Embedl Hub

Embedl Hub is a secure, traceable MLOps platform for edge AI workflows. It centralizes model compilation, on-device testing, validation, and artifact tracking into a single system. This enables teams to deliver production-grade edge AI with full traceability and compliance support — especially in safety-critical environments.

The Embedl Hub Python library (embedl-hub) enables you to programmatically interact with the Embedl Hub platform. Create a free Embedl Hub account to get started with the embedl-hub library.

Installation

embedl-hub requires Python 3.10 or newer.

The base install gives you experiment tracking, the CLI shell (auth, init, log, list-devices), and the Python tracking API — without any backend execution toolchain:

pip install embedl-hub

To use the bundled compile, profile, and invoke components (Embedl’s ready-made wrappers for TFLite, ONNX Runtime, and TensorRT), install one or more toolchain extras:

Workflow	Install command
TFLite (local + QAI Hub + device cloud)	pip install 'embedl-hub[tflite]'
ONNX Runtime (QAI Hub + embedl-ORT)	pip install 'embedl-hub[onnxruntime]'
TensorRT (trtexec over SSH)	pip install 'embedl-hub[tensorrt]'
Everything	pip install 'embedl-hub[all]'

Linux aarch64 installs of [tflite] skip the onnx2tf-based local conversion path and the ai-edge-* quantization deps because the upstream TensorFlow package does not publish Linux aarch64 wheels. The QAI Hub TFLite provider still works.

Running a backend command without its extra installed prints a clear hint pointing at the right pip install command rather than a raw import traceback.

Documentation

Tutorials and guides are available at hub.embedl.com/docs.

References

• API Reference
• CLI Reference