Deviations from Python Semantics
By default, instead of causing an
IndexError, accessing an
out-of-bound index of an array in a Numba-compiled function will return
invalid values or lead to an access violation error (it’s reading from
invalid memory locations). Bounds checking can be enabled on a specific
function via the boundscheck
option of the jit decorator. Additionally, the
can be set to 0 or 1 to globally override this flag.
Bounds checking will slow down typical functions so it is recommended to only use this flag for debugging purposes.
Exceptions and Memory Allocation
Due to limitations in the current compiler when handling exceptions, memory allocated (almost always NumPy arrays) within a function that raises an exception will leak. This is a known issue that will be fixed, but in the meantime, it is best to do memory allocation outside of functions that can also raise exceptions.
While Python has arbitrary-sized integers, integers in Numba-compiled functions get a fixed size through type inference (usually, the size of a machine integer). This means that arithmetic operations can wrapround or produce undefined results or overflow.
Type inference can be overridden by an explicit type specification, if fine-grained control of integer width is desired.
Calling the bitwise complement operator (the
~ operator) on a Python
boolean returns an integer, while the same operator on a Numpy boolean
returns another boolean:
>>> ~True -2 >>> ~np.bool_(True) False
Numba follows the Numpy semantics.
Global and closure variables
In nopython mode, global and closure variables are frozen by Numba: a Numba-compiled function sees the value of those variables at the time the function was compiled. Also, it is not possible to change their values from the function.
Numba may or may not copy global variables referenced inside a compiled function. Small global arrays are copied for potential compiler optimization with immutability assumption. However, large global arrays are not copied to conserve memory. The definition of “small” and “large” may change.
Zero initialization of variables
Numba does not track variable liveness at runtime. For simplicity of implementation, all variables are zero-initialized. Example:
from numba import njit @njit def foo(): for i in range(0): pass print(i) # will print 0 and not raise UnboundLocalError foo()