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2.2. Compilation

2.2.1. JIT functions

@numba.jit([signature, ]*, nopython=False, nogil=False, forceobj=False, locals={})

Compile the decorated function on-the-fly to produce efficient machine code. All parameters all optional.

signature represents the expected Numba Types of function arguments and return value. It can be given in several forms:

  • A tuple of Numba Types arguments (for example (numba.int32, numba.double)) representing the types of the function’s arguments; Numba will then infer an appropriate return type from the arguments.
  • A call signature using Numba Types, specifying both return type and argument types. This can be given in intuitive form (for example numba.void(numba.int32, numba.double)).
  • A string representation of one of the above, for example "void(int32, double)". All type names used in the string are assumed to be defined in the numba.types module.

nopython and nojit are boolean flags. locals is a mapping of local variable names to Numba Types.

This decorator has several modes of operation:

  • If signature is given, a single specialization is compiled corresponding to this signature. Calling the decorated function will then try to convert the arguments to this signature, and raise a TypeError if converting fails. If converting succeeds, the compiled machine code is executed with the converted arguments and the return value is converted back according to the signature.
  • If signature is not given, the decorated function implements multiple dispatch and lazy compilation. Each call to the decorated function will try to re-use an existing specialization if it exists (for example, a call with two integer arguments may re-use a specialization for argument types (numba.int64, numba.int64)). If no suitable specialization exists, a new specialization is compiled on-the-fly, stored for later use, and executed with the converted arguments.

If true, nopython forces the function to be compiled in nopython mode. If not possible, compilation will raise an error.

If true, forceobj forces the function to be compiled in object mode. Since object mode is slower than nopython mode, this is mostly useful for testing purposes.

If true, nogil tries to release the global interpreter lock inside the compiled function. The GIL will only be released if Numba can compile the function in object mode, otherwise a compilation warning will be printed.

The locals dictionary may be used to force the Numba Types of particular local variables, for example if you want to force the use of single precision floats at some point. In general, we recommend you let Numba’s compiler infer the types of local variables by itself.

Not putting any parentheses after the decorator is equivalent to calling the decorator without any arguments, i.e.:

def f(x): ...

is equivalent to:

def f(x): ...

The decorator returns a Dispatcher object.


If no signature is given, compilation errors will be raised when the actual compilation occurs, i.e. when the function is first called with some given argument types.


Compilation can be influenced by some dedicated Environment variables.

class Dispatcher

The class of objects created by calling numba.jit(). You shouldn’t try to create such an object in any other way. Dispatcher objects have the following methods and attributes:


The pure Python function which was compiled.


Print out a listing of the function source code annotated line-by-line with the corresponding Numba IR, and the inferred types of the various variables. If file is specified, printing is done to that file object, otherwise to sys.stdout.


Recompile all existing signatures. This can be useful for example if a global or closure variable was frozen by your function and its value in Python has changed. Since compiling isn’t cheap, this is mainly for testing and interactive use.

2.2.2. Vectorized functions (ufuncs)

@numba.vectorize(signatures, *, identity=None, nopython=True, forceobj=False, locals={})

Compile the decorated function on-the-fly and wrap it as a Numpy ufunc. The optional nopython, forceobj and locals arguments have the same meaning as in numba.jit().

signatures is a mandatory list of signatures expressed in the same form as in the numba.jit() signature argument.

identity is the identity (or unit) value of the function being implemented. Possible values are 0, 1, None, and the string "reorderable". The default is None. Both None and "reorderable" mean the function has no identity value; "reorderable" additionally specifies that reductions along multiple axes can be reordered. (Note that "reorderable" is only supported in Numpy 1.7 or later.)

If there are several signatures, they must be ordered from the more specific to the least specific. Otherwise, Numpy’s type-based dispatching may not work as expected. For example, the following is wrong:

@vectorize(["float64(float64)", "float32(float32)"])
def f(x): ...

as running it over a single-precision array will choose the float64 version of the compiled function, leading to much less efficient execution. The correct invocation is:

@vectorize(["float32(float32)", "float64(float64)"])
def f(x): ...
@numba.guvectorize(signatures, layout, *, identity=None, nopython=True, forceobj=False, locals={})

Generalized version of numba.vectorize(). While numba.vectorize() will produce a simple ufunc whose core functionality (the function you are decorating) operates on scalar operands and returns a scalar value, numba.guvectorize() allows you to create a Numpy ufunc whose core function takes array arguments of various dimensions.

The additional argument layout is a string specifying, in symbolic form, the dimensionality and size relationship of the argument types and return types. For example, a matrix multiplication will have a layout string of "(m,n),(n,p)->(m,p)". Its definition might be (function body omitted):

@guvectorize(["void(float64[:,:], float64[:,:], float64[:,:])"],
def f(a, b, result):
    """Fill-in *result* matrix such as result := a * b"""

If one of the arguments should be a scalar, the corresponding layout specification is () and the argument will really be given to you as a zero-dimension array (you have to dereference it to get the scalar value). For example, a one-dimension moving average with a parameterable window width may have a layout string of "(n),()->(n)".

Note that any output will be given to you preallocated as an additional function argument: your code has to fill it with the appropriate values for the function you are implementing.

See also

Specification of the layout string as supported by Numpy. Note that Numpy uses the term “signature”, which we unfortunately use for something else.