Both types of environment allow you to install packages without affecting other environments. There are two types of environments that you can create for your workspace: virtual and conda environments. You typically want to create an environment for each workspace. Any packages that you install or uninstall affect the global environment and all programs that you run within it.ĭo note that if you install packages into your global environment, though, in time it will become crowded with potentially unrelated or unexpected packages and make it difficult to properly test an application. For example, if you just run python, python3, or py at a new terminal (depending on how you installed Python), you're running in that interpreter's global environment. Python environments Global environmentsīy default, any Python interpreter installed runs in its own global environment. Note: If you'd like to become more familiar with the Python programming language, review More Python resources. An "environment" in Python is the context in which a Python program runs and consists of an interpreter and any number of installed packages. This article discusses the helpful Python environments features available in Visual Studio Code. Configure IntelliSense for cross-compilingĮdit Using Python environments in VS Code.To install conda-pack, make sure you are in the root or baseĮnvironment so that it is available in sub-environments. Specific and that the target computer must have the same platform and Keep in mind that conda-pack is both platform and operating system Packages from their respective repositories to create an environment. This is useful when you want to reproduce an environment Which includes all the binaries of the packages installed in theĮnvironment. so in case you dont have access to internet, then you can use conda pack.Ĭonda-pack is a command line tool that archives a conda environment, All other previous methods require internet connection. If you plan on getting an exact copy of your current environment and then move it to another machine with the same platform and OS, without redownloading all packages again from Internet (good for offline machines/behind firewalls). # as long as some version of Python is already installed on the machine. # Note that this command can also be run without activating the environment # Cleanup prefixes from in the active environment.
# libraries will work fine, but things that require prefix cleanups # Use Python without activating or fixing the prefixes. # Pack environment located at an explicit path into my_Īnd to restore it on the other machine(s): # Unpack environment into directory `my_env` Conda-forge: conda install -c conda-forge conda-packīacking up: # Pack environment my_env into my_
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