Skip to main content

The official Embedl Hub Python client library.

Project description

Embedl Hub Python library

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

  • Quantize your model for lower latency and memory usage.
  • Compile your model for execution on CPU, GPU, NPU or other AI accelerators on your target devices.
  • Benchmark your model's latency and memory usage on real edge devices in the cloud.

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

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 ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ auth           Store the API key for embedl-hub CLI.                                                                                                                │
│ init           Configure persistent CLI context.                                                                                                                    │
│ show           Print active project/experiment IDs and names.                                                                                                       │
│ compile        Compile a model into a device ready binary using Qualcomm AI Hub.                                                                                    │
│                Qualcomm AI Hub may return a zip file containing multiple files.                                                                                     │
│ quantize       Quantize an ONNX model using Qualcomm AI Hub.                                                                                                        │
│                Qualcomm AI Hub may return a zip file containing multiple files.                                                                                     │
│ benchmark      Benchmark 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.

Project details


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

embedl_hub-2025.11.3.tar.gz (68.2 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

embedl_hub-2025.11.3-py3-none-any.whl (68.7 kB view details)

Uploaded Python 3

File details

Details for the file embedl_hub-2025.11.3.tar.gz.

File metadata

  • Download URL: embedl_hub-2025.11.3.tar.gz
  • Upload date:
  • Size: 68.2 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.11.14

File hashes

Hashes for embedl_hub-2025.11.3.tar.gz
Algorithm Hash digest
SHA256 82c39359ba70bbe09c32d162f8ea5665504c5ee5ccf6211d39eac84ae2010320
MD5 9490d1f785250f10566e1e5e5a813b89
BLAKE2b-256 864b348b77215d49a43ef7b2069a55e5023b5bb4fbc0e275a77b5296755d9e54

See more details on using hashes here.

File details

Details for the file embedl_hub-2025.11.3-py3-none-any.whl.

File metadata

  • Download URL: embedl_hub-2025.11.3-py3-none-any.whl
  • Upload date:
  • Size: 68.7 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.11.14

File hashes

Hashes for embedl_hub-2025.11.3-py3-none-any.whl
Algorithm Hash digest
SHA256 f388056e4eb7d2c4e28d2219d0b3dca49d489045128f3822ea58db88bc499973
MD5 620c2dc7ea86e86342230135b4c31473
BLAKE2b-256 5b12dcbd0ad50eb72013387c81ce262551167d18b3a3bcef8b9e164f6af0865d

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page