LogoLogo
HomeDocsAPIProjectsForum
  • Getting Started
    • For beginners
    • For ML practitioners
    • For embedded engineers
  • Frequently asked questions
  • Tutorials
    • End-to-end tutorials
      • Continuous motion recognition
      • Responding to your voice
      • Recognize sounds from audio
      • Adding sight to your sensors
        • Collecting image data from the Studio
        • Collecting image data with your mobile phone
        • Collecting image data with the OpenMV Cam H7 Plus
      • Object detection
        • Detect objects using MobileNet SSD
        • Detect objects with FOMO
      • Sensor fusion
      • Sensor fusion using Embeddings
      • Processing PPG input with HR/HRV Features Block
      • Industrial Anomaly Detection on Arduino® Opta® PLC
    • Advanced inferencing
      • Continuous audio sampling
      • Multi-impulse
      • Count objects using FOMO
    • API examples
      • Running jobs using the API
      • Python API Bindings Example
      • Customize the EON Tuner
      • Ingest multi-labeled data using the API
      • Trigger connected board data sampling
    • ML & data engineering
      • EI Python SDK
        • Using the Edge Impulse Python SDK with TensorFlow and Keras
        • Using the Edge Impulse Python SDK to run EON Tuner
        • Using the Edge Impulse Python SDK with Hugging Face
        • Using the Edge Impulse Python SDK with Weights & Biases
        • Using the Edge Impulse Python SDK with SageMaker Studio
        • Using the Edge Impulse Python SDK to upload and download data
      • Label image data using GPT-4o
      • Label audio data using your existing models
      • Generate synthetic datasets
        • Generate image datasets using Dall·E
        • Generate keyword spotting datasets
        • Generate physics simulation datasets
        • Generate audio datasets using Eleven Labs
      • FOMO self-attention
    • Lifecycle Management
      • CI/CD with GitHub Actions
      • OTA Model Updates
        • with Nordic Thingy53 and the Edge Impulse APP
      • Data Aquisition from S3 Object Store - Golioth on AI
    • Expert network projects
  • Edge Impulse Studio
    • Organization hub
      • Users
      • Data campaigns
      • Data
      • Data transformation
      • Upload portals
      • Custom blocks
        • Transformation blocks
        • Deployment blocks
          • Deployment metadata spec
      • Health Reference Design
        • Synchronizing clinical data with a bucket
        • Validating clinical data
        • Querying clinical data
        • Transforming clinical data
        • Buildling data pipelines
    • 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]
    • Impulse design & Experiments
    • Bring your own model (BYOM)
    • Processing blocks
      • Raw data
      • Flatten
      • Image
      • Spectral features
      • Spectrogram
      • Audio MFE
      • Audio MFCC
      • Audio Syntiant
      • IMU Syntiant
      • HR/HRV features
      • Building custom processing blocks
        • Hosting custom DSP blocks
      • Feature explorer
    • Learning blocks
      • Classification (Keras)
      • Anomaly detection (K-means)
      • Anomaly detection (GMM)
      • Visual anomaly detection (FOMO-AD)
      • Regression (Keras)
      • Transfer learning (Images)
      • Transfer learning (Keyword Spotting)
      • Object detection (Images)
        • MobileNetV2 SSD FPN
        • FOMO: Object detection for constrained devices
      • NVIDIA TAO (Object detection & Images)
      • Classical ML
      • Community learn blocks
      • Expert Mode
      • Custom learning blocks
    • EON Tuner
      • Search space
    • Retrain model
    • Live classification
    • Model testing
    • Performance calibration
    • Deployment
      • EON Compiler
      • Custom deployment blocks
    • Versioning
  • 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
    • Edge Impulse Python SDK
  • Run inference
    • C++ library
      • As a generic C++ library
      • On your desktop computer
      • On your Zephyr-based Nordic Semiconductor development board
    • Linux EIM Executable
    • WebAssembly
      • Through WebAssembly (Node.js)
      • Through WebAssembly (browser)
    • Docker container
    • Edge Impulse firmwares
  • Edge AI Hardware
    • Overview
    • MCU
      • 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
    • CPU
      • macOS
      • Linux x86_64
    • Mobile Phone
    • Porting Guide
  • Integrations
    • Arduino Machine Learning Tools
    • NVIDIA Omniverse
    • Embedded IDEs - Open-CMSIS
    • Scailable
    • Weights & Biases
  • Pre-built datasets
    • Continuous gestures
    • Running faucet
    • Keyword spotting
    • LiteRT (Tensorflow Lite) reference models
  • Tips & Tricks
    • Increasing model performance
    • Data augmentation
    • Inference performance metrics
    • Optimize compute time
    • Adding parameters to custom blocks
    • Combine Impulses
  • Concepts
    • Glossary
    • Data Engineering
      • Audio Feature Extraction
      • Motion Feature Extraction
    • ML Concepts
      • Neural Networks
        • Layers
        • Activation Functions
        • Loss Functions
        • Optimizers
          • Learned Optimizer (VeLO)
        • Epochs
      • Evaluation Metrics
    • Edge AI
      • 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
    • What is embedded ML, anyway?
    • What is edge machine learning (edge ML)?
Powered by GitBook
On this page
  • Block runner
  • Block structure
  • Excluding files
  1. Tools
  2. Edge Impulse CLI

Blocks

PreviousImpulse runnerNextHimax flash tool

Last updated 6 months ago

The blocks CLI tool creates different blocks types that are used in organizational features such as:

  • - to transform large sets of data efficiently.

  • - to build personalized firmware using your own data or to create custom libraries.

  • - to create and host your custom signal processing techniques and use them directly in your projects.

  • - to use your custom neural network architectures and load pre-trained weights, with Keras, PyTorch and scikit-learn.

With the blocks CLI tool, you can create new blocks, run them locally, and push them to Edge Impulse infrastructure so we can host them for you. Edge Impulse blocks can be written in any language, and are based on Docker container for maximum flexibility.

As an example here, we will show how to create a transformation block.

You can create a new block by running:

$ edge-impulse-blocks init
? What is your user name or e-mail address (edgeimpulse.com)? jan@edgeimpulse.com
? What is your password? [hidden]
? In which organization do you want to create this block? EdgeImpulse Inc.
Attaching block to organization 'EdgeImpulse Inc.'
? Choose a type of block Transformation block
? Choose an option Create a new block
? Enter the name of your block Extract voice
? Enter the description of your block Extracts voice from video files
Creating block with config: {
  name: 'Extract voice',
  type: 'transform',
  description: 'Extracts voice from video files',
  organizationId: 4
}
? Would you like to download and load the example repository (Python)? yes
Template repository fetched!
Your new block 'Extract voice' has been created in '/Users/janjongboom/repos/custom-transform-block'.
When you have finished building your transformation block, run "edge-impulse-blocks push" to update the block in Edge Impulse.

When you're done developing the block you can push it to Edge Impulse via:

$ edge-impulse-blocks push
Archiving 'custom-transform-block'...
Archiving 'custom-transform-block' OK (2 KB)

Uploading block 'Extract voice' to organization 'EdgeImpulse Inc.'...
Uploading block 'Extract voice' to organization 'EdgeImpulse Inc.' OK

Building transformation block 'Extract voice'...
INFO[0000] Retrieving image manifest python:3.7.5-stretch
INFO[0000] Retrieving image python:3.7.5-stretch

...

Building transformation block 'Extract voice' OK

Your block has been updated, go to https://studio.edgeimpulse.com/organization/4/data to run a new transformation

The metadata about the block (which organization it belongs to, block ID) is saved in .ei-block-config, which you should commit. To view this data in a convenient format, run:

$ edge-impulse-blocks info
Name: TestDataItemTransform
Description: Data item transformation example
Organization ID: 1
Not pushed
Block type: transform
Operates on: dataitem
Bucket mount points:
    - ID: 1, Mount point: /path/to/bucket

Block runner

Rather than only running custom blocks in the cloud, the edge-impulse-blocks runner command lets developers download, configure, and run custom blocks entirely on their local machine, making testing and development much faster. The options depend on the type of block being run, and they can be viewed by using the help menu:

$ edge-impulse-blocks runner -h
Usage: edge-impulse-blocks runner [options]
Run the current block locally
Options:
  --data-item <dataItem>          Tranformation block: Name of data item
  --file <filename>               File tranformation block: Name of file in data item
  --epochs <number>               Transfer learning: # of epochs to train
  --learning-rate <learningRate>  Transfer learning: Learning rate while training
  --validation-set-size <size>    Transfer learning: Size of validation set
  --input-shape <shape>           Transfer learning: List of axis dimensions. Example: "(1, 4, 2)"
  --download-data                 Transfer learning or deploy: Only download data and don't run the block
  --port <number>                 DSP: Port to host DSP block on
  --extra-args <args>             Pass extra arguments/options to the Docker container
  -h, --help                      display help for command

As seen above, the runner accepts a list of relevant option flags along with a variable number of extra arguments that get passed to the Docker container at runtime for extra flexibility. As an example, here is what happens when edge-impulse-blocks runner is used on a file transformation block:

$ edge-impulse-blocks runner --data-item item1 --file sample_1.cbor
Found data item item1 with id=1, metadata={}
Downloading file sample_1.cbor to /path/to/block/data/dataset_1/item1...
File downloaded
...

Best of all, the runner only downloads data when it isn't present locally, thus saving time and bandwidth.

$ edge-impulse-blocks runner --data-item item1 --file sample_1.cbor
Found data item item1 with id=1, metadata={}
File already present; skipping download...
...

Block structure

Transformation blocks use Docker containers, a virtualization technique which lets developers package up an application with all dependencies in a single package. Thus, every block needs at least a Dockerfile. This is a file describing how to build the container that powers the block, and it has information about the dependencies for the block - like a list of Python packages your block needs. This Dockerfile needs to declare an ENTRYPOINT: a command that needs to run when the container starts.

An example of a Python container is:

FROM python:3.7.5-stretch

WORKDIR /app

# Python dependencies
COPY requirements.txt ./
RUN pip3 --no-cache-dir install -r requirements.txt

COPY . ./

ENTRYPOINT [ "python3",  "transform.py" ]

Which takes a base-image with Python 3.7.5, then installs all dependencies listed in requirements.txt, and finally starts a script called transform.py.

Note: Do not use a WORKDIR under /home! The /home path will be mounted in by Edge Impulse, making your files inaccessible.

Note: If you use a different programming language, make sure to use ENTRYPOINT to specify the application to execute, rather than RUN or CMD.

Besides your Dockerfile you'll also need the application files, in the example above transform.py and requirements.txt. You can place these in the same folder.

Excluding files

When pushing a new block all files in your folder are archived and sent to Edge Impulse, where the container is built. You can exclude files by creating a file called .ei-ignore in the root folder of your block. You can either set absolute paths here, or use wildcards to exclude many files. For example:

a-large-folder/*
some-path-to-a-text-file.txt

Clearing configuration

To clear the configuration, run:

$ edge-impulse-blocks --clean

This resets the CLI configuration and will prompt you to log in again.

API Key

You can use an API key to authenticate with:

$ edge-impulse-blocks --api-key ei_...

Note that this resets the CLI configuration and automatically configures your organization.

Other options

  • --dev - lists development servers, use in conjunction with --clean.

Transformation blocks
Deployment blocks
Custom DSP blocks
Custom machine learning models