Pyjion, a JIT extension for CPython that compiles your Python code into native CIL and executes it using the .NET 7 CLR.

You can test out Pyjion now at live.trypyjion.com.

Read the full documentation at pyjion.readthedocs.io.

$ pip install pyjion

Prerequisites:

• CPython 3.10
• CMake 3.13 +
• .NET 7
• scikit-build

 $ git clone git@github.com:tonybaloney/pyjion --recurse-submodules
 $ cd pyjion
 $ python -m pip install .

To get started, you need to have .NET installed, with Python 3.10 and the Pyjion package (I also recommend using a virtual environment).

After importing pyjion, enable it by calling pyjion.enable() which sets a compilation threshold to 0 (the code only needs to be run once to be compiled by the JIT):

>>> import pyjion
>>> pyjion.enable()

Any Python code you define or import after enabling pyjion will be JIT compiled. You don't need to execute functions in any special API, its completely transparent:

>>> def half(x):
...    return x/2
>>> half(2)
1.0

Pyjion will have compiled the half function into machine code on-the-fly and stored a cached version of that compiled function inside the function object. You can see some basic stats by running pyjion.info(f), where f is the function object:

>>> pyjion.info(half)
JitInfo(failed=False, compile_result=<CompilationResult.Success: 1>, compiled=True, optimizations=<OptimizationFlags.InlineFramePushPop|InlineDecref: 10>, pgc=1, run_count=1)

You can also execute Pyjion against any script or module:

pyjion my_script.py

Or, for an existing Python module:

pyjion -m calendar

Pyjion has 3 optimization levels, 0 is the lowest and 2 is the highest. By default, Pyjion will compile at level 1.

The level can be changed using the config() function:

pyjion.config(level=2)

Once you've run your code and tests on level 1, try level 2 to compare performance. If you have problems, try lowering the level.

If you get a pyjion.PyjionUnboxingError when running the code, this means that Pyjion optimized a function which had floats, ints or boolean values, but those types have changed in subsequent calls. To avoid this, you can disable PGC (pyjion.config(pgc=False)) or lower the optimization level. Both options will hurt the overall performance, so if you can, refactor the function to not have general operations for a mixture of types.

You can see the machine code for the compiled function by disassembling it in the Python REPL. Pyjion has essentially compiled your small Python function into a small, standalone application. Install distorm3 and rich first to disassemble x86-64 assembly and run pyjion.dis.dis_native(f):

>>> import pyjion.dis
>>> pyjion.dis.dis_native(half)
00000000: PUSH RBP
00000001: MOV RBP, RSP
00000004: PUSH R14
00000006: PUSH RBX
00000007: MOV RBX, RSI
0000000a: MOV R14, [RDI+0x40]
0000000e: CALL 0x1b34
00000013: CMP DWORD [RAX+0x30], 0x0
00000017: JZ 0x31
00000019: CMP QWORD [RAX+0x40], 0x0
0000001e: JZ 0x31
00000020: MOV RDI, RAX
00000023: MOV RSI, RBX
00000026: XOR EDX, EDX
00000028: POP RBX
00000029: POP R14
...

The complex logic of converting a portable instruction set into low-level machine instructions is done by .NET's CLR JIT compiler.

All Python code executed after the JIT is enabled will be compiled into native machine code at runtime and cached on disk. For example, to enable the JIT on a simple app.py for a Flask web app:

from src import pyjion
pyjion.enable()

from flask import Flask
app = Flask(__name__)

@app.route('/')
def hello_world():
    return 'Hello, World!'

app.run()

Like the word "pigeon". @DinoV wanted a name that had something with "Python" -- the "Py" part -- and something with "JIT" -- the "JI" part -- and have it be pronounceable.

PyPy is an implementation of Python with its own JIT. The biggest difference compared to Pyjion is that PyPy doesn't support all C extension modules without modification unless they use CFFI or work with the select subset of CPython's C API that PyPy does support. Pyjion also aims to support many JIT compilers while PyPy only supports their custom JIT compiler.

Pyston is an implementation of Python using LLVM as a JIT compiler. Compared to Pyjion, Pyston has partial CPython C API support but not complete support. Pyston also only supports LLVM as a JIT compiler.

Numba is a JIT compiler for "array-oriented and math-heavy Python code". This means that Numba is focused on scientific computing while Pyjion tries to optimize all Python code. Numba also only supports LLVM.

IronPython is an implementation of Python that is implemented using .NET. While IronPython tries to be usable from within .NET, Pyjion does not have a compatibility story with .NET. This also means IronPython cannot use C extension modules while Pyjion can.

Psyco was a module that monkeypatched CPython to add a custom JIT compiler. Pyjion wants to introduce a proper C API for adding a JIT compiler to CPython instead of monkeypatching it. It should be noted the creator of Psyco went on to be one of the co-founders of PyPy.

Unladen Swallow was an attempt to make LLVM be a JIT compiler for CPython. Unfortunately the project lost funding before finishing their work after having to spend a large amount of time fixing issues in LLVM's JIT compiler (which has greatly improved over the subsequent years).

Both Nuitka and Shedskin are Python-to-C++ transpilers, which means they translate Python code into equivalent C++ code. Being a JIT, Pyjion is not a transpiler.

Goal #1 is explicitly to add a C API to CPython to support JIT compilers. There is no expectation, though, to ship a JIT compiler with CPython. This is because CPython compiles with nothing more than a C89 compiler, which allows it to run on many platforms. But adding a JIT compiler to CPython would immediately limit it to only the platforms that the JIT supports.

No. Use Python.NET if you want integration with Python and .NET 4.

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.