Skip to main content

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      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-2025.9.1.dev0.tar.gz (48.9 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-2025.9.1.dev0-py3-none-any.whl (55.1 kB view details)

Uploaded Python 3

File details

Details for the file embedl_hub-2025.9.1.dev0.tar.gz.

File metadata

  • Download URL: embedl_hub-2025.9.1.dev0.tar.gz
  • Upload date:
  • Size: 48.9 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.11.11

File hashes

Hashes for embedl_hub-2025.9.1.dev0.tar.gz
Algorithm Hash digest
SHA256 9b45c06605aed6c4fcb1f4996bcc5b52d736b42fdb65bf1781a32c911506cf6a
MD5 f3ae7071ada43cab0765c5366aac53e1
BLAKE2b-256 5a3d890c5cc424d0e28ebe312b5dac4624ffc6e2ec11457a52713cea722269b5

See more details on using hashes here.

File details

Details for the file embedl_hub-2025.9.1.dev0-py3-none-any.whl.

File metadata

File hashes

Hashes for embedl_hub-2025.9.1.dev0-py3-none-any.whl
Algorithm Hash digest
SHA256 4e4a65509c30ac02b30cfff2d3574f2cc13a328b5978eea4b118f3fd8d4d41a3
MD5 d9eba77c8db806bdb78eb827d9a04a24
BLAKE2b-256 bb8212876649d0d104da5d5742515c99b2281773200252f5303c16ca4de5fa0c

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