Frequently Asked Questions


Numba could not be imported

If you are seeing an exception on importing Numba with an error message that starts with:

ImportError: Numba could not be imported.

here are some common issues and things to try to fix it.

  1. Your installation has more than one version of Numba a given environment.

    Common ways this occurs include:

    • Installing Numba with conda and then installing again with pip.

    • Installing Numba with pip and then updating to a new version with pip (pip re-installations don’t seem to always clean up very well).

    To fix this the best approach is to create an entirely new environment and install a single version of Numba in that environment using a package manager of your choice.

  2. Your installation has Numba for Python version X but you are running with Python version Y.

    This occurs due to a variety of Python environment mix-up/mismatch problems. The most common mismatch comes from installing Numba into the site-packages/environment of one version of Python by using a base or system installation of Python that is a different version, this typically happens through the use of the “wrong” pip binary. This will obviously cause problems as the C-Extensions on which Numba relies are bound to specific Python versions. A way to check if this likely the problem is to see if the path to the python binary at:

    python -c 'import sys; print(sys.executable)'

    matches the path to your installation tool and/or matches the reported installation location and if the Python versions match up across all of these. Note that Python version X.Y.A is compatible with X.Y.B.

    To fix this the best approach is to create an entirely new environment and ensure that the installation tool used to install Numba is the one from that environment/the Python versions at install and run time match.

  3. Your core system libraries are too old.

    This is a somewhat rare occurrence, but there are occasions when a very old (typically out of support) version of Linux is in use it doesn’t have a glibc library with sufficiently new versioned symbols for Numba’s shared libraries to resolve against. The fix for this is to update your OS system libraries/update your OS.

  4. You are using an IDE e.g. Spyder.

    There are some unknown issues in relation to installing Numba via IDEs, but it would appear that these are likely variations of 1. or 2. with the same suggested fixes. Also, try installation from outside of the IDE with the command line.

If you have an installation problem which is not one of the above problems, please do ask on and if possible include the path where Numba is installed and also the output of:

python -c 'import sys; print(sys.executable)'


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

As of Numba 0.39, you can, so long as the function argument has also been JIT-compiled:

def f(g, x):
    return g(x) + g(-x)

result = f(jitted_g_function, 1)

However, dispatching with arguments that are functions has extra overhead. If this matters for your application, you can also use a factory function to capture the function argument in a closure:

def make_f(g):
    # Note: a new f() is created each time make_f() is called!
    def f(x):
        return g(x) + g(-x)
    return f

f = make_f(jitted_g_function)
result = f(1)

Improving the dispatch performance of functions in Numba is an ongoing task.

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.

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.

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

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.

How can I tell if parallel=True worked?

If the parallel=True transformations failed for a function decorated as such, a warning will be displayed. See also Diagnostics for information about parallel diagnostics.


Does Numba inline functions?

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

Does Numba vectorize array computations (SIMD)?

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

Why has my loop 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:


Using --debug-only requires LLVM to be build with assertions enabled to work. Use the build of llvmlite in the Numba channel which is linked against LLVM with assertions enabled.

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.

Why are the typed containers slower when used from the interpreter?

The Numba typed containers found in numba.typed e.g. numba.typed.List store their data in an efficient form for access from JIT compiled code. When these containers are used from the CPython interpreter, the data involved has to be converted from/to the container format. This process is relatively costly and as a result impacts performance. In JIT compiled code no such penalty exists and so operations on the containers are much quicker and often faster than the pure Python equivalent.

Does Numba automatically parallelize code?

It can, in some cases:

  • Ufuncs and gufuncs with the target="parallel" option will run on multiple threads.

  • The 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.

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.

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.

GPU Programming

How do I work around the CUDA initialized before forking error?

On Linux, the multiprocessing module in the Python standard library defaults to using the fork method for creating new processes. Because of the way process forking duplicates state between the parent and child processes, CUDA will not work correctly in the child process if the CUDA runtime was initialized prior to the fork. Numba detects this and raises a CudaDriverError with the message CUDA initialized before forking.

One approach to avoid this error is to make all calls to numba.cuda functions inside the child processes or after the process pool is created. However, this is not always possible, as you might want to query the number of available GPUs before starting the process pool. In Python 3, you can change the process start method, as described in the multiprocessing documentation. Switching from fork to spawn or forkserver will avoid the CUDA initialization issue, although the child processes will not inherit any global variables from their parent.

Integration with other utilities

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

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.

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')

How do I get Numba development builds?

Pre-release versions of Numba can be installed with conda:

$ conda install -c numba/label/dev numba


Where does the project name “Numba” come from?

“Numba” is a combination of “NumPy” and “Mamba”. Mambas are some of the fastest snakes in the world, and Numba makes your Python code fast.

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. You can also find the sources on github, including a pre-print pdf, in case you don’t have access to the ACM site but would like to read the paper.

How do I write a minimal working reproducer for a problem with Numba?

A minimal working reproducer for Numba should include:

  1. The source code of the function(s) that reproduce the problem.

  2. Some example data and a demonstration of calling the reproducing code with that data. As Numba compiles based on type information, unless your problem is numerical, it’s fine to just provide dummy data of the right type, e.g. use numpy.ones of the correct dtype/size/shape for arrays.

  3. Ideally put 1. and 2. into a script with all the correct imports. Make sure your script actually executes and reproduces the problem before submitting it! The target is to make it so that the script can just be copied directly from the issue tracker and run by someone else such that they can see the same problem as you are having.

Having made a reproducer, now remove every part of the code that does not contribute directly to reproducing the problem to create a “minimal” reproducer. This means removing imports that aren’t used, removing variables that aren’t used or have no effect, removing lines of code which have no effect, reducing the complexity of expressions, and shrinking input data to the minimal amount required to trigger the problem.

Doing the above really helps out the Numba issue triage process and will enable a faster response to your problem!

Suggested further reading on writing minimal working reproducers.