User Manual

• A ~5 minute guide to Numba
  • How do I get it?
  • Will Numba work for my code?
  • What is nopython mode?
  • How to measure the performance of Numba?
  • How fast is it?
  • How does Numba work?
  • Other things of interest:
    • GPU targets:
• Overview
• Installation
  • Compatibility
  • Installing using conda on x86/x86_64/POWER Platforms
  • Installing using pip on x86/x86_64 Platforms
  • Enabling AMD ROCm GPU Support
  • Installing on Linux ARMv7 Platforms
  • Installing on Linux ARMv8 (AArch64) Platforms
  • Installing from source
    • Build time environment variables and configuration of optional components
  • Dependency List
  • Checking your installation
• Compiling Python code with @jit
  • Basic usage
    • Lazy compilation
    • Eager compilation
  • Calling and inlining other functions
  • Signature specifications
  • Compilation options
    • nopython
    • nogil
    • cache
    • parallel
• Flexible specializations with @generated_jit
  • Example
  • Compilation options
• Creating NumPy universal functions
  • The @vectorize decorator
  • The @guvectorize decorator
    • Overwriting input values
  • Dynamic universal functions
• Compiling Python classes with @jitclass
  • Basic usage
  • Inferred class member types from type annotations with as_numba_type
  • Specifying numba.typed containers as class members explicitly
  • Support operations
  • Limitations
  • The decorator: @jitclass
• Creating C callbacks with @cfunc
  • Basic usage
  • Example
  • Dealing with pointers and array memory
  • Handling C structures
    • With CFFI
    • With numba.types.Record.make_c_struct
    • Full example
  • Signature specification
  • Compilation options
• Compiling code ahead of time
  • Overview
    • Benefits
    • Limitations
  • Usage
    • Standalone example
    • Distutils integration
    • Signature syntax
• Automatic parallelization with @jit
  • Supported Operations
  • Explicit Parallel Loops
  • Examples
  • Diagnostics
    • The parallel diagnostics report sections
• Using the @stencil decorator
  • Basic usage
  • Stencil Parameters
  • Kernel shape inference and border handling
  • Stencil decorator options
    • neighborhood
    • func_or_mode
    • cval
    • standard_indexing
  • StencilFunc
  • Stencil invocation options
    • out
• Callback into the Python Interpreter from within JIT’ed code
  • The objmode context-manager
• Automatic module jitting with jit_module
  • Example usage
  • API
• Performance Tips
  • No Python mode vs Object mode
  • Loops
  • Fastmath
  • Parallel=True
  • Intel SVML
  • Linear algebra
• The Threading Layers
  • Which threading layers are available?
  • Setting the threading layer
    • Selecting a threading layer for safe parallel execution
    • Selecting a named threading layer
  • Extra notes
  • Setting the Number of Threads
    • Example of Limiting the Number of Threads
    • API Reference
• Command line interface
  • Usage
  • Help
  • System information
  • Debugging
• 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
    • Example debug usage
    • Globally override debug setting
  • Using Numba’s direct gdb bindings in nopython mode
    • Set up
    • Basic gdb support
    • Running with gdb enabled
    • Adding breakpoints to code
    • Debugging in parallel regions
    • Using the gdb command language
    • How does the gdb binding work?
  • Debugging CUDA Python code
    • Using the simulator
    • Debug Info
• Frequently Asked Questions
  • Programming
    • Can I pass a function as an argument to a jitted function?
    • Numba doesn’t seem to care when I modify a global variable
    • Can I debug a jitted function?
    • How can I create a Fortran-ordered array?
    • How can I increase integer width?
    • How can I tell if parallel=True worked?
  • Performance
    • Does Numba inline functions?
    • Does Numba vectorize array computations (SIMD)?
    • Why my loop is not vectorized?
    • 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
    • How do I work around the CUDA intialized before forking error?
  • Integration with other utilities
    • Can I “freeze” an application which uses Numba?
    • I get errors when running a script twice under Spyder
    • Why does Numba complain about the current locale?
    • How do I get Numba development builds?
  • Miscellaneous
    • Where does the project name “Numba” come from?
    • How do I reference/cite/acknowledge Numba in other work?
      • Other related papers
    • How do I write a minimal working reproducer for a problem with Numba?
• Examples
  • Mandelbrot
  • Moving average
  • Multi-threading
• Talks and Tutorials
  • Talks on Numba
  • Talks on Applications of Numba
  • Tutorials