Frequently Asked Questions

Where Can I Report Bugs / Send Feedback / Request Features?


Why Build Another Python Application Packaging Tool?

It is true that several other tools exist to turn Python code into distributable applications! Comparisons to Other Tools attempts to exhaustively compare PyOxidizer to these myriad of tools. (If a tool is missing or the comparison incomplete or unfair, please file an issue so Python application maintainers can make better, informed decisions!)

The long version of how PyOxidizer came to be can be found in the Distributing Standalone Python Applications blog post. If you really want to understand the motivations for starting a new project rather than using or improving an existing one, read that post.

If you just want the extra concise version, at the time PyOxidizer was conceived, there were no Python application packaging/distribution tool which satisfied all of the following requirements:

  • Works across all platforms (many tools target e.g. Windows or macOS only).
  • Does not require an already-installed Python on the executing system (rules out e.g. zip file based distribution mechanisms).
  • Has no special system requirements (e.g. SquashFS, container runtimes).
  • Offers startup performance no worse than traditional python execution.
  • Supports single file executables with none or minimal system dependencies.

Can Python 2.7 Be Supported?

In theory, yes. However, it is considerable more effort than Python 3. And since Python 2.7 is being deprecated in 2020, in the project author’s opinion it isn’t worth the effort.

No python interpreter found of version 3.* Error When Building

This is due to a dependent crate insisting that a Python executable exist on PATH. Set the PYTHON_SYS_EXECUTABLE environment variable to the path of a Python 3.7 executable and try again. e.g.:

$ export PYTHON_SYS_EXECUTABLE=/usr/bin/python3.7
# Windows
$ SET PYTHON_SYS_EXECUTABLE=c:\python37\python.exe


The pyoxidizer tool should take care of setting PYTHON_SYS_EXECUTABLE and prevent this error. If you see this error and you are building with pyoxidizer, it is a bug that should be reported.

Why Rust?

This is really 2 separate questions:

  • Why choose Rust for the run-time/embedding components?
  • Why choose Rust for the build-time components?

PyOxidizer binaries require a driver application to interface with the Python C API and that driver application needs to compile to native code in order to provide a native executable without requiring a run-time on the machine it executes on. In the author’s opinion, the only appropriate languages for this were C, Rust, and maybe C++.

Of those 3, the project’s author prefers to write new projects in Rust because it is a superior systems programming language that has built on lessons learned from decades working with its predecessors. The author prefers technologies that can detect and eliminate entire classes of bugs (like buffer overflow and use-after-free) at compile time. On a less-opinionated front, Rust’s built-in build system support means that we don’t have to spend considerable effort solving hard problems like cross-compiling. Implementing the embedding component in Rust also creates interesting opportunities to embed Python in Rust programs. This is largely an unexplored area in the Python ecosystem and the author hopes that PyOxidizer plays a part in more people embedding Python in Rust.

For the non-runtime packaging side of PyOxidizer, pretty much any programming language would be appropriate. The project’s author initially did prototyping in Python 3 but switched to Rust for synergy with the the run-time driver and because Rust had working solutions for several systems-level problems, such as parsing ELF, DWARF, etc executables, cross-compiling, integrating custom memory allocators, etc. A minor factor was the author’s desire to learn more about Rust by starting a real Rust project.

Why is the Rust Code… Not Great?

This is the project author’s first real Rust project. Suggestions to improve the Rust code would be very much appreciated!

Keep in mind that the pyoxidizer crate is a build-time only crate and arguably doesn’t need to live up to quality standards as crates containing run-time code. Things like aggressive .unwrap() usage are arguably tolerable.

The run-time code that produced binaries run (pyembed) is held to a higher standard and is largely panic! free.

What is the Magic Sauce That Makes PyOxidizer Special?

There are 2 technical achievements that make PyOxidizer special.

First, PyOxidizer consumes Python distributions that were specially built with the aim of being used for standalone/distributable applications. These custom-built Python distributions are compiled in such a way that the resulting binaries have very few external dependencies and run on nearly every target system. Other tools that produce standalone Python binaries often rely on an existing Python distribution, which often doesn’t have these characteristics.

Second is the ability to import .py/.pyc files from memory. Most other self-contained Python applications rely on Python’s zipimporter or do work at run-time to extract the standard library to a filesystem (typically a temporary directory or a FUSE filesystem like SquashFS). What PyOxidizer does is expose the .py/.pyc modules data to the Python interpreter via a Python extension module built-in to the binary. In addition, the importlib._bootstrap_external module (which is frozen into libpython) is replaced by a modified version that defines a custom module importer capable of loading Python modules from the in-memory data structures exposed from the built-in extension module.

The custom importlib_bootstrap_external frozen module trick is probably the most novel technical achievement of PyOxidizer. Other Python distribution tools are encouraged to steal this idea!

See pyembed Crate for an overview of how the in-memory import machinery works.

Can Applications Import Python Modules from the Filesystem?

Yes. While the default is to import all Python modules from in-memory data structures linked into the binary, it is possible to configure sys.path to allow importing from additional filesystem paths. Support for importing compiled extension modules is also possible.

What are the Implications of Static Linking?

Most Python distributions rely heavily on dynamic linking. In addition to python frequently loading a dynamic libpython, many C extensions are compiled as standalone shared libraries. This includes the modules _ctypes, _json, _sqlite3, _ssl, and _uuid, which provide the native code interfaces for the respective non-_ prefixed modules which you may be familiar with.

These C extensions frequently link to other libraries, such as libffi, libsqlite3, libssl, and libcrypto. And more often than not, that linking is dynamic. And the libraries being linked to are provided by the system/environment Python runs in. As a concrete example, on Linux, the _ssl module can be provided by, which can have a shared library dependency against and, which can be located in /usr/lib/x86_64-linux-gnu or a similar location under /usr.

When Python extensions are statically linked into a binary, the Python extension code is part of the binary instead of in a standalone file.

If the extension code is linked against a static library, then the code for that dependency library is part of the extension/binary instead of dynamically loaded from a standalone file.

When PyOxidizer produces a fully statically linked binary, the code for these 3rd party libraries is part of the produced binary and not loaded from external files at load/import time.

There are a few important implications to this.

One is related to security and bug fixes. When 3rd party libraries are provided by an external source (typically the operating system) and are dynamically loaded, once the external library is updated, your binary can use the latest version of the code. When that external library is statically linked, you need to rebuild your binary to pick up the latest version of that 3rd party library. So if e.g. there is an important security update to OpenSSL, you would need to ship a new version of your application with the new OpenSSL in order for users of your application to be secure. This shifts the security onus from e.g. your operating system vendor to you. This is less than ideal because security updates are one of those problems that tend to benefit from greater centralization, not less.

It’s worth noting that PyOxidizer’s library security story is the same as it is for e.g. Docker images. Docker images have the same security properties. If you are OK distributing Docker images, you should be OK with distributing executables built with PyOxidizer.

Another implication of static linking is licensing considerations. Static linking can trigger stronger licensing protections and requirements. Read more at Licensing Considerations.

error while loading shared libraries: cannot open shared object file: No such file or directory When Building

If you see this error when building, it is because your Linux system does not conform to the Linux Standard Base Specification, does not provide a file, and the Python distribution that PyOxidizer attempts to run to compile Python source modules to bytecode can’t execute.

Fedora 30+ are known to have this issue. A workaround is to install the libxcrypt-compat on the machine running pyoxidizer. See for more info.