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5. GlossaryΒΆ

JIT function
Shorthand for “a function compiled with Numba using the @jit decorator.”
See loop-jitting.
A feature of compilation in object mode where a loop can be automatically extracted and compiled in nopython mode. This allows functions with operations unsupported in nopython mode (such as array creation) to see significant performance improvements if they contain loops with only nopython-supported operations.
nopython mode
A Numba compilation mode that generates code that does not access the Python C API. This compilation mode produces the highest performance code, but requires that the native types of all values in the function can be inferred, and that no new objects are allocated. Unless otherwise instructed, the @jit decorator will automatically fall back to object mode if nopython mode cannot be used.
object mode
A Numba compilation mode that generates code that handles all values as Python objects and uses the Python C API to perform all operations on those objects. Code compiled in object mode will often run no faster than Python interpreted code, unless the Numba compiler can take advantage of loop-jitting.
type inference
The process by which Numba determines the specialized types of all values within a function being compiled. Type inference can fail if arguments or globals have Python types unknown to Numba, or if functions are used that are not recognized by Numba. Sucessful type inference is a prerequisite for compilation in nopython mode.
A NumPy universal function. Numba can create new compiled ufuncs with the @vectorize decorator.