numba.pydata.orgNumba: A High Performance Python Compiler

Learn Numba in 5 minutes Documentation Overview User Manual Reference Manual NVIDIA CUDA GPU Programming AMD ROCm GPU Programming Developer Manual Release Notes Install Examples Talks/Tutorials Community Github PyPI Gitter Chat Numba Mailing List Numba makes Python code fast Numba is an open source JIT compiler that translates a subset of Python and NumPy code into fast machine code. Learn More Try Numba » Accelerate Python Functions Numba translates Python functions to optimized machine code at runtime using the industry-standard LLVM compiler library. Numba-compiled numerical algorithms in Python can approach the speeds of C or FORTRAN. You don’t need to replace the Python interpreter, run a separate compilation step, or even have a C/C++ compiler installed. Just apply one of the Numba decorators to your Python function, and Numba does the rest. Learn More » Try Now » from numba import jit import random @jit(nopython=True) def monte_carlo_pi(nsamples): acc = 0 for i in range(nsamples): x = random.random() y = random.random() if (x ** 2 + y ** 2)1.0: acc += 1 return 4.0 * acc / nsamples Built for Scientific Computing Numba is designed to be used with NumPy arrays and functions. Numba generates specialized code for different array data types and layouts to optimize performance. Special decorators can create universal functions that broadcast over NumPy arrays just like NumPy functions do. Numba also works great with Jupyter notebooks for interactive computing, and with distributed execution frameworks, like Dask and Spark. Learn More » Try Now » @numba.jit(nopython=True, parallel=True) def logistic_regression(Y, X, w, iterations): for i in range(iterations): w -= np.dot(((1.0 / (1.0 + np.exp(-Y * np.dot(X, w))) - 1.0) * Y), X) return w Parallelize Your Algorithms Numba offers a range of options for parallelizing your code for CPUs and GPUs, often with only minor code changes. Simplified Threading @jit(nopython=True, parallel=True) def simulator(out): # iterate loop in parallel for i in prange(out.shape[0]): out[i] = run_sim() Numba can automatically execute NumPy array expressions on multiple CPU cores and makes it easy to write parallel loops. Learn More » Try Now » SIMD Vectorization LBB0_8: vmovups (%rax,%rdx,4), %ymm0 vmovups (%rcx,%rdx,4), %ymm1 vsubps %ymm1, %ymm0, %ymm2 vaddps %ymm2, %ymm2, %ymm2 Numba can automatically translate some loops into vector instructions for 2-4x speed improvements. Numba adapts to your CPU capabilities, whether your CPU supports SSE, AVX, or AVX-512. Learn More » Try Now » GPU Acceleration With support for both NVIDIA’s CUDA and AMD’s ROCm drivers, Numba lets you write parallel GPU algorithms entirely from Python. Numba CUDA » Numba ROCm » Portable Compilation Ship high performance Python applications without the headache of binary compilation and packaging. Your source code remains pure Python while Numba handles the compilation at runtime. We test Numba continuously in more than 200 different platform configurations. Numba supports Intel and AMD x86, POWER8/9, and ARM CPUs, NVIDIA and AMD GPUs, Python 2.7 and 3.4-3.7, as well as Windows/macOS/Linux. Precompiled Numba binaries for most systems are available as conda packages and pip-installable wheels. Learn More » Acknowledgements Numba development is made possible through the current and/or past support of a number of organizations: © 2018 Anaconda HTML layout adapted from the Dask...