Common Issues and Pitfalls

This section attempts to document common issues and pitfalls that users need to be aware of when trying to freeze their applications with PyInstaller, as certain features require special care and considerations that might not be obvious when developing and running unfrozen python programs.

Launching External Programs from the Frozen Application

In a PyInstaller-frozen application, the run-time environment of the application’s process is often modified to ensure that when it comes to loading of the shared libraries, the bundled copies of shared libraries are preferred over the copies that might be available on the target system. The exact way of modifying the library search order (environment variables versus low-level API) depends on the operating system, but in general, changes made to the frozen application’s run-time environment are also inherited by subprocesses launched by the frozen application. This ensures that the application itself (for example, the binary python extensions it loads) as well as bundled helper programs that the application might run as a subprocess (for example, gspawn when using GLib/Gio via gi.repository on Windows, QtWebEngineHelper from PyQt and PySide packages, and so on) use the shared libraries they they were originally built against and thus have compatible ABI. This makes frozen applications portable, more or less self-contained, and isolated from the target environment.

The above paradigm is inherently at odds with code that is trying to launch an external program, i.e., a program that is available on the target system (and launched, for example, via System-installed external programs are built against shared libraries provided by the system, which might be of different and incompatible versions compared to the ones bundled with the frozen application. And because in the run-time environment of the PyInstaller-frozen application (which is inherited by the launched subprocesses) the library search path is modified to prefer the bundled shared libraries, trying to launch an external program might fail due to shared library conflicts.

Therefore, if your code (or the 3rd party code you are using) is trying to launch an external program, you need to ensure that the changes to the library search paths, made for the frozen application’s main process, are reset or reverted. The specifics of such run-time environment sanitization are OS-dependent, and are outlined in the following sub-sections.


On some operating systems, the library search path is modified only via environment variables; in such cases, if you are launching the subprocess in your code (e.g., via, you can pass the sanitized environment to the subprocess via the env argument. This way, only the environment of the sub-process is modified, while the environment of the frozen application itself (i.e., its search paths) are left unchanged.

If this is not possible, however, you might need to temporarily sanitize the environment of the main application, launch the external program (so it inherits the sanitized environment), and then restore the main application’s environment back to the original (PyInstaller-adjusted) version.

If you are launching the external program after all modules have been imported and their dependencies have been loaded, and if your frozen application does not include any helper programs that might be launched after your external program, you can simply sanitize main application’s run-time environment, without having to worry about restoring it after your external program is launched.

Linux and Unix-like OSes

On POSIX systems (with exception of macOS - see its dedicated sub-section), the library search path is modified via the LD_LIBRARY_PATH environment variable (LIBPATH on AIX).

During the frozen application’s startup, the PyInstaller’s bootloader checks whether the LD_LIBRARY_PATH environment variable is already set, and, if necessary, creates a copy of its contents into LD_LIBRARY_PATH_ORIG environment variable. Then, it modifies LD_LIBRARY_PATH by prepending the application’s top level directory (i.e., the path that is also available in sys._MEIPASS).

Therefore, prior to launching an external program, the LD_LIBRARY_PATH should be either cleared (to use the system default) or reset to the value stored in LD_LIBRARY_PATH_ORIG (if available). See LD_LIBRARY_PATH / LIBPATH considerations for details and an example.


On Windows, the PyInstaller’s bootloader sets the library search path to the top-level application directory (i.e., the path that is also available in sys._MEIPASS) using the SetDllDirectoryW Win32 API function.

As noted in the API documentation, calling this function also affects the children processes started from the frozen application. To undo the effect of this call and restore standard search paths, SetDllDirectory function should be called with NULL argument. As discussed in #3795, the most practical way to achieve this from python code is to use ctypes, for example:

import sys
if sys.platform == "win32":
    import ctypes

PyInstaller’s bootloader does not modify the PATH environment variable. However, the PATH environment variable may be modified by run-time hooks for specific packages, in order to facilitate discovery of dynamic dependencies that are loaded at run-time.

Therefore, it may also be necessary to sanitize the PATH environment variable, and (temporarily) remove any paths anchored in top-level application directory (sys._MEIPASS) prior to launching the external program.


On macOS, PyInstaller rewrites the library paths in collected binaries to refer to copies (or symbolic links) in the top-level application directory, relative to the binary’s location. Therefore, PyInstaller’s bootloader does not need to modify the DYLD_LIBRARY_PATH environment variable.

However, the DYLD_LIBRARY_PATH environment variable may be modified by run-time hooks for specific packages, in order to facilitate discovery of dynamic dependencies that are loaded at run-time.

Therefore, it may also be necessary to sanitize the DYLD_LIBRARY_PATH environment variable, and (temporarily) remove any paths anchored in top-level application directory (sys._MEIPASS) prior to launching the external program.


If you are building a macOS .app bundle, you should be aware that when launched from Finder, the app process runs in an environment with reduced set of environment variables. Most notably, the PATH environment variable is set to only /usr/bin:/bin:/usr/sbin:/sbin. Therefore, programs installed in locations that are typically in PATH when running a Terminal session (e.g., /usr/local/bin, /opt/homebrew/bin) will not be visible to the app, unless referenced by their full path.


Currently, the only supported multi-processing framework is the multiprocessing module from the Python standard library, and even that requires you to make a multiprocessing.freeze_support() call before using any multiprocessing functionality.

A typical symptom of failing to call multiprocessing.freeze_support() before your code (or 3rd party code you are using) attempts to make use of multiprocessing functionality is an endless spawn loop of your application process.


multiprocessing supports different start modes: spawn, fork, and forkserver. Of these, fork is the only one that might work in the frozen application without calling multiprocessing.freeze_support(). The default start method on Windows and macOS is spawn, while fork is default on other POSIX systems (however, Python 3.14 plans to change that).

Why is calling multiprocessing.freeze_support() required?

As implied by its name, the multiprocessing module spawns several processes; typically, these are worker processes running your tasks. On POSIX systems, spawn and forkserver start methods also spawn a dedicated resource tracker process that tracks and handles clean-up of unlinked shared resources (e.g., shared memory segments, semaphores).

The sub-processes started by multiprocessing are spawned using sys.executable - when running an unfrozen python script, this corresponds to your python interpreter executable (e.g., python.exe). The command-line arguments instruct the interpreter to run a corresponding function from the multiprocessing module. For example, the spawned worker process on Windows looks as follows:

python.exe -c "from multiprocessing.spawn import spawn_main; spawn_main(parent_pid=6872, pipe_handle=520)" --multiprocessing-fork

Similarly, when using the spawn start method on a POSIX system, the resource tracker process is started with the following arguments:

python -c from multiprocessing.resource_tracker import main;main(5)

while the worker process is started with the following arguments:

python -c "from multiprocessing.spawn import spawn_main; spawn_main(tracker_fd=6, pipe_handle=8)" --multiprocessing-fork

In the frozen application, sys.executable points to your application executable. So when the multiprocessing module in your main process attempts to spawn a subprocess (a worker or the resource tracker), it runs another instance of your program, with the following arguments for resource tracker:

my_program -B -S -I -c "from multiprocessing.resource_tracker import main;main(5)"

and for the worker process:

my_program --multiprocessing-fork tracker_fd=6 pipe_handle=8

On Windows, the worker process looks similar:

my_program.exe --multiprocessing-fork parent_pid=8752 pipe_handle=1552

If no special handling is in place in the program code, the above invocations end up executing your program code, which leads to one of the two outcomes:

  • this second program instance again reaches the point where multiprocessing module attempts to spawn a subprocess, leading to an endless recursive spawn loop that eventually crashes your system.

  • if you have command-line parsing implemented in your program code, the command-line parser raises an error about unrecognized parameters. Which may lead to periodic attempts at spawning the resource tracker process.

Enter multiprocessing.freeze_support() - PyInstaller provides a custom override of this function, which is required to be called on all platforms (in contrast to original standard library implementation, which, as suggested by its documentation, caters only to Windows). Our implementation inspects the arguments (sys.argv) passed to the process, and if they match the arguments used by multiprocessing for a worker process or resource tracker, it diverts the program flow accordingly (i.e., executes the corresponding multiprocessing code and exits after finished execution).

This ensures that multiprocessing sub-processes, while re-using the application executable, execute their intended multiprocessing functionality instead of executing your main program code.

When to call multiprocessing.freeze_support()?

The rule of thumb is, multiprocessing.freeze_support() should be called before trying to use any of multiprocessing functionality (such as spawning a process or opening process pool, or allocating a shared resource, for example a semaphore).

Therefore, as documented in original implementation of multiprocessing.freeze_support(), a typical call looks like this:

from multiprocessing import Process, freeze_support

def f():
    print('hello world!')

if __name__ == '__main__':

However, there are scenarios where you might need to make the call even sooner, before (at least some of) the imports at the top of your script. This might be necessary if your script imports a module that does one of the following during its initialization (i.e., when it is imported):

  • makes use of multiprocessing functionality.

  • parses command-line arguments for your program.

  • imports and initializes a GUI framework. While this might not result in an error, it should be avoided in the worker processes by diverting the program flow before it happens.

Similarly, if both of the following conditions are true:

  • your script imports several heavy-weight modules that are needed by the main program but not by the worker process

  • your script does not directly use multiprocessing functionality itself, but rather imports a 3rd party module and calls a function from it that uses multiprocessing,

then it might be worth placing the multiprocessing.freeze_support() before the imports, to avoid unnecessarily slowing the worker processes:

# Divert the program flow in worker sub-process as soon as possible,
# before importing heavy-weight modules.
if __name__ == '__main__':
    import multiprocessing

# Import several heavy-weight modules
import numpy as np
import cv2
# ...
import some_module

if __name__ == '__main__':
    # Call some 3rd party function that internally uses multiprocessing


If multiprocessing is used only in an external module that is imported and used by your script, then the multiprocessing worker sub-process needs to load and initialize only that module; therefore, diverting the program flow using multiprocessing.freeze_support() before performing heavy-weight imports avoids unnecessarily slowing down the worker process.

On the other hand, if your main script (also) uses multiprocessing functionality, then the corresponding worker sub-process also need to execute the remainder of your script, including the imports; which limits the performance benefits of an early multiprocessing.freeze_support() call.

What about other multi-processing frameworks?

The Python ecosystem provides several alternatives to the multiprocessing from the Python standard library - none of them are supported by PyInstaller.

The PyInstaller-frozen application does not have access to python interpreter executable (python or python.exe) and its environment, and must therefore use its embedded python interpreter. Therefore, any other alternative python-based multi-processing solution would also need to spawn its worker subprocesses using the program executable (sys.executable).

Even if the alternative multi-processing framework uses sys.executable to spawn its subprocesses, your program code would need to be made aware of such attempts, and handle them accordingly. In other words, you would need to implement inspection of program arguments (sys.argv), detect attempts at spawning worker subprocesses based on the arguments, and divert the program flow into corresponding framework’s function instead of letting it reach your main program code.

sys.stdin, sys.stdout, and sys.stderr in noconsole/windowed Applications (Windows only)

On Windows, the --noconsole allows you to build a frozen application using the “windowed” bootloader variant, which was built with /SUBSYSTEM:WINDOWED option (as opposed to /SUBSYSTEM:CONSOLE; see here for details), and thus has no console attached. This is similar to the windowed python interpreter executable, pythonw.exe, which can be used to run python scripts that do not require a console, nor want to open a console window when launched.

A direct consequence of building your frozen application in the windowed/no-console mode is that standard input/output file objects, sys.stdin, sys.stdout, and sys.stderr are unavailable, and are set to None. The same would happen if you ran your unfrozen code using the pythonw.exe interpreter, as documented under sys.__stderr__ in Python standard library documentation.

Therefore, if your code (or the 3rd party code you are using) naively attempts to access attributes of sys.stdout and sys.stderr objects without first ensuring that the objects are available, the frozen application will raise an AttributeError; for example, trying to access sys.stderr.flush will result in 'NoneType' object has no attribute 'flush'.

The best practice would be to fix the offending code so that it checks for availability of the standard I/O file objects before trying to use them; this will ensure compatibility with both pythonw.exe interpreter and with PyInstaller’s noconsole mode. However, if fixing the problem is not an option (for example, the problem originates from a 3rd party module and is beyond your control), you can work around it by setting dummy file handles at the very start of your program:

import sys
import os

if sys.stdout is None:
    sys.stdout = open(os.devnull, "w")
if sys.stderr is None:
    sys.stderr = open(os.devnull, "w")

# the rest of your imports

# and the rest of your program


If you plan to build your frozen application in windowed/no-console mode, we recommend that you first try running your unfrozen script using the pythonw.exe interpreter to ensure that it works correctly when console is unavailable.