embedl-hub 2026.5.0

The official Embedl Hub Python client library.

Embedl Hub Python library

Optimize and deploy your model on any edge device with the Embedl Hub Python library:

• Compile your model for execution on CPU, GPU, NPU or other AI accelerators using ONNX Runtime, TensorRT, or TFLite backends.
• Profile your model's latency and memory usage on real edge devices in the cloud.
• Invoke your compiled model to run inference with real input data.

The library logs your metrics, parameters, and results on the Embedl Hub website, allowing you to inspect, compare, and reproduce your results.

For comprehensive getting started guides and API reference, visit the Embedl Hub documentation.

Create a free Embedl Hub account to get started.

Installation

embedl-hub requires Python 3.10 or newer.

Install embedl-hub with pip:

pip install embedl-hub

Linux aarch64 installs support the TensorRT and ONNX Runtime workflows. Local TFLite conversion and other ai-edge-quantizer-backed workflows are not available there because the upstream TensorFlow package does not publish Linux aarch64 distributions.

Usage

The embedl-hub library can be used in two ways:

CLI

The embedl-hub (or ehub) command provides an end-to-end workflow for compiling, profiling, and invoking models from the terminal:

Usage: embedl-hub [OPTIONS] COMMAND [ARGS]...

 embedl-hub end-to-end Edge-AI workflow CLI

╭─ Options ────────────────────────────────────────────────────────────────╮
│ --version      -V               Print embedl-hub version and exit.       │
│ --verbose      -v      INTEGER  Increase verbosity (-v, -vv, -vvv).      │
│ --help                          Show this message and exit.              │
╰──────────────────────────────────────────────────────────────────────────╯
╭─ Commands ───────────────────────────────────────────────────────────────╮
│ auth           Store the API key for embedl-hub CLI.                     │
│ init           Configure persistent CLI context.                         │
│ show           Print the active project name and artifact directory.     │
│ compile        Compile a model for on-device deployment.                 │
│ profile        Profile a compiled model on a target device.              │
│ invoke         Run inference on a compiled model.                        │
│ log            Show past runs from the artifact directory.               │
│ list-devices   List available devices.                                   │
╰──────────────────────────────────────────────────────────────────────────╯

Python API

For programmatic use, import from the embedl_hub package. The API provides compiler, profiler, and invoker components for each supported backend (ONNX Runtime, TensorRT, TFLite):

from embedl_hub.compile import OnnxRuntimeCompiler
from embedl_hub.profile import OnnxRuntimeProfiler
from embedl_hub.invoke import OnnxRuntimeInvoker

See the Embedl Hub documentation for detailed guides and examples.

License

Copyright (C) 2025, 2026 Embedl AB

This software is subject to the Embedl Hub Software License Agreement.