This will create a virtual environment without any of the packages installed on your Raspberry Pi. If you want to keep the packages already installed, add the "--system-site-packages" option to the virtualenv command.
On my system, there are only 5 packages present in flaskenv, while in a virtualenv created with --system-site-packages there are a bit over 10. Remember that installing yolk only affects the current virtualenv, so to run it again on your base system, you will need to do easy_install again.
A Python virtual environment allows you to build individual environments for your programming projects to install the required packages for the projects. Keeping environments separate for each project allows for the minimal dependencies to be installed to run the project. It also prevents package versions from clashing across projects. Finally, it makes it a little bit easier to share the environment requirements with someone else who wants to run your Python project.
If you look into the directory that was created by the python3-venv module, you will find everything that is needed to make the terminal session run within an isolated Python 3 environment. Any Python 3 dependencies that are installed for this virtual environment will be contained within this directory.
Once you had activated your virtual environment, you can then install whatever dependencies that your Python 3 application will need with the pip command. For example, I can run the following command to install Flask into my virtual environment:
Since I am running this examples from Raspbian lite, the pip is not installed by default. Given these points, you can be sure that your current shell terminal had deactivated the Python 3 virtual environment
Tip: The location of virtualenvwrapper may differ, depending on how you setup the tool. If you ran it as we did, your installation is located in ~/.local/bin/. If you ran system-wide (with sudo), then the path to the wrapper is /usr/local/bin. Locate the correct location before going further - you might want to run the following to find the correct location:
Tip: on Windows, virtualenvwrapper won't work. Check the Windows specific version - virtualenvwrapper-win instead. If you prefer PowerShell, then it's better to install virtualenvwrapper-powershell instead.
If you are using pyenv as a virtual environment tool of choice, we recommend to add a nice plugin: pyenv-pip-update. This allows you to upgrade pip in all your environments at once, which is a rather common annoyance otherwise.
We do however recommend to setup your Python on WSL - Windows Subsystem for Linux. pyenv-win is not so straightforward, when it comes to virtual environments, while pyenv has amazing virtual environment support out of the box.
I am trying to create a virtual environment that can access GPIO pins. In order to do that, I need access to RPi.GPIO in the environment. I am unable to install that into the virtual environment. I managed to make it work by allowing the environment to have access to system packages, but I would like to avoid that.
For development machines like Mac/Windows/Ubuntu/etc., we recommend the PyCharm IDE, as it automatically makes/manages virtual environments for you, along with a bunch of other benefits. Alternatively, conda, pipenv, or virtualenv could be used directly (and/or with your preferred IDE).
For installations on resource-constrained systems, such as the Raspberry Pi or other small Linux systems, we recommend conda, pipenv, or virtualenv. To set up a virtual environment with virtualenv, run virtualenv venv && source venv/bin/activate.
However, the specifics for each virtual machine tool differ. We've produced guides for installing Linux in a virtual machine. As Raspberry Pi Desktop is based on Linux, you'll find everything you need to know in these guides:
This will install Rhasspy inside a virtual environment at $PWD/.venv by default with all of the supported speech to text engines and supporting tools. When installation is finished, copy rhasspy.sh somewhere in your PATH and rename it to rhasspy.
This article discusses the helpful Python environments features available in Visual Studio Code. An "environment" in Python is the context in which a Python program runs and consists of an interpreter and any number of installed packages.
There are two types of environments that you can create for your workspace: virtual and conda environments. Both types of environment allow you to install packages without affecting other environments. This lets you isolate what packages you install for your workspace so that they don't interfere with your needs in another workspace.
A virtual environment is a built-in way to create an environment to isolate the packages you install per workspace. A virtual environment creates a folder that contains a copy (or symlink) to a specific interpreter. When you install packages into a virtual environment it will end up in this new folder so that they are not interspersed with other packages used or needed by other workspaces.
Note: The command will also install necessary packages outlined in a requirements/dependencies file, such as requirements.txt, pyproject.toml, or environment.yml, located in the project folder. It will also add a .gitignore file to the virtual environment to help prevent you from accidentally committing the virtual environment to source control.
Tip: Make sure to update your source control settings to prevent accidentally committing your virtual environment (in for example .gitignore). Since virtual environments are not portable, it typically does not make sense to commit them for others to use.
The Python extension automatically detects existing conda environments. We recommend you install a Python interpreter into your conda environment, otherwise one will be installed for you after you select the environment. For example, the following command creates a conda environment named env-01 with a Python 3.9 interpreter and several libraries:
The Python: Select Interpreter command displays a list of available global environments, conda environments, and virtual environments. (See the Where the extension looks for environments section for details, including the distinctions between these types of environments.) The following image, for example, shows several Anaconda and CPython installations along with a conda environment and a virtual environment (env) that's located within the workspace folder:
While it is possible to use pip without a virtual environment, it is not advised:virtual environments create a clean Python environment that does not interferewith any existing system installation, can be easily removed, and contain onlythe package versions your application needs. They help avoid a commonchallenge known asdependency hell.
After partitioning your disk, the installation wizard will ask you if you want to install the GRUB boot loader on your newly created hard drive. This is perfectly safe to do, as again, this is a new virtual hard drive, with nothing previously installed on it.
You must activate the virtual environment with the above command every time you open a newshell to run, install or update Red. You can check out other commands like pyenv local andpyenv global if you wish to keep the virtualenv activated all the time.
The use of virtual Python environments is highly recommended but why?What is the link between the module venv and thevirtualenv utility? And why are pip andsetuptools playing hide and seek as I try to install them on?Indeed should they be installed at all?
This posting is about virtual Python environments on three differentLinux systems.Ubuntu 17.10 to Ubuntu 18.04 (Mint 20.1) on a relatively powerful desktop computer.Raspbian 9.1 Lite (Stretch) to Raspberry Pi OS (Buster) on a Raspberry Pi 3.Armbian 5.31 (DietPi) on an Orange Pi Zero.
Most Python projects are not standalone monoliths. They use standardlibraries provided with Python itself. Often they also use third partylibraries. But that can be tricky. Python project 1 could require theinstallation version 2.18.4 of requests a commonly usedhttp library. What happens if Python project 2 decides to install theolder 1.2.1 version of requests? It could clobber the newerversion 2.18.4 and then Python project 1 might be broken. Virtual environmentsare meant to solve this problem. Each virtual environment contains allneeded libraries for a Python project in a "box" that is independent ofall other virtual environments.
At this point, reading A non-magical introduction to Pip and Virtualenv for Pythonbeginners by Jamie Matthews would be a good idea. Just remember, thattext was written in 2013 and since then venv is preferredto virtualenv in Python 3x (among other things,virtualenv copies the compiler binaries to the virtualenvironment while venv creates links). Also, I will not be installingpip et al globally, but only in virtual environments.More about that latter.
Python packages themselves are divided into two categories: systemand site. The first refers to packages that are part of the standardPython library, while later refers to packages from third parties. Thesewill be installed in an activated the virtual environment automatically bypip.Python 2 and 3 Installed on Ubuntu 17.10 and Raspbian 9.1 Ubuntu 17.10 comes with two version of Python installed: 2.7.14 and 3.6.3. To quote thepython.org wiki Python 2.x is legacy, Python 3.x isthe present and future of the language. One would think that version 3can handle any version 2 code. Generally, speaking that is true but notalways. Version 3 of Python introduce new keywords. If a version 2 scriptused any of these words as variable names, then these will have to bechanged. Also, some version 2 libraries may not have been ported to version3. I am new to Python, so I have no investment in the older version and willbe using version 3. 2b1af7f3a8