You can’t, but in many cases you can use a closure to emulate it. For example, this example:
@jit(nopython=True) def f(g, x): return g(x) + g(-x) result = f(my_g_function, 1)
could be rewritten using a factory function:
def make_f(g): # Note: a new f() is compiled each time make_f() is called! @jit(nopython=True) def f(x): return g(x) + g(-x) return f f = make_f(my_g_function) result = f(1)
Numba considers global variables as compile-time constants. If you want
your jitted function to update itself when you have modified a global
variable’s value, one solution is to recompile it using the
recompile() method. This is a relatively slow operation,
though, so you may instead decide to rearchitect your code and turn the
global variable into a function argument.
pdb or other such high-level facilities is currently not
supported from Numba-compiled code. However, you can temporarily disable
compilation by setting the
Numba currently doesn’t support the
order argument to most Numpy
functions such as
numpy.empty() (because of limitations in the
type inference algorithm). You can work around this issue by
creating a C-ordered array and then transposing it. For example:
a = np.empty((3, 5), order='F') b = np.zeros(some_shape, order='F')
can be rewritten as:
a = np.empty((5, 3)).T b = np.zeros(some_shape[::-1]).T
By default, Numba will generally use machine integer width for integer
variables. On a 32-bit machine, you may sometimes need the magnitude of
64-bit integers instead. You can simply initialize relevant variables as
np.int64 (for example
np.int64(0) instead of
0). It will
propagate to all computations involving those variables.
Numba gives enough information to LLVM so that functions short enough can be inlined. This only works in nopython mode.
Numba doesn’t implement such optimizations by itself, but it lets LLVM apply them.
No, it doesn’t. If you want to run computations concurrently on multiple threads (by releasing the GIL) or processes, you’ll have to handle the pooling and synchronisation yourself.
Or, you can take a look at NumbaPro.
Not significantly. New users sometimes expect to JIT-compile such functions:
def f(x, y): return x + y
and get a significant speedup over the Python interpreter. But there isn’t much Numba can improve here: most of the time is probably spent in CPython’s function call mechanism, rather than the function itself. As a rule of thumb, if a function takes less than 10 µs to execute: leave it.
The exception is that you should JIT-compile that function if it is called from another jitted function.
If you’re using PyInstaller or a similar utility to freeze an application,
you may encounter issues with llvmlite. llvmlite needs a non-Python DLL
for its working, but it won’t be automatically detected by freezing utilities.
You have to inform the freezing utility of the DLL’s location: it will
usually be named
llvmlite/binding/llvmlite.dll, depending on your system.
When you run a script in a console under Spyder, Spyder first tries to
reload existing modules. This doesn’t work well with Numba, and can
produce errors like
TypeError: No matching definition for argument type(s).
There is a fix in the Spyder preferences. Open the “Preferences” window,
select “Console”, then “Advanced Settings”, click the “Set UMR excluded
modules” button, and add
numba inside the text box that pops up.
To see the setting take effect, be sure to restart the IPython console or kernel.
If you get an error message such as the following:
RuntimeError: Failed at nopython (nopython mode backend) LLVM will produce incorrect floating-point code in the current locale
it means you have hit a LLVM bug which causes incorrect handling of floating-point constants. This is known to happen with certain third-party libraries such as the Qt backend to matplotlib.
To work around the bug, you need to force back the locale to its default value, for example:
import locale locale.setlocale(locale.LC_NUMERIC, 'C')