venv-pack is a command-line tool for packaging virtual environments for distribution. This is useful for deploying code in a consistent environment.

Supports virtual environments created using:

  • venv (part of the standard library, preferred method)
  • virtualenv (older tool, Python 2 compatible)

See conda-pack for a similar tool made for Conda environments.


Install from Conda-Forge:

$ conda install -c conda-forge venv-pack

Install from PyPI:

$ pip install venv-pack

Install from source:

venv-pack is available on github and can always be installed from source.

$ pip install git+

Command-line Usage

venv-pack is primarily a command-line tool. Full CLI docs can be found here.

One common use case is packing an environment on one machine to distribute to other machines as part of a deployment process.

On the source machine

# Pack the current environment into my_env.tar.gz
$ venv-pack -o my_env.tar.gz

# Pack an environment located at an explicit path into my_env.tar.gz
$ venv-pack -p /explicit/path/to/my_env

On the target machine

# Unpack environment into directory `my_env`
$ mkdir -p my_env
$ tar -xzf my_env.tar.gz -C my_env

# Use python without activating the environment. Most python
# libraries will work fine, but scripts (e.g. ipython) will fail.
$ ./my_env/bin/python

# Activate the environment. This adds `my_env/bin` to your path
$ source my_env/bin/activate

# Run python from in the environment
(my_env) $ python

# Scripts now work fine
(my_env) $ ipython --version

# Deactivate the environment to remove it from your path
(my_env) $ deactivate

API Usage

venv-pack also provides a Python API, the full documentation of which can be found here. The API mostly mirrors that of the venv-pack command-line. Repeating the examples from above:

import venv_pack

# Pack the current environment into my_env.tar.gz

# Pack an environment located at an explicit path into my_env.tar.gz


This tool is new, and has a few caveats.

  • Python is not packaged with the environment, but rather symlinked in the environment. This is useful for deployment situations where Python is already installed on the machine, but the required library dependencies may not be.
  • Windows is not currently supported (should be easy to fix, contributions welcome!)
  • The os type where the environment was built must match the os type of the target. This means that environments built on windows can’t be relocated to linux.