turbo-ml 0.1.17

High-performance ML primitives powered by Rust

turbo_ml 🚀

turbo_ml is an experimental high-performance Python library backed by Rust, designed to accelerate selected numerical and ML-related operations compared to pure Python implementations.

⚠️ Important: This project is currently under active development.
It is not guaranteed to work for every Python use case or environment.

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📌 Project Status (Read This First)

• 🧪 Development stage: Early / Experimental
• ⚡ Focus: Performance-critical operations
• 🔧 Backend: Rust (via PyO3)
• 🛑 Not a drop-in replacement for all ML libraries

If you are looking for a fully mature ML framework, this may not yet be suitable for production use.

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✅ Supported Python Versions (Guaranteed)

Fully supported and tested:

• Python 3.8
• Python 3.9
• Python 3.10
• Python 3.11
• Python 3.12

✅ If py --list shows Python 3.12, installation is guaranteed to work.

❌ Not supported yet( Force Install is Supported ):

• Python 3.13
• Python 3.14+

This limitation exists due to upstream Rust–Python bindings (PyO3).
Support will be added once upstream compatibility is stable.

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📦 Installation

Standard Installation (Recommended)

pip install turbo_ml

If you are using Windows, Linux, or macOS with Python 3.8–3.12, this will install a prebuilt binary wheel.
You do NOT need Rust or a C/C++ compiler.

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Special Installation ( If facing any error to install )

export PYO3_USE_ABI3_FORWARD_COMPATIBILITY=1
pip install turbo-ml

If you are using Windows, Linux, or macOS with Python 3.13 & 3.14.

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🔍 Verifying Installation

After installation, verify with:

python -c "import turbo_ml; print('turbo_ml installed successfully')"

If no error appears, installation is complete.

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▶️ How to Run the Example Scripts

This repository includes two test files to compare performance.

1️⃣ base_python_test.py

A normal Python script without any acceleration.

Run:

python base_python_test.py

Purpose:

• Acts as a baseline
• Uses standard Python logic
• Slower execution for heavy computation

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2️⃣ turbo_test.py

Uses the turbo_ml library.

Run:

python turbo_test.py

Purpose:

• Imports turbo_ml
• Executes the same logic using Rust-accelerated functions
• Expected to run faster for supported operations

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⚠️ Common Errors and How to Fix Them

❌ Error: Python version not supported

Error message example:

ERROR: turbo_ml requires Python < 3.13

Cause:
You are using Python 3.13 or newer.

Fix:

1. Install Python 3.12
2. Create a virtual environment using it:

   py -3.12 -m venv venv
   

3. Activate the environment and reinstall:

   pip install turbo_ml
   

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❌ Error: pip tries to build from source (Rust / MSVC errors)

Cause:

• Unsupported Python version
• pip cache using an old build
• Wheel not selected

Fix:

pip uninstall turbo_ml -y
pip cache purge
pip install turbo_ml --no-cache-dir

If it still tries to compile, check:

python --version

Ensure it is ≤ 3.12.

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❌ Error: ModuleNotFoundError: turbo_ml

Cause:

• Installed in a different Python environment
• Virtual environment not activated

Fix:

which python
pip show turbo_ml

Ensure both point to the same environment.

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🧪 Experimental Nature (Caution)

• This library does not accelerate arbitrary Python code
• Only specific operations are optimized
• Performance gains depend on:
  • Data size
  • Operation type
  • System architecture

You may observe:

• No speedup for small inputs
• Different behavior compared to pure Python
• Missing features (for now)

This is expected during early development.

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🛠 Development Disclaimer

• APIs may change without notice
• Performance claims may evolve
• Backward compatibility is not guaranteed yet

If something breaks, it is likely a known limitation, not a user mistake.

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📬 Feedback & Contributions

This project is actively evolving.

• Bug reports are welcome
• Performance benchmarks are appreciated
• Contributions should focus on measurable speedups

Repository: https://github.com/Dhakshin2007/turbo_ml

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🧠 Final Note

turbo_ml is an exploration of what’s possible when Python and Rust work together.

Use it to:

• Learn
• Experiment
• Benchmark
• Push performance boundaries

Not (yet) to:

• Replace full ML frameworks
• Assume universal compatibility

Thank you for testing an early-stage project.