- A ~5 minute guide to Numba
- Installing using conda on x86/x86_64/POWER Platforms
- Installing using pip on x86/x86_64 Platforms
- Installing on Linux ARMv8 (AArch64) Platforms
- Installing from source
- Dependency List
- Version support information
- Checking your installation
- Compiling Python code with
- Flexible specializations with
- Creating NumPy universal functions
- Compiling Python classes with
- Creating C callbacks with
- Compiling code ahead of time
- Automatic parallelization with
- Using the
- Callback into the Python Interpreter from within JIT’ed code
- Automatic module jitting with
- Performance Tips
- The Threading Layers
- Which threading layers are available?
- Setting the threading layer
- Setting the threading layer selection priority
- Extra notes
- Setting the Number of Threads
- Getting a Thread ID
- 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
- The compiled code is too slow
- Disabling JIT compilation
- Debugging JIT compiled code with GDB
- Using Numba’s direct
- Debugging CUDA Python code
- Frequently Asked Questions
- Does Numba inline functions?
- Does Numba vectorize array computations (SIMD)?
- Why has my loop not vectorized?
- Why are the
typedcontainers 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
- Talks and Tutorials