User Manual
- A ~5 minute guide to Numba
- Overview
- Installation
- Compiling Python code with
@jit
- Creating NumPy universal functions
- Compiling Python classes with
@jitclass
- Creating C callbacks with
@cfunc
- Compiling code ahead of time
- Automatic parallelization with
@jit
- Using the
@stencil
decorator - Callback into the Python Interpreter from within JIT’ed code
- Automatic module jitting with
jit_module
- Performance Tips
- The Threading Layers
- Command line interface
- Troubleshooting and tips
- What to compile
- My code doesn’t compile
- My code has a type unification problem
- My code has an untyped list problem
- Object mode or
@jit(forceobj=True)
is too slow - Disabling JIT compilation
- Debugging JIT compiled code with GDB
- Using Numba’s direct
gdb
bindings innopython
mode - Debugging CUDA Python code
- Frequently Asked Questions
- Installation
- Programming
- Performance
- Does Numba inline functions?
- Does Numba vectorize array computations (SIMD)?
- Why has my loop not vectorized?
- Why are the
typed
containers slower when used from the interpreter? - Does Numba automatically parallelize code?
- Can Numba speed up short-running functions?
- There is a delay when JIT-compiling a complicated function, how can I improve it?
- GPU Programming
- Integration with other utilities
- Miscellaneous
- Examples
- Talks and Tutorials