Cython
Numba
PyInstaller
nuitka
cx_Freeze
PyPy
py2exe
bbfreeze
Apple Core ML
Amazon Machine Learning
TensorFlow Lite
Roboflow Universe
ML5.js
Machine Learning Playground
Apple Machine Learning Journal
QuickAI
Cython Apple Core MLBased on our record, Cython should be more popular than Apple Core ML. It has been mentiond 47 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
Https://cython.org can help with that. - Source: Hacker News / about 1 month ago
The approach that I favour is to use Cython. The nice thing with this approach is that your code is still written as (almost) Python, but so long as you define all required types correctly it will automatically create the C extension for you. Early versions of Cython required using Cython specific typing (Python didn't have type hints when Cython was created), but it can now use Python's type hints. Source: 11 months ago
Just for reference, * Nuitka[0] "is a Python compiler written in Python. It's fully compatible with Python 2.6, 2.7, 3.4, 3.5, 3.6, 3.7, 3.8, 3.9, 3.10, and 3.11." * Pypy[1] "is a replacement for CPython" with builtin optimizations such as on the fly JIT compiles. * Cython[2] "is an optimising static compiler for both the Python programming language and the extended Cython programming language... Makes writing C... - Source: Hacker News / about 1 year ago
Profile and optimize the hotspots with cython (or whatever the cool kids are using these days... It's been a while.). Source: about 1 year ago
JIT essentially means generating machine code for the language on the fly, either during loading of the interpreter (method JIT), or by profiling and optimizing hotspots (tracing JIT). The language itself can be statically or dynamically typed. You could also compile a dynamic language ahead of time, for example, cython. Source: about 1 year ago
On the machine learning side of AI, they have CoreML. You can drag-and-drop images into Xcode to train an image classifier. And run the models on device, so if solar flares destroy the cell phone network and terrorists bomb all the data centers, your phone could still tell you if it's a hot dog or not. https://developer.apple.com/machine-learning/ https://developer.apple.com/machine-learning/core-ml/... - Source: Hacker News / 2 months ago
Apple has actually created ML chipsets, so AI can be executed natively, on-device. https://developer.apple.com/machine-learning/. - Source: Hacker News / 4 months ago
For your reference, Apple's pages for Machine Learning for Developers and for their research. The Apple Neural Engine was custom designed to work better with their proprietary machine learning programs -- and they've been opening up access to developers by extending support / compatibility for TensorFlow and PyTorch. They've also got CoreML, CreateML, and various APIs they are making to allow more use of their... Source: 12 months ago
> It’d be one thing if Apple actually worked on AI softwares a bit and made it readily available to developers. * Apple Silicon CPUs have a Neural Engine specifically made for fast ML-inference * Apple supports PyTorch (https://developer.apple.com/metal/pytorch/) * Apple has its own easily accessible machine-learning framework called Core-ML (https://developer.apple.com/machine-learning/) So it would be inaccurate... - Source: Hacker News / 12 months ago
This is the developer documentation where they advertise the APIs - https://developer.apple.com/machine-learning/. Source: over 2 years ago
Numba - Numba gives you the power to speed up your applications with high performance functions written...
Amazon Machine Learning - Machine learning made easy for developers of any skill level
PyInstaller - PyInstaller is a program that freezes (packages) Python programs into stand-alone executables...
TensorFlow Lite - Low-latency inference of on-device ML models
nuitka - Nuitka is a Python compiler.
Roboflow Universe - You no longer need to collect and label images or train a ML model to add computer vision to your project.
