1.8. Frequently Asked Questions

1.8.1. Programming

1.8.1.1. Can I pass a function as an argument to a jitted function?

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)

1.8.1.2. Numba doesn’t seem to care when I modify a global variable

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.

1.8.1.3. Can I debug a jitted function?

Calling into 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_DISABLE_JIT environment variable.

1.8.2. Performance

1.8.2.1. Does Numba inline functions?

Numba gives enough information to LLVM so that functions short enough can be inlined. This only works in nopython mode.

1.8.2.2. Does Numba vectorize array computations (SIMD)?

Numba doesn’t implement such optimizations by itself, but it lets LLVM apply them.

1.8.2.3. Does Numba parallelize code?

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.

1.8.2.4. Can Numba speed up short-running functions?

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.

1.8.2.5. There is a delay when JIT-compiling a complicated function, how can I improve it?

Try to pass cache=True to the @jit decorator. It will keep the compiled version on disk for later use.

A more radical alternative is ahead-of-time compilation.

1.8.3. Integration with other utilities

1.8.3.1. Can I “freeze” an application which uses Numba?

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/libllvmlite.so or llvmlite/binding/llvmlite.dll, depending on your system.

1.8.3.2. I get errors when running a script twice under Spyder

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.