Compiling Python code with @jit

Numba provides several utilities for code generation, but its central feature is the numba.jit() decorator. Using this decorator, you can mark a function for optimization by Numba’s JIT compiler. Various invocation modes trigger differing compilation options and behaviours.

Basic usage

Lazy compilation

The recommended way to use the @jit decorator is to let Numba decide when and how to optimize:

from numba import jit

def f(x, y):
    # A somewhat trivial example
    return x + y

In this mode, compilation will be deferred until the first function execution. Numba will infer the argument types at call time, and generate optimized code based on this information. Numba will also be able to compile separate specializations depending on the input types. For example, calling the f() function above with integer or complex numbers will generate different code paths:

>>> f(1, 2)
>>> f(1j, 2)

Eager compilation

You can also tell Numba the function signature you are expecting. The function f() would now look like:

from numba import jit, int32

@jit(int32(int32, int32))
def f(x, y):
    # A somewhat trivial example
    return x + y

int32(int32, int32) is the function’s signature. In this case, the corresponding specialization will be compiled by the @jit decorator, and no other specialization will be allowed. This is useful if you want fine-grained control over types chosen by the compiler (for example, to use single-precision floats).

If you omit the return type, e.g. by writing (int32, int32) instead of int32(int32, int32), Numba will try to infer it for you. Function signatures can also be strings, and you can pass several of them as a list; see the numba.jit() documentation for more details.

Of course, the compiled function gives the expected results:

>>> f(1,2)

and if we specified int32 as return type, the higher-order bits get discarded:

>>> f(2**31, 2**31 + 1)

Calling and inlining other functions

Numba-compiled functions can call other compiled functions. The function calls may even be inlined in the native code, depending on optimizer heuristics. For example:

def square(x):
    return x ** 2

def hypot(x, y):
    return math.sqrt(square(x) + square(y))

The @jit decorator must be added to any such library function, otherwise Numba may generate much slower code.

Signature specifications

Explicit @jit signatures can use a number of types. Here are some common ones:

  • void is the return type of functions returning nothing (which actually return None when called from Python)

  • intp and uintp are pointer-sized integers (signed and unsigned, respectively)

  • intc and uintc are equivalent to C int and unsigned int integer types

  • int8, uint8, int16, uint16, int32, uint32, int64, uint64 are fixed-width integers of the corresponding bit width (signed and unsigned)

  • float32 and float64 are single- and double-precision floating-point numbers, respectively

  • complex64 and complex128 are single- and double-precision complex numbers, respectively

  • array types can be specified by indexing any numeric type, e.g. float32[:] for a one-dimensional single-precision array or int8[:,:] for a two-dimensional array of 8-bit integers.

Compilation options

A number of keyword-only arguments can be passed to the @jit decorator.


Numba has two compilation modes: nopython mode and object mode. Nopython mode is the default and it produces much faster code, but has limitations.

@jit  # same as @jit(nopython=True) or @njit since Numba 0.59
def f(x, y):
    return x + y


Whenever Numba optimizes Python code to native code that only works on native types and variables (rather than Python objects), it is not necessary anymore to hold Python’s global interpreter lock (GIL). Numba will release the GIL when entering such a compiled function if you passed nogil=True.

def f(x, y):
    return x + y

Code running with the GIL released runs concurrently with other threads executing Python or Numba code (either the same compiled function, or another one), allowing you to take advantage of multi-core systems. This will not be possible if the function is compiled in object mode.

When using nogil=True, you’ll have to be wary of the usual pitfalls of multi-threaded programming (consistency, synchronization, race conditions, etc.).


To avoid compilation times each time you invoke a Python program, you can instruct Numba to write the result of function compilation into a file-based cache. This is done by passing cache=True:

def f(x, y):
    return x + y


Caching of compiled functions has several known limitations:

  • The caching of compiled functions is not performed on a function-by-function basis. The cached function is the the main jit function, and all secondary functions (those called by the main function) are incorporated in the cache of the main function.

  • Cache invalidation fails to recognize changes in functions defined in a different file. This means that when a main jit function calls functions that were imported from a different module, a change in those other modules will not be detected and the cache will not be updated. This carries the risk that “old” function code might be used in the calculations.

  • Global variables are treated as constants. The cache will remember the value of the global variable at compilation time. On cache load, the cached function will not rebind to the new value of the global variable.


Enables automatic parallelization (and related optimizations) for those operations in the function known to have parallel semantics. For a list of supported operations, see Automatic parallelization with @jit. This feature is enabled by passing parallel=True and must be used in conjunction with nopython=True:

@jit(nopython=True, parallel=True)
def f(x, y):
    return x + y