Virtual Environment Stacks¶
Machine learning and AI libraries for Python are big. Really big. Nobody wants to download and install multiple copies of PyTorch or CUDA if they can reasonably avoid it.
venvstacks allows you to package Python applications and all their dependencies into a
portable, deterministic format, without needing to include copies of these large Python
frameworks in every application archive.
It achieves this by using Python’s sitecustomize.py environment setup feature
to chain together three layers of Python virtual environments:
“Runtime” layers: environments containing the desired version of a specific Python interpreter
“Framework” layers: environments containing desired versions of key Python frameworks
“Application” layers: environments containing components to be launched directly
Application layer environments may include additional unpackaged Python launch modules or
packages for invocation with python’s -m switch.
While the layers are archived and published separately, their dependency locking is integrated, allowing the application layers to share dependencies installed in the framework layers, and the framework layers to share dependencies installed in the runtime layers.
Refer to the Project Overview for an example of specifying, locking, building, and publishing a set of environment stacks.
venvstacks is available from the Python Package Index,
and can be installed with pipx (or similar tools):
$ pipx install venvstacks
Alternatively, it can be installed as a user level package (although this may make future Python version upgrades more irritating):
$ pip install --user venvstacks