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title ⚡ raypy - Fast Parallelization for Python Users
description 🚀 Accelerate Python functions in parallel with Raypy, a Rust-powered library that simplifies parallel execution using a simple decorator.

⚡ raypy - Fast Parallelization for Python Users

Download the latest release

🚀 Getting Started

Welcome to raypy, a powerful Rust-based Python module designed for fast CPU parallelization. This guide will help you download and run raypy on your computer, enabling you to optimize your applications effectively.

📂 Key Features

  • High Performance: Leverage Rust’s speed with Python’s ease of use.
  • Easy Integration: Seamless usage within your existing Python projects.
  • Parallel Computation: Utilize multi-core processors for faster calculations.
  • Customizable Functions: Ability to define your own parallelized functions.
  • Support for Async Tasks: Improve efficiency with asynchronous capabilities.

💻 System Requirements

To run raypy, your system should meet the following requirements:

  • Operating System: Windows, macOS, or Linux
  • Python Version: Python 3.6 or higher
  • Memory: At least 4 GB RAM
  • Processor: Multi-core processor recommended

📥 Download & Install

To get started with raypy, you need to download the latest version from our Releases page.

📂 Installation Steps

  1. Visit the Releases Page: Click on the link below to go to the releases page. Visit the Releases Page

  2. Select the Latest Release: Choose the most recent version. The format should indicate the version number, for example, raypy-v1.0.0.zip or raypy-v1.0.0.tar.gz.

  3. Download the File: Click on the download button for your operating system to save the file to your computer.

  4. Extract the Package: Navigate to your downloads folder, right-click the downloaded file, and select "Extract" or "Unzip".

  5. Install the Requirements: Open your command line interface (Terminal or Command Prompt). Navigate to the extracted folder and run:

    pip install -r requirements.txt

  6. Run raypy: After installation, you can use raypy by writing Python scripts that utilize its features. Sample usage could look like:

    from raypy import your_function

result = your_function(data)
print(result)

📊 Example Usage

Here’s a simple example to illustrate how to use raypy in a Python script:

from raypy import parallel_fibonacci

# Calculate the 30th Fibonacci number in parallel
result = parallel_fibonacci(30)
print(f"The 30th Fibonacci number is: {result}")

This script will efficiently calculate the requested Fibonacci number using the parallel capabilities of raypy.

📚 Documentation

For detailed information about the functions and capabilities of raypy, please refer to our comprehensive documentation available at the repo.

  • Function Definitions: Understand how to use each built-in function.
  • Best Practices: Learn about the best ways to implement raypy in your projects.
  • Common Issues: Troubleshoot potential issues that may arise.

🛠️ Support & Contribution

If you encounter problems or have questions, please check the Issues section of our GitHub repository. You can submit a new issue or ask for help there.

If you wish to contribute to raypy, please refer to our Contribution Guidelines in the repository.

📞 Contact

For additional support, you can reach out through the "Contact" section of the repository. We aim to respond promptly to inquiries.

📈 Feedback

Your feedback is valuable. Please let us know your experience with raypy, and how we can make it better.

Thank you for using raypy, where we enhance Python’s capabilities through fast parallel processing!