Is MicroPython fast enough?

Norma 38 Published: 09/29/2024

Is MicroPython fast enough?

I'm glad you're interested in learning about MicroPython's performance!

MicroPython is a lean and mean operating system designed specifically for microcontrollers and other resource-constrained devices. Its primary goal is to provide an efficient way to run Python code on tiny computers, which often require a balance between power consumption, memory usage, and processing speed.

To answer your question: "Is MicroPython fast enough?"

The short answer is: It depends!

MicroPython's performance largely relies on the specific hardware it's running on. On modern microcontrollers with decent processing power, such as the Pyboard or the ESP32, MicroPython can handle tasks efficiently. For instance:

Reading and writing to the file system: 10-100 ms (milliseconds) Executing simple Python scripts: 1-5 ms Running a full-fledged Python application: 50-200 ms

However, when running on older or less powerful microcontrollers, MicroPython's performance can be affected by the limited resources. For example:

On an Arduino Uno or similar boards: You might notice slower execution times (100-500 ms) due to the limited processing power and memory. On ultra-low-power devices like 8-bit microcontrollers: Execution times might range from 1-10 seconds, depending on the complexity of the Python code.

Now, let's get into some interesting aspects that impact MicroPython's performance:

Memory management: MicroPython uses a custom garbage collector to manage memory efficiently. This ensures that your Python scripts don't consume too much RAM. Just-in-Time (JIT) compilation: Some versions of MicroPython use JIT compilation, which translates Python code into machine-specific instructions on the fly. This optimization can significantly improve performance. Code caching: MicroPython caches executed Python code to speed up future executions by avoiding repeated compilations.

When evaluating MicroPython's performance, consider the following:

Your specific hardware and its limitations The complexity of your Python code (more complex scripts might be slower) Any external dependencies or libraries you're using

To get the most out of MicroPython's performance:

Optimize your Python code: Use efficient algorithms, minimize loops, and avoid unnecessary operations. Choose the right hardware: Select a microcontroller that matches your project's requirements and has sufficient processing power. Explore optimizations: Check if the specific MicroPython version you're using offers features like JIT compilation or code caching to boost performance.

In conclusion:

MicroPython is capable of running Python code efficiently on various microcontrollers, depending on the hardware and software configuration. While it may not be as fast as other languages on more powerful devices, it's an excellent choice for small, resource-constrained projects where power consumption, memory usage, and simplicity are crucial factors.

Now, go forth and create something amazing with MicroPython!

How much RAM do I need for MicroPython?

I'm happy to respond in English for this one!

When it comes to determining how much RAM (Random Access Memory) you need for Micropython, there are a few factors to consider.

Firstly, the amount of RAM required will depend on the specific project or application you're working on. Are you building a simple IoT device that just needs to send and receive some data? Or are you creating a complex machine learning model that requires a significant amount of memory to operate?

In general, MicroPython is designed to be used on low-power, low-resource microcontrollers. For these types of devices, you can get away with very small amounts of RAM - as little as 128KB or even less. However, if you're using MicroPython for more complex projects, such as:

Building a machine learning model Running a full-featured web server Creating a real-time operating system (RTOS) Using advanced cryptographic libraries

then you'll likely need more RAM.

Here's a rough guide to help you estimate the amount of RAM you might need:

Simple IoT devices: 128KB - 256KB Small-to-medium-sized projects: 256KB - 512KB Large-scale projects: 512KB - 1MB or more

It's also worth noting that MicroPython has a garbage collector, which helps to free up memory by automatically freeing objects that are no longer needed. This can help you get away with less RAM than you might need otherwise.

In terms of specific device requirements, here are some examples:

ESP32: 520KB (with MicroPython) or 528KB (with Lua) PyBoard: 256KB CircuitPython-enabled boards: varies depending on the board and project

Ultimately, the amount of RAM you need will depend on your specific use case. If you're unsure, it's always a good idea to start with a smaller amount of RAM and then adjust as needed.

I hope this helps!