Change python3.10 to your installed Python version in the command. If you have never used venv, you should first install its dependencies on your computer with the following command. You can either start by creating an empty environment or mention the python version and packages you need at the time of creation itself. Venv is the recommended way to create a virtual environment in Python and it comes preinstalled. Create conda environment Conda centrally manages the environments you create, so, you don’t have to bother about creating a folder for specific environments yourself. Therefore, it is advisable to use a virtual environment for your Python projects. If you don’t use virtual environments, you don’t have any control on the versions of the packages you have used in your project, which is problematic when you try to run your software on a different machine. Think of a virtual environment as an isolated room that has minimal dependencies. It’s an isolated Python environment that has its own tools and libraries independent of the main setup. This is where a virtual environment comes in handy. As Linux comes with a preinstalled version of Python and uses different packages to run the operating system, manually installed packages in the global scope can disrupt its functioning. If you have multiple versions of Python on your system, you can select a specific Python version by running python3 or whichever version you want. venv will usually install the most recent version of Python that you have available. If you use the default package installer, pip, or pip3 to install Python libraries and packages, it will install the packages globally. The module used to create and manage virtual environments is called venv. Creating this conda environment creates a new conda. Python is not good at managing dependencies. where condaenvtf22 is the name of the new conda environment and 3.7.10 is the Python version installed. Sudo update-alternatives -config python Manage Python Projects With Virtual Environments We can basically set up one or more environments (or envs) with specific Python versions, modules and dependencies, and we can swap back and forth between them.
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