The official Embedl Hub Python client library.
Project description
Embedl Hub Python library
Embedl Hub is a platform for building efficient edge AI applications. With Embedl Hub, you can:
- Find the best model for your application using on-device benchmarks.
- Fine-tune the model on your own dataset and benchmark it on your target device.
- Deploy your application with the confidence that your model meets your performance requirements.
The Embedl Hub Python library (embedl-hub) lets you interact with Embedl Hub in scripts
and from your terminal. Create a free Embedl Hub account
to get started with the embedl-hub library.
Installation
The simplest way to install embedl-hub is through pip:
pip install embedl-hub
Quickstart
We recommend using our end-to-end workflow CLI to quickly get started building your edge AI application:
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). │
│ --install-completion Install completion for the current shell. │
│ --show-completion Show completion for the current shell, to copy it or customize the installation. │
│ --help Show this message and exit. │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
╭─ Commands ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ init Create new or load existing project and/or experiment. │
│ show Print active project/experiment IDs and names. │
│ tune Fine-tune a model on your dataset. │
│ export Compile a TorchScript model into an ONNX model using Qualcomm AI Hub. │
│ quantize Quantize an ONNX model using Qualcomm AI Hub. │
│ compile Compile an ONNX model into a device ready binary using Qualcomm AI Hub. │
│ benchmark Profile compiled model on device and measure it's performance. │
│ list-devices List all available target devices. │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
License
Copyright (C) 2025 Embedl AB
This software is subject to the Embedl Hub Software License Agreement.
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Filter files by name, interpreter, ABI, and platform.
If you're not sure about the file name format, learn more about wheel file names.
Copy a direct link to the current filters
File details
Details for the file embedl_hub-2025.7.0.tar.gz.
File metadata
- Download URL: embedl_hub-2025.7.0.tar.gz
- Upload date:
- Size: 46.3 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/6.1.0 CPython/3.11.11
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
41b08a8887d85c519f6c9512938edbd73f8da0644f203fa7ea8b182b8e2cfadf
|
|
| MD5 |
192c7daee50e8d981df57bce0d6dada3
|
|
| BLAKE2b-256 |
74780cea60efe0a679f637cf295ab52ed1cf82ae2fc991977a9c81662ccd5c4f
|
File details
Details for the file embedl_hub-2025.7.0-py3-none-any.whl.
File metadata
- Download URL: embedl_hub-2025.7.0-py3-none-any.whl
- Upload date:
- Size: 51.1 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/6.1.0 CPython/3.11.11
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
cb9cd837fa401243a467b262683d822ed307f9ebdf9082eaef4b6f142282f13d
|
|
| MD5 |
d951fd8281d1cb16e851ba52828ba28a
|
|
| BLAKE2b-256 |
ff9ef0260fadf34245736dcbf72588c28fddd7c719d5158b7f24f16b379fb362
|
