobs using the API
      • Trigger connected board data sampling
    • Python SDK examples
      • Using the Edge Impulse Python SDK to run EON Tuner
      • Using the Edge Impulse Python SDK to upload and download data
      • Using the Edge Impulse Python SDK with Hugging Face
      • Using the Edge Impulse Python SDK with SageMaker Studio
      • Using the Edge Impulse Python SDK with TensorFlow and Keras
      • Using the Edge Impulse Python SDK with Weights & Biases
    • Expert network projects
  • Edge Impulse Studio
    • Organization hub
      • Users
      • Data campaigns
      • Data
        • Cloud data storage
      • Data pipelines
      • Data transformation
        • Transformation blocks
      • Upload portals
      • Custom blocks
        • Custom AI labeling blocks
        • Custom deployment blocks
        • Custom learning blocks
        • Custom processing blocks
        • Custom synthetic data blocks
        • Custom transformation blocks
      • Health reference design
        • Synchronizing clinical data with a bucket
        • Validating clinical data
        • Querying clinical data
        • Transforming clinical data
    • Project dashboard
      • Select AI hardware
    • Devices
    • Data acquisition
      • Uploader
      • Data explorer
      • Data sources
      • Synthetic data
      • Labeling queue
      • AI labeling
      • CSV Wizard (time-series)
      • Multi-label (time-series)
      • Tabular data (pre-processed & non-time-series)
      • Metadata
      • Auto-labeler | deprecated
    • Impulses
    • EON Tuner
      • Search space
    • Processing blocks
      • Audio MFCC
      • Audio MFE
      • Audio Syntiant
      • Flatten
      • HR/HRV features
      • Image
      • IMU Syntiant
      • Raw data
      • Spectral features
      • Spectrogram
      • Custom processing blocks
      • Feature explorer
    • Learning blocks
      • Anomaly detection (GMM)
      • Anomaly detection (K-means)
      • Classification
      • Classical ML
      • Object detection
        • MobileNetV2 SSD FPN
        • FOMO: Object detection for constrained devices
      • Object tracking
      • Regression
      • Transfer learning (images)
      • Transfer learning (keyword spotting)
      • Visual anomaly detection (FOMO-AD)
      • Custom learning blocks
      • Expert mode
      • NVIDIA TAO | deprecated
    • Retrain model
    • Live classification
    • Model testing
    • Performance calibration
    • Deployment
      • EON Compiler
      • Custom deployment blocks
    • Versioning
    • Bring your own model (BYOM)
    • File specifications
      • deployment-metadata.json
      • ei-metadata.json
      • ids.json
      • parameters.json
      • sample_id_details.json
      • train_input.json
  • Tools
    • API and SDK references
    • Edge Impulse CLI
      • Installation
      • Serial daemon
      • Uploader
      • Data forwarder
      • Impulse runner
      • Blocks
      • Himax flash tool
    • Edge Impulse for Linux
      • Linux Node.js SDK
      • Linux Go SDK
      • Linux C++ SDK
      • Linux Python SDK
      • Flex delegates
      • Rust Library
    • Rust Library
    • Edge Impulse Python SDK
  • Run inference
    • C++ library
      • As a generic C++ library
      • On Android
      • On your desktop computer
      • On your Alif Ensemble Series Device
      • On your Espressif ESP-EYE (ESP32) development board
      • On your Himax WE-I Plus
      • On your Raspberry Pi Pico (RP2040) development board
      • On your SiLabs Thunderboard Sense 2
      • On your Spresense by Sony development board
      • On your Syntiant TinyML Board
      • On your TI LaunchPad using GCC and the SimpleLink SDK
      • On your Zephyr-based Nordic Semiconductor development board
    • Arm Keil MDK CMSIS-PACK
    • Arduino library
      • Arduino IDE 1.18
    • Cube.MX CMSIS-PACK
    • Docker container
    • DRP-AI library
      • DRP-AI on your Renesas development board
      • DRP-AI TVM i8 on Renesas RZ/V2H
    • IAR library
    • Linux EIM executable
    • OpenMV
    • Particle library
    • Qualcomm IM SDK GStreamer
    • WebAssembly
      • Through WebAssembly (Node.js)
      • Through WebAssembly (browser)
    • Edge Impulse firmwares
    • Hardware specific tutorials
      • Image classification - Sony Spresense
      • Audio event detection with Particle boards
      • Motion recognition - Particle - Photon 2 & Boron
      • Motion recognition - RASynBoard
      • Motion recognition - Syntiant
      • Object detection - SiLabs xG24 Dev Kit
      • Sound recognition - TI LaunchXL
      • Keyword spotting - TI LaunchXL
      • Keyword spotting - Syntiant - RC Commands
      • Running NVIDIA TAO models on the Renesas RA8D1
      • Two cameras, two models - running multiple object detection models on the RZ/V2L
  • Edge AI Hardware
    • Overview
    • Production-ready
      • Advantech ICAM-540
      • Seeed SenseCAP A1101
      • Industry reference design - BrickML
    • MCU
      • Ambiq Apollo4 family of SoCs
      • Ambiq Apollo510
      • Arducam Pico4ML TinyML Dev Kit
      • Arduino Nano 33 BLE Sense
      • Arduino Nicla Sense ME
      • Arduino Nicla Vision
      • Arduino Portenta H7
      • Blues Wireless Swan
      • Espressif ESP-EYE
      • Himax WE-I Plus
      • Infineon CY8CKIT-062-BLE Pioneer Kit
      • Infineon CY8CKIT-062S2 Pioneer Kit
      • Nordic Semi nRF52840 DK
      • Nordic Semi nRF5340 DK
      • Nordic Semi nRF9160 DK
      • Nordic Semi nRF9161 DK
      • Nordic Semi nRF9151 DK
      • Nordic Semi nRF7002 DK
      • Nordic Semi Thingy:53
      • Nordic Semi Thingy:91
      • Open MV Cam H7 Plus
      • Particle Photon 2
      • Particle Boron
      • RAKwireless WisBlock
      • Raspberry Pi RP2040
      • Renesas CK-RA6M5 Cloud Kit
      • Renesas EK-RA8D1
      • Seeed Wio Terminal
      • Seeed XIAO nRF52840 Sense
      • Seeed XIAO ESP32 S3 Sense
      • SiLabs Thunderboard Sense 2
      • Sony's Spresense
      • ST B-L475E-IOT01A
      • TI CC1352P Launchpad
    • MCU + AI accelerators
      • Alif Ensemble
      • Arduino Nicla Voice
      • Avnet RASynBoard
      • Seeed Grove - Vision AI Module
      • Seeed Grove Vision AI Module V2 (WiseEye2)
      • Himax WiseEye2 Module and ISM Devboard
      • SiLabs xG24 Dev Kit
      • STMicroelectronics STM32N6570-DK
      • Synaptics Katana EVK
      • Syntiant Tiny ML Board
    • CPU
      • macOS
      • Linux x86_64
      • Raspberry Pi 4
      • Raspberry Pi 5
      • Texas Instruments SK-AM62
      • Microchip SAMA7G54
      • Renesas RZ/G2L
    • CPU + AI accelerators
      • AVNET RZBoard V2L
      • BrainChip AKD1000
      • i.MX 8M Plus EVK
      • Digi ConnectCore 93 Development Kit
      • MemryX MX3
      • MistyWest MistySOM RZ/V2L
      • Qualcomm Dragonwing RB3 Gen 2 Dev Kit
      • Renesas RZ/V2L
      • Renesas RZ/V2H
      • IMDT RZ/V2H
      • Texas Instruments SK-TDA4VM
      • Texas Instruments SK-AM62A-LP
      • Texas Instruments SK-AM68A
      • Thundercomm Rubik Pi 3
    • GPU
      • Advantech ICAM-540
      • NVIDIA Jetson
      • Seeed reComputer Jetson
    • Mobile phone
    • Porting guide
  • Integrations
    • Arduino Machine Learning Tools
    • AWS IoT Greengrass
    • Embedded IDEs - Open-CMSIS
    • NVIDIA Omniverse
    • Scailable
    • Weights & Biases
  • Tips & Tricks
    • Combining impulses
    • Increasing model performance
    • Optimizing compute time
    • Inference performance metrics
  • Concepts
    • Glossary
    • Course: Edge AI Fundamentals
      • Introduction to edge AI
      • What is edge computing?
      • What is machine learning (ML)?
      • What is edge AI?
      • How to choose an edge AI device
      • Edge AI lifecycle
      • What is edge MLOps?
      • What is Edge Impulse?
      • Case study: Izoelektro smart grid monitoring
      • Test and certification
    • Data engineering
      • Audio feature extraction
      • Motion feature extraction
    • Machine learning
      • Data augmentation
      • Evaluation metrics
      • Neural networks
        • Layers
        • Activation functions
        • Loss functions
        • Optimizers
          • Learned optimizer (VeLO)
        • Epochs
    • What is embedded ML, anyway?
    • What is edge machine learning (edge ML)?
Powered by GitBook
On this page
  • Block structure
  • Block interface
  • Inputs
  • Outputs
  • Understanding operating modes
  • File
  • Directory
  • Standalone
  • Updating data item metadata
  • Showing the block in Studio
  • For transformation jobs
  • For project data sources
  • Initializing the block
  • Testing the block locally
  • With blocks runner
  • With Docker
  • Pushing the block to Edge Impulse
  • Using the block in Studio
  • Examples
  • Troubleshooting
  • Additional resources

Was this helpful?

Export as PDF
  1. Edge Impulse Studio
  2. Organization hub
  3. Custom blocks

Custom transformation blocks

PreviousCustom synthetic data blocksNextHealth reference design

Last updated 2 months ago

Was this helpful?

Custom transformation blocks are a way to extend the capabilities of Edge Impulse beyond the built into the platform. If none of the existing blocks created by Edge Impulse fit your needs, you can create custom transformation blocks to integrate your own data pre-processing for unique project requirements.

Ready to dive in and start building? Jump to the !

Block structure

The transformation block structure is shown below. Please see the overview page for more details.

Block interface

The sections below define the required and optional inputs and the expected outputs for custom transformation blocks.

Inputs

Transformation blocks have access to environment variables, command line arguments, and mounted storage buckets.

Environment variables

The following environment variables are accessible inside of transformation blocks. Environment variable values are always stored as strings.

Variable
Passed
Description

EI_API_ENDPOINT

Always

The API base URL: https://studio.edgeimpulse.com/v1

EI_API_KEY

Always

The organization API key with member privileges: ei_2f7f54...

EI_INGESTION_HOST

Always

The host for the ingestion API: edgeimpulse.com

EI_LAST_SUCCESSFUL_RUN

Always

The last time the block was successfully run, if a part of a data pipeline: 1970-01-01T00:00:00.000Z

EI_ORGANIZATION_ID

Always

The ID of the organization that the block belongs to: 123456

EI_PROJECT_ID

Conditional

Passed if the transformation block is a data source for a project. The ID of the project: 123456

EI_PROJECT_API_KEY

Conditional

Passed if the transformation block is a data source for a project. The project API key: ei_2a1b0e...

You can also define your own environment variables to pass to your custom block using the requiredEnvVariables property in the parameters.json file. You will then be prompted for the associated values for these properties when pushing the block to Edge Impulse using the CLI. Alternatively, these values can be added (or changed) by editing the block in Studio after pushing.

Command line arguments

In addition to the items defined by you, the following arguments will be automatically passed to your custom transformation block.

Argument
Passed
Description

--in-file <file>

Conditional

Passed if operation mode is set to file. Provides the file path as a string. This is the file to be processed by the block.

--in-directory <dir>

Conditional

Passed if operation mode is set to directory. Provides the directory path as a string. This is the directory to be processed by the block.

--out-directory <dir>

Conditional

Passed if operation mode is set to either file or directory. Provides the directory path to the output directory as a string. This is where block output needs to be written.

--hmac-key <key>

Conditional

Passed if operation mode is set to either file or directory. Provides a project HMAC key as a string, if it exists, otherwise '0'.

--metadata <metadata>

Conditional

Passed if operation mode is set to either file or directory, the pass in metadata property (indMetadata) is set to true, and the metadata exists. Provides the metadata associated with data item as a stringified JSON object.

--upload-category <category>

Conditional

Passed if operation mode is set to file or directory and the transformation job is configured to import the results into a project. Provides the upload category (split, training, or testing) as a string.

--upload-label <label>

Conditional

Passed if operation mode is set to file or directory and the transformation job is configured to import the results into a project. Provides the upload label as a string.

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.

Lastly a user can be prompted for extra CLI arguments when configuring a transformation job if the allowExtraCliArguments property is set to true.

Mounted storage buckets

/mnt/s3fs/<bucket-name>

The mount point can be changed by editing your parameters.json file before pushing the block to Edge Impulse or editing the block in Studio after pushing.

Outputs

There are no required outputs from transformation blocks. In general, for blocks operating in file or directory mode, new data is written to the directory given by the --out-directory <dir> argument. For blocks operating in standalone mode, any actions are typically achieved using API calls inside the block itself.

Understanding operating modes

Transformation blocks can operate in one of three modes: file, directory, or standalone.

File

As the name implies, file transformation blocks operate on files. When configuring a transformation job, the user will select a list of files to transform. These files will be individually passed to and processed by the script defined in your transformation block. File transformation blocks can be run in multiple processing jobs in parallel.

Each file will be passed to your block using the --in-file <file> argument.

Directory

As the name implies, directory transformation blocks operate on directories. When configuring a transformation job, the user will select a list of directories to transform. These directories will be individually passed to and processed by the script defined in your transformation block. Directory transformation blocks can be run in multiple processing jobs in parallel.

Each directory will be passed to your block using the --in-directory <dir> argument.

Standalone

Standalone transformation blocks are a flexible way to run generic cloud jobs that can be used for a wide variety of tasks. In standalone mode, no data is passed into your block. If you need to access your data, you will need to mount your storage bucket(s) into your block. Standalone transformation blocks are run as a single processing job; they cannot be run in multiple processing jobs in parallel.

Updating data item metadata

If your custom transformation block is operating in directory mode and transforming a clinical dataset, you can update the metadata associated with the data item after it is processed.

with open(os.path.join(args.out_directory, 'ei-metadata.json'), 'w') as f:
    f.write(json.dumps({
        'version': 1,
        'action': 'add',
        'metadata': {
            'now': round(time.time() * 1000)
        }
    }))

Showing the block in Studio

There are two locations within Studio that transformation blocks can be found: transformation jobs and project data sources.

Transformation blocks operating in file or directory mode will always been shown as an option in the block dropdown for transformation jobs. They cannot be used as a project data source.

Transformation blocks operating in standalone mode can optionally be shown in the block dropdown for transformation jobs and/or in the block dropdown for project data sources.

For transformation jobs

Operating mode
Shown in block dropdown

file

Always

directory

Always

standalone

If showInCreateTransformationJob property set to true.

For project data sources

Operating mode
Shown in block dropdown

file

Never

directory

Never

standalone

If showInDataSources property set to true.

Initializing the block

Testing the block locally

To speed up your development process, you can test your custom transformation block locally. There are two ways to achieve this. You will need to have Docker installed on your machine for either approach.

With blocks runner

If your custom transformation block is operating in either file or directory mode, you will be prompted for information to look up and download data (a file or a directory) for the block to operate on when using the blocks runner. This can be achieved by providing either a data item name (clinical data) or the path within a dataset for a file or directory (clinical or default data). You can also specify some of this information using the blocks runner command line arguments.

Argument
Description

--dataset <dataset>

Transformation blocks in file or directory mode. Files and directories will be looked up within this dataset. If not provided, you will be prompted for a dataset name.

--data-item <data-item>

Clinical data only. Transformation blocks in directory mode. The data item will be looked up, downloaded, and passed to the container when it is run. If not provided, you will be prompted for the information required to look up a data item.

--file <filename>

Clinical data only. Transformation blocks in file mode. Must be used in conjunction with --data-item <data-item>. The file will be looked up, downloaded, and passed to the container when it is run. If not provided, you will be prompted for the information required to look up a file within a data item.

--skip-download

Skips downloading the data.

--extra-args <args>

Additional arguments for your script.

Additional arguments to your script can be provided as a single string using --extra-args <args> argument.

 edge-impulse-blocks runner --extra-args "--custom-param-one foo --custom-param-two bar"

Using the above approach will create an ei-block-data directory within your custom block directory. It will contain subdirectories for the data that has been downloaded.

With Docker

For the second method, you can build the Docker image and run the container directly. You will need to pass any environment variables or command line arguments required by your script to the container when you run it.

If your transformation block operates in either file or directory mode, you will also need to create a data/ directory within your custom block directory and place your data used for testing here.

docker build -t custom-transformation-block .

file mode:

docker run --rm -v $PWD/data:/data -e CUSTOM_ENV_VAR='<env-value>' custom-transformation-block --in-file /data/<file> --out-directory /data/out --custom-param foo

directory mode:

docker run --rm -v $PWD/data:/data -e CUSTOM_ENV_VAR='<env-value>' custom-transformation-block --in-directory /data  --out-directory /data/out --custom-param foo

standalone mode:

docker run --rm -e CUSTOM_ENV_VAR='<env-value>' custom-transformation-block --custom-param foo

Pushing the block to Edge Impulse

Using the block in Studio

Examples

Note that when using the above search term you will come across synthetic data blocks as well. Please read the repository description to identify if it is for a transformation block or a synthetic data block.

Further, several example transformation blocks have been gathered into a single repository:

Troubleshooting

Files in storage bucket cannot be accessed

If you cannot access the files in your storage bucket, make sure that the mount point has been properly configured and that you are referencing this location within your processing script.

You can double check the mount point by looking at the additional mount point section when editing the block in Studio:

If you do not see your storage bucket, you can mount it here.

When using the Edge Impulse CLI to initialize your block, it is a common mistake to forget to press the <space> key to select the bucket for mounting (and therefore the storage bucket is not added to the parameters.json file).

Transformation job runs indefinitely

If you notice that your transformation job runs indefinitely, it is probably because of an error with your processing script or the script has not been properly terminated.

Make sure to exit your script with code 0 (return 0, exit(0) or sys.exit(0)) for success or with any other error code for failure.

Additional resources

The parameter items defined in your parameters.json file will be passed as command line arguments to the script you defined in your Dockerfile as the ENTRYPOINT for the Docker image. Please refer to the documentation for further details about creating this file, parameter options available, and examples.

One or more buckets can be mounted inside of your block. If storage buckets exist in your organization, you will be prompted to mount the bucket(s) when initializing the block with the Edge Impulse CLI. The default mount point will be:

To do so, your custom transformation block needs to write an ei-metadata.json file to the directory specified in the --out-directory <dir> argument. Please refer to the documentation for further details about this file.

For the first method, you can use the CLI edge-impulse-blocks runner tool. See for additional details.

Edge Impulse has developed several transformation blocks, some of which are built into the platform. The code for these blocks can be found in public repositories under the . The repository names typically follow the convention of example-transform-<description>. As such, they can be found by going to the Edge Impulse account and searching the repositories for example-transform.

parameters.json
cloud data storage
ei-metadata.json
Edge Impulse GitHub account
Transformation block examples
Custom blocks
Transformation blocks
edge-impulse-blocks
parameters.json
ei-metadata.json
transformation blocks
custom blocks
examples
Block runner
Custom transformation block structure
Setting up mount point

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.

custom blocks

Only available on the Enterprise plan

This feature is only available on the Enterprise plan. Review our or sign up for our free today.

plans and pricing
Enterprise trial

After you have pushed your block to Edge Impluse, it can be used in the same way as any other built-in block.

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.

custom blocks