python-to-binary

py2bin turns Python into machine code using nothing but the Python standard library. No Cython, Nuitka, mypyc, Rust, C, C++, PyInstaller, PPCI, bootloader, assembler, linker or SDK - and no gcc or clang at any point. The only thing a build needs is an interpreter.

pip install python-to-binary
py2bin compile-capi app.py --target darwin-arm64 -o app

Source, issues and the full documentation: https://github.com/yu314-coder/python_to_binary

Platforms

What compile-capi - the tier that turns your program into machine code that drives CPython - can target today.

Each working target is held to the same standard: an 889-program corpus is compiled for it and every program's output and exit code compared against CPython's. macOS agrees on 878 and differs on 7; a 100-program slice run through Wine agrees on 93 and differs on 5. The differences are the same on every platform and are inherent rather than open - CPython's "Did you mean" needs a Python frame to suggest from, and the repr of a compiled function really is a builtin function's.

The native tier (py2bin compile, no CPython at all) targets all six.

The paths through it

Three ways to turn a program into an artifact. They trade the same three things against each other, and which one you want depends on which you care about.

compile is the fastest and the smallest. Python AST → py2bin IR → optimizer → handwritten x86-64/ARM64 → ELF, PE or Mach-O. There is no interpreter in the artifact and none on the machine: 11× faster than CPython on that loop, in 48 KB that runs on a bare system. You pay for it in what it will accept - integers, floats, strings, control flow, your own functions - and it will not import a package at all.

freeze is the most complete. It ships your program beside an interpreter that runs it, so NumPy, Torch and a GUI toolkit all work exactly as they do now. Nothing is translated, so nothing is faster; the artifact is the largest of the three because an interpreter and every dependency are inside it.

compile-capi is the middle, and the one under active work. It translates ordinary Python into C that drives the CPython C API, then compiles that C with py2bin's own C compiler - the tier Nuitka occupies, with Nuitka's dependency on clang removed. Almost the whole language goes through, and anything the linked interpreter can import still works, so a real application with pywebview and Pillow compiles. Integer loops beat CPython because their locals are held in registers; most other operations are slower, because each one is a real C-API call where the interpreter has specialised bytecode. The per-feature table is below.

The loop above is deliberately unkind to compile-capi: its accumulator is compared against a parameter, which the register analysis cannot claim, so the fast path is off. On a loop it can claim, the same tier is 1.67× faster than CPython.

Using it

pip install python-to-binary

Bundling a real application into a macOS .app that carries its own interpreter and packages:

py2bin compile-capi app.py --target darwin-arm64 \
  --app --name "My App" --icon icon.icns \
  --embed-python --site ../Resources/site-packages \
  --bundle-site /path/to/venv/lib/python3.14/site-packages \
  --prune-unused --zip-stdlib \
  -o dist/MyApp.app --clean

How it works

Nothing wraps a toolchain; each stage is a module you can read.

capi_emit.py        Python AST  ->  C that calls the CPython C API
capi_ints.py          which locals may live in a machine register
c_preprocessor.py   #include, macros, conditionals
c_frontend.py       C  ->  py2bin IR
native/ir.py        the IR itself
native/optimizer.py constant folding, dead code, write merging
native/arm64.py     IR  ->  ARM64 instructions
native/x86_64.py    IR  ->  x86-64, System V and Microsoft x64
native/formats/     Mach-O, PE32+, ELF
freezer.py          bundling: interpreter, packages, pruning, archives
cabi.py             the vetted CPython entry points

So compile-capi is five stages, all of them in this package: capi_emit → c_preprocessor → c_frontend → native.x86_64/native.arm64 → native.formats.macho/pe.

There is no import ctypes anywhere on that path, which a test asserts by compiling in a fresh interpreter and listing what got loaded. ctypes is standard library and would pass an imports-only-stdlib check, but it pulls in subprocess - and there are Pythons where a subprocess is not something a program may have.

Three questions, and nothing to type

py2bin make

It asks which file is the program, which machine it is for, and what shape it should take. Everything else is found or downloaded rather than typed - the other .py files beside it, the libraries it imports, an interpreter for the target, web/ and assets/ if they are there, and an icon if one is.

Two other ways in ship in the source distribution: build.py runs a clone with nothing installed, and get-py2bin.py fetches py2bin for a machine with neither - falling back to curl or wget where Python's own networking is kept away from the interpreter.

Bundling for Windows

The executable, the interpreter and the packages share one directory, and one command assembles it:

py2bin compile-capi app.py --target windows-x86_64 --crash-log \
  --runtime /path/to/embeddable-cpython \
  --bundle-site /path/to/site-packages \
  -o dist/win/MyApp.exe

Neither needs a path on this machine: --auto-fetch downloads the interpreter for the target, and --fetch-package NAME downloads and unpacks a project's wheel. Both are checked against a published hash and cached.

--bundle-site copies packages into Lib\site-packages and names it on the interpreter's path, which has to happen together: the embeddable CPython ships a pythonXY._pth naming exactly two places, and once it exists sys.path is those two and nothing else. Packages are invisible until the path file names them, and the program reports ModuleNotFoundError for a directory plainly on disk - silently, if it is windowed.

A wheel must also match the interpreter's ABI, not only its version: cp314 and cp314t differ by a character, the second is for the free-threaded build, and only one loads.

macOS bundles, signing and disk images

--app writes a .app; --embed-python makes it carry its own interpreter, so it runs on a Mac with no Python installed. The bundle is signed and sealed as the last step of the build, once everything is in place, and codesign --verify --deep --strict exits 0 on the result.

The signature is ad-hoc - no Apple Developer ID, no notarisation, since either needs a paid account and Apple's own tooling. That only matters to Gatekeeper, which inspects apps carrying a quarantine flag: copied from a USB stick there is none, downloaded through a browser there is, and then the app needs one trip through System Settings → Privacy & Security → Open Anyway.

--dmg writes a mountable disk image beside the bundle. No hdiutil is involved, because nothing in this library may reach for a subprocess; the filesystem is written byte by byte as ISO 9660 with Joliet, which macOS mounts with files executable - what an .app needs in order to launch.

py2bin compile-capi app.py --app --dmg -o dist/MyApp.app

What compile-capi supports

Every row is checked by compiling it, running it, running the same source under CPython, and requiring identical stdout and exit status.

A generator cannot be compiled the way the rest is - a C function has one entry and its locals die with its frame, so it cannot stop in the middle of itself. It is turned inside out instead: the body is cut into blocks at each yield, the blocks are numbered, and the function becomes a class whose __next__ dispatches on which block to run next, with the locals as attributes because they have to outlive a return. The class is then compiled by the machinery that already compiles classes, so there is no new C and nothing interpreted at run time.

That covers yield as a statement or as a value, send, yield from, straight-line code, if/else, while, for, break, continue and a bare return. next(g) is g.send(None) here as it is in the protocol, and yield from is written as the loop it is before the body is cut - which forwards iteration but not a send into the sub-generator, so a yield from whose value is used is refused rather than quietly answering None.

A try/except around a yield works, and the way it works is worth saying, because "the handler has to survive the suspension" sounds like it needs something the cut cannot give. It does not: an exception can only be raised while a block is running, so each block of the guarded region carries the handler and it is re-established on every entry rather than having to persist across one.

await is the same machine with a second name on it. Awaiting an object with __await__ means delegating to the iterator it answers with, and a state machine is one - so an async def compiles to the same class, plus __await__ returning itself, and await x is PEP 380's expansion of yield from x.__await__(). A real event loop then drives it through send exactly as it drives a coroutine: asyncio.run, asyncio.sleep and asyncio.gather all work on compiled coroutines.

A finally around a yield works, and so do with, async for and async with - see How finally and with are handled above for how, and for the two shapes that are still refused.

A refusal is a file:line:col error, never a silent approximation. On an 889-program corpus, 878 programs produce byte-identical output to CPython; the 7 that differ do so inherently (CPython's "Did you mean" needs a Python frame, "v" is "v" depends on interning) and 4 are refused outright.

How fast each one is

300,000 iterations, median of 5, against the interpreter it links. Higher is better; 1.00× means the same speed as CPython.

Integer loops win because a local the analysis picks out is held in a machine register, with an overflow check that falls back to unbounded arithmetic when the value leaves the word. That is what CPython's specialising interpreter does, and doing anything less was what made this tier slower than not compiling at all.

Everything else loses, by a factor that tracks how many C-API calls the operation costs. Each one is a real call with the reference-count discipline around it, where the interpreter's specialised bytecode does the same work inline. Floats are not held in registers at all yet, which is the same job as the integers and not done.

Method calls and instantiation are far worse than the pattern predicts - 21× and 11× rather than the 2-4× everything else pays. That is not the C-API overhead; something in the class path is doing work per call that it should do once. It is the clearest thing to fix next and it is measured here rather than left out.

Raising a class

raise ValueError names a class and raise ValueError("x") an instance, and the two want different things from the C API - asking type() for the class of a class answers type, the metaclass. The plainest raise a program can write therefore ended in SystemError: exception <class 'type'> is not a BaseException subclass, in compiled code of every kind. Fixed.

How finally and with are handled

A generator becomes a class with __next__, not a generator: never closed, never finalised by the collector, so the only ways out of a protected region are the ones the rewriter can see. The cleanup is not a real finally: - a yield returns from __next__, so one would fire on every suspension. It is attached to the raising path as a handler that runs it and re-raises, while the ordinary path jumps to a block holding the same cleanup. with expands into the try it stands for and takes that path, with __exit__ looked up once on the type and suppression honoured.

async for and async with take the same route, each written out as what it stands for. A return here is signalled by raising StopIteration, so the cleanup's handler had to learn to tell the frame leaving from a real failure - otherwise __aexit__ is handed a StopIteration where CPython passes None.

Still refused, with the line and the reason: a finally that itself yields, and a break or continue leaving the region.

Measured against Nuitka

manim_app: 10,100 lines, pywebview + Pillow + pyobjc, built both ways on the same machine.

Run time, median of 5, seconds:

Loops beat both because a local the analysis picks out is held in a register rather than on the heap, with the overflow check that falls back to unbounded arithmetic when it leaves the word. Calls still lose: an argument is boxed at the call and unboxed inside, where the interpreter's specialised call pays neither.

Licence

MIT. Full documentation, source and issues: https://github.com/yu314-coder/python_to_binary