Virtual Environments (Livehacking Screenplay)

First: Help

$ python -m venv --help
usage: venv [-h] [--system-site-packages] [--symlinks | --copies] [--clear] [--upgrade] [--without-pip] [--prompt PROMPT] ENV_DIR [ENV_DIR ...]

Creates virtual Python environments in one or more target directories.

positional arguments:
  ENV_DIR               A directory to create the environment in.

optional arguments:
  -h, --help            show this help message and exit
  --system-site-packages
                        Give the virtual environment access to the system site-packages dir.
  --symlinks            Try to use symlinks rather than copies, when symlinks are not the default for the platform.
  --copies              Try to use copies rather than symlinks, even when symlinks are the default for the platform.
  --clear               Delete the contents of the environment directory if it already exists, before environment creation.
  --upgrade             Upgrade the environment directory to use this version of Python, assuming Python has been upgraded in-place.
  --without-pip         Skips installing or upgrading pip in the virtual environment (pip is bootstrapped by default)
  --prompt PROMPT       Provides an alternative prompt prefix for this environment.

Once an environment has been created, you may wish to activate it, e.g. by sourcing an activate script in its bin directory.

Setup

Create a virtual environment my_venv in directory /home/jfasch/my_venv:

$ python -m venv /home/jfasch/my_venv
$ tree /home/jfasch/my_venv
/home/jfasch/my_venv/
├── bin
│   ├── activate
│   ├── activate.csh
│   ├── activate.fish
│   ├── Activate.ps1
│   ├── easy_install
│   ├── easy_install-3.8
│   ├── pip
│   ├── pip3
│   ├── pip3.8
│   ├── python -> /usr/bin/python
│   └── python3 -> python
├── include
├── lib
│   └── python3.8
... much ...

Note

What is referred to by /home/jfasch/my_venv is a directory. You may choose whatever path you want (e.g. C:\Users\Student\my_venv on Windows). Take care though that you have write permissions, or the creation will fail with a descriptive error.

  • Hm. Looks like the Python environment has been duplicated.

  • bin/python, bin/pip, lib/python3.8

  • Point paths to it?

  • ⟶ activating

Note

Debian is different

$ python3 -m venv /home/jfasch/my_venv The virtual environment was not created successfully because ensurepip is not available. On Debian/Ubuntu systems, you need to install the python3-venv package using the following command.

apt-get install python3-venv

You may need to use sudo with that command. After installing the python3-venv package, recreate your virtual environment.

Failing command: [‘/home/jfasch/my_venv/bin/python3’, ‘-Im’, ‘ensurepip’, ‘–upgrade’, ‘–default-pip’]

Unixen
$ . /home/jfasch/my_venv/bin/activate
(my_venv) $
Doze, cmd.exe
C:\Users\Student\my_venv\Scripts\activate.bat
Doze, Powershell
PS C:\> \Users\Student\my_venv\Scripts\Activate.ps1

  • A-ha! Prompt.

  • What are the effects?

Python version sandboxed
(my_venv) $ type python
python is /home/jfasch/my_venv/bin/python
$PATH
(my_venv) $ echo $PATH
/home/jfasch/my_venv/bin:...(original PATH)...
No pip installed packages
(my_venv) $ pip list
Package    Version
---------- -------
pip        20.2.2
setuptools 41.6.0

Local Package Installation

(my_venv) $ pip install sklearn
Collecting sklearn
  Using cached sklearn-0.0.tar.gz (1.1 kB)
Collecting scikit-learn
... roedel ...

(my_venv) $ pip list
Package       Version
------------- -------
joblib        0.16.0
numpy         1.19.1
pip           20.2.2
scikit-learn  0.23.2
scipy         1.5.2
setuptools    41.6.0
sklearn       0.0
threadpoolctl 2.1.0

Freezing an Environment

Same information as in pip list (except pip and setuptools which are built-in) …

(my_venv) $ pip freeze
joblib==0.16.0
numpy==1.19.1
scikit-learn==0.23.2
scipy==1.5.2
sklearn==0.0
threadpoolctl==2.1.0

Save to a file. Usually called requirements.txt, but not necessarily so.

(my_venv) $ pip freeze > ~/tmp/requirements.txt

Destroying an Environment

(my_venv) $ deactivate
$ rm -rf my_venv/

Recreating an Environment

$ python -m venv my_recreated_venv
$ . ./my_recreated_venv/bin/activate
(my_recreated_venv) $
(my_recreated_venv) $ pip install -r ~/tmp/requirements.txt
... roedel ...
(my_recreated_venv) $ pip list
Package       Version
------------- -------
joblib        0.16.0
numpy         1.19.1
pip           19.3.1
scikit-learn  0.23.2
scipy         1.5.2
setuptools    41.6.0
sklearn       0.0
threadpoolctl 2.1.0

Using Virtual Environments With Visual Studio Code

Here is an explanation of the matter. In short: persuade VS Code to use the Python interpreter from your virtual environment - that one has all the paths VS Code needs to display help.