numba

Compile Python and NumPy code into machine code with JIT compilation.

19|2|Updated Jan 31, 2026

Search Tags:#performance #optimization #gpu #jit #parallelism #numba

Ready to run

npx skills add https://github.com/tondevrel/scientific-agent-skills --skill numba

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: numba
Source: https://github.com/tondevrel/scientific-agent-skills/tree/main/skills/numba
Command: npx skills add https://github.com/tondevrel/scientific-agent-skills --skill numba

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill dramatically speeds up computationally intensive Python code, especially numerical algorithms and loops, by compiling them into efficient machine code.

Core Features & Use Cases

Just-In-Time (JIT) Compilation: Compiles Python functions to native machine code on the fly.
Nopython Mode (@njit): Ensures maximum performance by avoiding Python interpreter overhead.
Parallelism (prange): Automatically parallelizes loops across multiple CPU cores.
GPU Acceleration (cuda.jit): Enables execution of code on NVIDIA GPUs.
Use Case: Optimize a complex scientific simulation or a deep learning data preprocessing step that is currently bottlenecked by slow Python loops.

Quick Start

Use the numba skill to apply the @njit decorator to the provided Python function 'calculate_stats'.

Frequently Asked Questions about numba

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I speed up Python loops and numerical algorithms without rewriting them in C?▼

To speed up Python numerical algorithms, you can use Just-In-Time (JIT) compilation to translate Python and NumPy code into fast machine code on the fly. This avoids the need to rewrite your logic in a lower-level language like C.

How does JIT compilation work with NumPy code to achieve high-performance computing?▼

JIT compilation works by compiling a subset of Python and NumPy code into native machine code just before execution. This process bypasses the Python interpreter overhead, enabling high-performance execution for computationally intensive tasks.

Can I use parallel execution to optimize numerical algorithms across multiple CPU cores?▼

Yes, you can optimize numerical algorithms across multiple CPU cores using automatic parallelism. By utilizing parallel execution loops, the JIT compiler distributes loop iterations across available CPU cores for faster processing.

Does JIT compilation support GPU acceleration for Python code?▼

Yes, JIT compilation supports GPU acceleration for Python code. You can execute computationally intensive functions directly on NVIDIA GPUs using specific GPU JIT decorators to achieve massive parallel processing performance.

What is the best way to ensure maximum performance when optimizing Python code with JIT?▼

The best way to ensure maximum performance is to use nopython mode during JIT compilation. This mode completely avoids Python interpreter overhead by forcing the compiler to generate purely native machine code, ensuring peak execution speed.

When should I not use JIT compilation for my Python code?▼

You should not use JIT compilation for code that relies heavily on unsupported Python features or external libraries outside the NumPy subset. The compiler can only optimize a specific subset of Python, meaning non-numerical or dynamic workloads may fail to compile or see no performance gain.