JIT compiler for Python that translates numerical code into optimized machine code using LLVM. Supports @jit, @njit, @vectorize, @guvectorize, and @jitclass decorators with CPU parallelization, SIMD vectorization, and CUDA GPU targets. Use when accelerating Python numerical loops, creating NumPy-compatible ufuncs, parallelizing array operations, or targeting GPU execution from pure Python.
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name
numba-0-65-0
description
JIT compiler for Python that translates numerical code into optimized machine code using LLVM. Supports @jit, @njit, @vectorize, @guvectorize, and @jitclass decorators with CPU parallelization, SIMD vectorization, and CUDA GPU targets. Use when accelerating Python numerical loops, creating NumPy-compatible ufuncs, parallelizing array operations, or targeting GPU execution from pure Python.
Numba is an open-source just-in-time (JIT) compiler that translates a subset of Python and NumPy code into fast machine code. It uses the LLVM compiler infrastructure to generate optimized native code at runtime, achieving performance comparable to C, C++, or Fortran without switching languages or interpreters.
Key capabilities:
JIT compilation — On-the-fly code generation at import time or runtime via decorators
Native CPU code — Code tailored to specific CPU capabilities (SSE, AVX, AVX-512)
GPU acceleration — NVIDIA CUDA support for parallel GPU algorithms from pure Python
NumPy integration — Deep support for NumPy arrays, ufuncs, and broadcasting
Parallel execution — Automatic multi-core parallelization with parallel=True and explicit prange loops
SIMD vectorization — Automatic translation of loops into vector instructions
Numba works best on code that uses NumPy arrays, mathematical operations, and loops. It is not suited for code heavy in pandas, string manipulation, or general-purpose Python features.
When to Use
Accelerating performance-critical numerical loops in Python
Parallelizing array computations across multiple CPU cores
Writing stencil operations for image processing, PDE solving, and spatial computations
Compiling Python classes with typed fields for use in JIT-compiled code
Generating C callbacks for interfacing with native libraries
Targeting NVIDIA CUDA GPUs from pure Python
Building ahead-of-time compiled extension modules
Core Concepts
Compilation Modes
Numba operates in two compilation modes:
Nopython mode — The default and recommended mode. Compiles the function to run entirely without the Python interpreter, producing the fastest code. Use @njit (alias for @jit(nopython=True)) or plain @jit.
Object mode — Falls back to running code through the Python interpreter when nopython compilation fails. Use only when necessary; it provides minimal speedup and adds overhead.
Type System
Numba uses a fine-grained type system rather than Python's dynamic types. Types are inferred from argument types at call time. Key numeric types include int8–, –, , , , , and boolean. Array types use subscript notation: for 1D, for 2D.
int64
uint8
uint64
float32
float64
complex64
complex128
float64[:]
float64[:, :]
Lazy vs Eager Compilation
Lazy compilation (default) — Compilation is deferred until the first function call with specific argument types. Separate specializations are generated for different input types.
Eager compilation — Specify an explicit signature at decoration time to compile immediately: @jit(float64(float64, float64)).
Performance Measurement
Always account for compilation time when benchmarking. The first call includes JIT compilation overhead. Use timeit or call the function once before timing to measure post-compilation performance.
Installation / Setup
Numba is available via conda or pip:
Conda: conda install numba
Pip: pip install numba
Optional dependencies for additional functionality:
scipy — enables numpy.linalg function compilation
colorama — color highlighting in error messages
pyyaml — YAML configuration file support (.numba_config.yaml)
intel-cmplr-lib-rt — Intel SVML high-performance math library (x86_64)
tbb — Intel TBB threading layer for parallel execution
Supported platforms: x86, x86_64, POWER8/9, ARM (including Apple M1), NVIDIA GPUs. Operating systems: Windows, macOS, Linux. Python versions: 3.9–3.12.
Usage Examples
Basic JIT Compilation
from numba import njit
import numpy as np
@njitdefsum_array(arr):
total = 0.0for i inrange(arr.shape[0]):
for j inrange(arr.shape[1]):
total += arr[i, j]
return total
x = np.arange(100).reshape(10, 10)
print(sum_array(x))
Parallel Loops with prange
from numba import njit, prange
import numpy as np
@njit(parallel=True)defparallel_sum(A):
s = 0.0for i in prange(A.shape[0]):
s += A[i]
return s
Creating a ufunc with @vectorize
from numba import vectorize, float64
@vectorize([float64(float64, float64)])defadd(x, y):
return x + y
# Works like a NumPy ufunc with reduce, accumulate, broadcasting
result = add.reduce(some_array, axis=0)
import numpy as np
from numba import int32, float32
from numba.experimental import jitclass
spec = [
('value', int32),
('array', float32[:]),
]
@jitclass(spec)classBag:
def__init__(self, value):
self.value = value
self.array = np.zeros(value, dtype=np.float32)
@propertydefsize(self):
returnself.array.size
defincrement(self, val):
for i inrange(self.size):
self.array[i] += val
returnself.array
mybag = Bag(10)
C Callback with @cfunc
from numba import cfunc
@cfunc("float64(float64, float64)")defadd(x, y):
return x + y
# Access the ctypes callbackprint(add.ctypes(4.0, 5.0)) # prints 9.0
Supported Features: Python language support, built-in types, standard library modules, NumPy features, deviations from Python semantics → Supported Features
Environment Variables and Debugging: Configuration via .numba_config.yaml, JIT flags, debugging tools, GDB integration → Configuration and Debugging
