1.13. Frequently Asked Questions¶

1.13.1.1. 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)

1.13.1.2. 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.

1.13.1.3. 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.

1.13.1.4. How can I create a Fortran-ordered array?¶

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

1.13.1.5. How can I increase integer width?¶

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.

1.13.1.6. How can I tell if parallel=True worked?¶

Set the environment variable NUMBA_WARNINGS to non-zero and if the parallel=True transformations failed for a function decorated as such, a warning will be displayed.

Also, setting the environment variable NUMBA_DEBUG_ARRAY_OPT_STATS will show some statistics about which operators/calls are converted to parallel for-loops.

1.13.2.1. Does Numba inline functions?¶

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

1.13.2.2. Does Numba vectorize array computations (SIMD)?¶

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

1.13.2.3. Why my loop is not vectorized?¶

Numba enables the loop-vectorize optimization in LLVM by default. While it is a powerful optimization, not all loops are applicable. Sometimes, loop-vectorization may fail due to subtle details like memory access pattern. To see additional diagnostic information from LLVM, add the following lines:

import llvmlite.binding as llvm
llvm.set_option('', '--debug-only=loop-vectorize')

This tells LLVM to print debug information from the loop-vectorize pass to stderr. Each function entry looks like:

LV: Checking a loop in "<low-level symbol name>" from <function name>
LV: Loop hints: force=? width=0 unroll=0
...
LV: Vectorization is possible but not beneficial.
LV: Interleaving is not beneficial.

Each function entry is separated by an empty line. The reason for rejecting the vectorization is usually at the end of the entry. In the example above, LLVM rejected the vectorization because doing so will not speedup the loop. In this case, it can be due to memory access pattern. For instance, the array being looped over may not be in contiguous layout.

When memory access pattern is non-trivial such that it cannot determine the access memory region, LLVM may reject with the following message:

LV: Can't vectorize due to memory conflicts

Another common reason is:

LV: Not vectorizing: loop did not meet vectorization requirements.

In this case, vectorization is rejected because the vectorized code may behave differently. This is a case to try turning on fastmath=True to allow fastmath instructions.

1.13.2.4. Does Numba automatically parallelize code?¶

It can, in some cases:

• Ufuncs and gufuncs with the target="parallel" option will run on multiple threads.
• The experimental parallel=True option to @jit will attempt to optimize array operations and run them in parallel. It also adds support for prange() to explicitly parallelize a loop.

You can also manually run computations on multiple threads yourself and use the nogil=True option (see releasing the GIL). Numba can also target parallel execution on GPU architectures using its CUDA and HSA backends.

1.13.2.5. 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.

1.13.2.6. 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.

1.13.3.1. 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.

1.13.3.2. 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.

1.13.3.3. Why does Numba complain about the current locale?¶

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')

1.13.4.1. How do I reference/cite/acknowledge Numba in other work?¶

For academic use, the best option is to cite our ACM Proceedings: Numba: a LLVM-based Python JIT compiler.