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Cosmin Ștefan Ciocan
106 lines
3.8 KiB
Markdown
106 lines
3.8 KiB
Markdown
# nvcc4jupyter: CUDA C++ plugin for Jupyter Notebook
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| Testing | ![Python Versions][python-version] [![CI - Test][test-badge]][test-workflow] [![Coverage][coverage-badge]][coverage-results] |
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| Code Quality | [![Code style: black][black-badge]][black-project] [![security: bandit][bandit-badge]][bandit-project]|
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| Package | [![PyPI Latest Release][pypi-latest-version]][pypi-project-url] [![PyPI Downloads][pypi-downloads]][pypi-project-url] |
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<!-- Testing badges -->
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[python-version]: https://img.shields.io/pypi/pyversions/nvcc4jupyter
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[test-badge]: https://github.com/andreinechaev/nvcc4jupyter/actions/workflows/test.yml/badge.svg
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[test-workflow]: https://github.com/andreinechaev/nvcc4jupyter/actions/workflows/test.yml
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[coverage-badge]: https://codecov.io/github/andreinechaev/nvcc4jupyter/coverage.svg?branch=master
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[coverage-results]: https://codecov.io/gh/andreinechaev/nvcc4jupyter
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<!-- Code Quality badges -->
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[black-badge]: https://img.shields.io/badge/code%20style-black-000000.svg
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[black-project]: https://github.com/ambv/black
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[bandit-badge]: https://img.shields.io/badge/security-bandit-yellow.svg
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[bandit-project]: https://github.com/PyCQA/bandit
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<!-- Package badges -->
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[pypi-project-url]: https://pypi.org/project/nvcc4jupyter/
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[pypi-latest-version]: https://img.shields.io/pypi/v/nvcc4jupyter.svg
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[pypi-downloads]: https://img.shields.io/pypi/dm/nvcc4jupyter.svg?label=PyPI%20downloads
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**nvcc4jupyter** is a Jupyter Notebook plugin that provides cell and line
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[magics](https://ipython.readthedocs.io/en/stable/interactive/magics.html)
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to allow running CUDA C++ code from a notebook. This is especially
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useful when combined with a hosted service such a Google's
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[Colab](https://colab.research.google.com/) which provide CUDA capable GPUs
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and you can start learning CUDA C++ without having to install anything or even
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to own a GPU yourself.
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## Table of Contents
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- [Main Features](#main-features)
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- [Install](#install)
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- [Usage](#usage)
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- [License](#license)
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- [Documentation](#documentation)
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- [Contributing](#contributing)
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## Main Features
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Here are just a few of the things that nvcc4jupyter does well:
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- [Easily run CUDA C++ code](https://nvcc4jupyter.readthedocs.io/en/latest/usage.html#hello-world)
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- [Profile your code with NVIDIA Nsight Compute or Nsight Systems](https://nvcc4jupyter.readthedocs.io/en/latest/usage.html#profiling)
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- [Compile your code with external libraries (e.g. OpenCV)](https://nvcc4jupyter.readthedocs.io/en/latest/notebooks.html#compiling-with-external-libraries)
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- [Share code between different programs in the same notebook / split your code into multiple files for improved readability](https://nvcc4jupyter.readthedocs.io/en/latest/usage.html#groups)
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## Install
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The installer for the latest released version is available at the [Python
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Package Index (PyPI)](https://pypi.org/project/nvcc4jupyter).
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```sh
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pip install nvcc4jupyter
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```
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## Usage
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First, load the extension to enable the magic commands:
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```
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%load_ext nvcc4jupyter
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```
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Running a quick CUDA Hello World program:
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```c++
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%%cuda
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#include <stdio.h>
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__global__ void hello(){
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printf("Hello from block: %u, thread: %u\n", blockIdx.x, threadIdx.x);
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}
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int main(){
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hello<<<2, 2>>>();
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cudaDeviceSynchronize();
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}
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```
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For more advanced use cases, see [the documentation](https://nvcc4jupyter.readthedocs.io/en/latest/usage.html).
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## Documentation
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The official documentation is hosted on [readthedocs](https://nvcc4jupyter.readthedocs.io/).
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## License
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[MIT](LICENSE)
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## Contributing
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The recommended setup for development is using the devcontainer in GitHub
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Codespaces or locally in VSCode.
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If not using the devcontainer you need to install the package with the
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development dependencies and install the pre-commit hook before commiting any
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changes:
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```bash
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pip install -e .[dev]
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pre-commit install
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```
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<hr>
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[Go to Top](#table-of-contents)
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