Visual Studio Code on Raspberry Pi
You can run Visual Studio Code on Raspberry Pi devices.
By downloading and using Visual Studio Code, you agree to the license terms and privacy statement.
Installation
Visual Studio Code is officially distributed via the Raspberry Pi OS (previously called Raspbian) APT repository, in both 32-bit and 64-bit variants.
You can install it by running:
Running VS Code
After installing the VS Code package, you can run VS Code by typing code in a terminal or launching it via the Programming menu.
Updates
Your Raspberry Pi should handle updating VS Code in the same way as other packages on the system:
You can always check when a new release is available in our Updates page.
System requirements
VS Code is supported on these Raspberry Pi models running a 32-bit or 64-bit version of Raspberry Pi OS:
- Raspberry Pi 3 Model B/B+
- Raspberry Pi 4 Model B
- Raspberry Pi 400
While 1 GB of memory (RAM) meets the minimum system requirements, users will benefit from installing VS Code on a Raspberry Pi 4 with more memory.
First-generation Raspberry Pi modules and Raspberry Pi Zero are not supported as they only include an ARMv6 CPU.
Workaround for poor performance
VS Code on Raspberry Pi 4 may be slow with the default setup. A workaround is to disable hardware (GPU) acceleration in VS Code:
- Open the VS Code argv.json file using the Preferences: Configure Runtime Arguments command.
- Set "disable-hardware-acceleration": true .
- Restart VS Code.
The "disable-hardware-acceleration": true runtime argument switch has the effect of passing the —disable-gpu command-line argument on VS Code startup.
Next steps
Once you have installed VS Code, these topics will help you learn more about it:
Setup Visual Studio Code and Jupyter on Raspberry Pi
VSCode on Raspberry Pi
Introduction
Microsoft has released a version of Visual Studio Code (VSCode) for ARM-based computers like the Raspberry Pi. VSCode is one of the most popular and powerful code editors. There are many useful extensions for VSCode that make programming a lot easier.
For example, we can start a Jupyter notebook inside VSCode. Jupyter is an interactive Python environment that we can use to experiment with machine learning libraries. In this tutorial, we will:
- install and use VSCode on Raspberry Pi,
- install and use Jupyter on Raspberry Pi,
- do a simple machine learning lab inside Jupyter.
Install Visual Studio Code
First, visit the website of Visual Studio Code. On this page, click the link for the ARM version of the deb file.
Download the ARM 32 bit version of the DEB file
When the download is completed, open the terminal and navigate to the Downloads folder.
Check the name of the installation file.
Suppose the name of the file is code_1.52.0-1607640235_armhf.deb , type the following command to install Visual Studio Code.
Replace code_1.52.0-1607640235_armhf.deb by the name of the file that you have downloaded. Do not miss the ./ before the filename. The installation should be completed in a few minutes.
Installing VSCode
After the installation is completed, we can launch VSCode from the ‘Programming’ menu.
Launching VSCode from the menu
We can also launch VSCode from the terminal.
Install the Python extension
To install an extension, we can click the extension icon on the left.
Click the extension icon
In the search field, enter ‘python’, then select the Python extension made by Microsoft. Click the ‘Install’ button to install this extension.
Locate the Python extension made by Microsoft
Install Jupyter
Next, we will install Jypyter on Raspberry Pi. We can use the terminal as before, but we can also use the built-in terminal inside VSCode. To open the terminal, click Terminal → New Terminal in the menu or press ‘Ctrl + Shift + `’.
Terminal inside VSCode
In the terminal, use the following command to install Jupyter:
When the installation is completed, we can create a new Jupyter notebook directly inside VSCode. To do that, click View → Command Palette… or press ‘Ctrl + Shift + P’. Type ‘Jupyter’ in the search field of the command palette and select ‘Create New Blank Jupyter Notebook’.
Create New Blank Jupyter Notebook
Note: Many functionalities provided by VSCode extensions can be used through the Command Palette.
In the Jupyter notebook, we can write Python code inside a cell, and the code will be executed if we press ‘Shift + Enter’. The result of the code will then be shown in the cell.
Run Python codes inside Jupyter
Since we can run the cell one by one at any order we want, Jupyter is an excellenet tool for us to experiment with different things in Python. After writing codes to the notebook, don’t forget to save it (as a ‘.ipynb’ file) and share it with others!
Do a Machine Learning Experiment
Download this notebook from the repository and open it in VSCode. In this notebook, we perform a simple linear regression with scikit-learn, a popular Python package for machine learning. Linear regression can be thought as drawing the best-fit line that matches the given data as closely as possible. If you want to know more about the mathematics behind linear regression and other methods of statistical learning, you may read An Introduction to Statistical Learning with Applications in R.
We go through the steps here and you can try it on your own.
Note: If you receive a warning when opening the notebook, simply click ‘Trust’ to dismiss the warning.
First, we install or update the libraries that we are going to use.
Note: apt-get and pip3 are Linux commands, and Linux commands can be executed inside the notebook. To execute a Linux command, add an exclamation mark before the command.
Then, we import the necessary libraries.
After that, we setup matplotlib for plotting graphs.
Notice that %matplotlib inline is a Jupyter command. It allows us to plot graphs with matplotlib right inside the Jupyter notebook.
Next, we generate some data.
X is an array containing some random numbers from 0 to 1. Each number in y is created by multiplying the corresponding number in X by 3, and then adding 4 and a random number to the product. Therefore, the best-fit line should look like ‘y = 3x + 4’. Let’s plot the data and see if this is the case.
Plot a graph inside Jupyter
Let’s do the linear regression now. Scikit-learn makes this task extremely simple. We only need to create a LinearRegression model object, and fit the model with the training data. We can look at the intercept and the slope of the straight line after the fitting.
We can use the model to make predictions on new data.
Finally, we can plot the results and compare the models and the training data.
The best-fit line
Conclusion
Although the Raspberry Pi is not a powerful computer that can train giant machine learning models, it is definitely enough for exploring many machine learning methods that are not computationally intensive. Using VSCode with the Python extension and the Jupyter notebook makes the Raspberry Pi an excellent platform for studying these machine learning strategies. We will explore machine learning further with these tools in future tutorials.
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Visual Studio Code on Raspberry Pi
You can run Visual Studio Code on Raspberry Pi devices.
By downloading and using Visual Studio Code, you agree to the license terms and privacy statement.
Visual Studio Code is officially distributed via the Raspberry Pi OS (previously called Raspbian) APT repository, in both 32-bit and 64-bit variants.
You can install it by running:
Running VS Code
After installing the VS Code package, you can run VS Code by typing code in a terminal or launching it via the Programming menu.
Your Raspberry Pi should handle updating VS Code in the same way as other packages on the system:
You can always check when a new release is available in our Updates page.
VS Code is supported on these Raspberry Pi models running a 32-bit or 64-bit version of Raspberry Pi OS:
- Raspberry Pi 3 Model B/B+
- Raspberry Pi 4 Model B
- Raspberry Pi 400
While 1 GB of memory (RAM) meets the minimum system requirements, users will benefit from installing VS Code on a Raspberry Pi 4 with more memory.
First-generation Raspberry Pi modules and Raspberry Pi Zero are not supported as they only include an ARMv6 CPU.
Workaround for poor performance
VS Code on Raspberry Pi 4 may be slow with the default setup. A workaround is to disable hardware (GPU) acceleration in VS Code:
- Open the VS Code argv.json file using the Preferences: Configure Runtime Arguments command.
- Set «disable-hardware-acceleration»: true .
- Restart VS Code.
The «disable-hardware-acceleration»: true runtime argument switch has the effect of passing the —disable-gpu command-line argument on VS Code startup.
Once you have installed VS Code, these topics will help you learn more about it:
Как на расбери пай установить visual studio
Вы можете работать в среде Visual Studio Code на платках Raspberry Pi.
[Системные требования]
VS Code поддерживает следующие модели Raspberry Pi, работающие в 32-битной или 64-битной операционной системе Raspberry Pi OS:
Raspberry Pi 3 Model B/B+
Raspberry Pi 4 Model B
Raspberry Pi 400
Для работы требуется минимум 1 GB памяти RAM, и чем больше памяти, тем лучше.
Первые модели Raspberry Pi и Raspberry Pi Zero не поддерживаются, потому что на них установлен только ARMv6 CPU.
[Установка]
Visual Studio Code официально распространяется через APT-репозиторий Raspberry Pi OS (ранее эта операционная система называлась Raspbian), в обоих вариантах 32 и 64 бит. Для установки выполните следующие команды:
Обновление. VS Code на Raspberry Pi обновляется точно также, как и все другие пакеты в системе:
Вы всегда можете проверить наличие новых релизов на страничке обновлений [2].
[Запуск VS Code]
Ярлычок для запуска Visual Studio Code находится в главном меню, раздел Programming, либо можно в терминале выполнить команду code .
[Повышение производительности]
VS Code на Raspberry Pi 4 может после установки по умолчанию работать довольно медленно. Есть способ повысить быстродействие, если в VS Code запретить аппаратное ускорение (GPU, hardware acceleration):
1. Откройте файл VS Code argv.json, используя меню Preferences -> Configure Runtime Arguments.
2. Установите в true опцию «disable-hardware-acceleration».
3. Перезапустите VS Code.
Опция «disable-hardware-acceleration»: true при запуске дает тот же эффект, что передача аргумента —disable-gpu в командную строк запуска VS Code.
Описание дополнительных компонентов VS Code, интерфейса пользователя и настроек см. по ссылкам [3, 4, 5].
