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Adafruit Industries · September 11 · 42 sec

CircuitPython Turbo: Faster Code, Same Friendly Python

Meet CircuitPython Turbo from Adafruit. CircuitPython Turbo gives the busiest parts of your Python project a faster way to run. "Turbo" translated selected Python functions into instructions the chip can execute directly, while Python keeps handling the rest. In one recorded test, a Mandelbrot calculation ran nearly 20 times faster on an Adafruit Metro RP2040. That does not make everything 20 times faster, but it can free up time for animation, graphics, and responding to input. Explore real hardware demos, see how it works, and try it yourself. Keep the readable code, speed up the demanding parts, and build something worth watching. Go Blinka! Speed off into the project with the Adafruit Learning guide: https://learn.adafruit.com/circuitpython-turbo?view=all CircuitPython Turbo uses mpy-cross to compile selected Python functions ahead of time into architecture-specific native .mpy modules. Keep code.py as the coordinator while imported modules handle computational hotspots. Native mode retains Python objects; Viper supports machine-word integers and pointers for tighter loops. On Adafruit Metro RP2040, a fixed-point 160 × 120 Mandelbrot benchmark dropped from 8,335.3 ms as bytecode to 422.9 ms with Viper: 19.71× faster computation. The source remains available as an import-time fallback when compiled modules are absent. Explore the toolchain, compare outputs, and measure your complete application: faster arithmetic does not eliminate display, storage, or communication delays. Same snake, less waiting 🐍 #circuitpython #adafruit #python

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Meet CircuitPython Turbo from Adafruit. CircuitPython Turbo gives the busiest parts of your Python project a faster way to run. "Turbo" translated selected Python functions into instructions the chip can execute directly, while Python keeps handling the rest. In one recorded test, a Mandelbrot calculation ran nearly 20 times faster on an Adafruit Metro RP2040. That does not make everything 20 times faster, but it can free up time for animation, graphics, and responding to input. Explore real hardware demos, see how it works, and try it yourself. Keep the readable code, speed up the demanding parts, and build something worth watching. Go Blinka!

Speed off into the project with the Adafruit Learning guide:

https://learn.adafruit.com/circuitpython-turbo?view=all

CircuitPython Turbo uses mpy-cross to compile selected Python functions ahead of time into architecture-specific native .mpy modules. Keep code.py as the coordinator while imported modules handle computational hotspots. Native mode retains Python objects; Viper supports machine-word integers and pointers for tighter loops. On Adafruit Metro RP2040, a fixed-point 160 × 120 Mandelbrot benchmark dropped from 8,335.3 ms as bytecode to 422.9 ms with Viper: 19.71× faster computation. The source remains available as an import-time fallback when compiled modules are absent. Explore the toolchain, compare outputs, and measure your complete application: faster arithmetic does not eliminate display, storage, or communication delays.

Same snake, less waiting 🐍

#circuitpython #adafruit #python

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