Daisykit Python
https://pypi.org/project/daisykit/
Daisykit is an easy AI toolkit for software engineers to integrate pretrained AI models and pipelines into their projects. You DON'T need to be an AI engineer to build AI software. This open source project includes:
- Daisykit SDK - C++, the core of models and algorithms in NCNN deep learning framework.
- Daisykit Python wrapper for easy integration with Python.
- Daisykit Android - Example app demonstrate how to use Daisykit SDK in Android.
How to install ?
For Windows:
pip3 install daisykit
For Ubuntu:
- Install dependencies
sudo apt install pybind11-dev # Pybind11 - For Python/C++ Wrapper
sudo apt install libopencv-dev # For OpenCV
sudo apt install libvulkan-dev # Optional - For GPU support
- Install DaisyKit (compile from source)
pip3 install --upgrade pip # Ensure pip is updated
pip3 install daisykit
For other platforms:
-
Install OpenCV, Pybind11 and Vulkan development package (if you want GPU support)
-
Install DaisyKit (compile from source)
pip3 install --upgrade pip # Ensure pip is updated
pip3 install daisykit
Examples
Read Documentation.
Note for Python build
Current CD (continuous delivery) flow is partial, which means we only have prebuilt linux wheels for x86_64 and for Windows.
- Prebuilt wheels for linux x86_64 are built with Github actions.
- Windows wheels (64bit) are built manually on a local machine.
- macOS prebuilt wheels are not available for now. However, you can install dependencies (OpenCV, Vulkan) manually, then install Daisykit with pip command.
We will be happy if you can make a pull request to make the CD build fully automated. A good choice is using Github flow for all building tasks.
Current steps for Windows build:
bash ./build_tools/py_windows/build_dk_all_pythons_windows.sh
bash ./build_tools/py_windows/build_dk_python_source_dist.sh
bash ./build_tools/upload_pypi.sh
Bug report
Please open an issue on our official repository if you find any error.
1 maintainer
Metadata
1 maintainer
Release files for daisykit 0.3.0.5
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| daisykit-0.3.0.5.tar.gz | 18.7 MB | Details |
Built distributions (wheels)
Total release size: 1.7 GB
Release files / daisykit-0.3.0.5.tar.gz
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Release files / daisykit-0.3.0.5-cp311-cp311-win_amd64.whl
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Release files / daisykit-0.3.0.5-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
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Release files / daisykit-0.3.0.5-cp311-cp311-macosx_13_0_arm64.whl
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Release files / daisykit-0.3.0.5-cp311-cp311-macosx_11_0_arm64.whl
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Release files / daisykit-0.3.0.5-cp310-cp310-win_amd64.whl
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Release files / daisykit-0.3.0.5-cp39-cp39-win_amd64.whl
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Release files / daisykit-0.3.0.5-cp39-cp39-macosx_13_0_arm64.whl
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Release files / daisykit-0.3.0.5-cp37-cp37m-win_amd64.whl
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/4.0.2 CPython/3.9.17
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Release files / daisykit-0.3.0.5-cp37-cp37m-macosx_10_9_x86_64.whl
| Download URL | daisykit-0.3.0.5-cp37-cp37m-macosx_10_9_x86_64.whl |
|---|---|
| Size | 93.9 MB |
| Tags | CPython 3.7 CPython 3.7 pymalloc macOS 10.9+ x86-64 |
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SHA-256 checksum How to use checksums |
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BLAKE2b-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/4.0.2 CPython/3.9.17
|
Release files / daisykit-0.3.0.5-cp36-cp36m-manylinux_2_17_i686.manylinux2014_i686.whl
| Download URL | daisykit-0.3.0.5-cp36-cp36m-manylinux_2_17_i686.manylinux2014_i686.whl |
|---|---|
| Size | 23.6 MB |
| Tags | CPython 3.6 CPython 3.6 pymalloc Linux glibc 2.17+ x86-32 |
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SHA-256 checksum How to use checksums |
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BLAKE2b-256 checksum How to use checksums |
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| Upload date | |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/4.0.2 CPython/3.9.17
|
Release files / daisykit-0.3.0.5-cp36-cp36m-macosx_10_9_x86_64.whl
| Download URL | daisykit-0.3.0.5-cp36-cp36m-macosx_10_9_x86_64.whl |
|---|---|
| Size | 93.9 MB |
| Tags | CPython 3.6 CPython 3.6 pymalloc macOS 10.9+ x86-64 |
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SHA-256 checksum How to use checksums |
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BLAKE2b-256 checksum How to use checksums |
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| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/4.0.2 CPython/3.9.16
|
