vpython
is a tool, written in Go, which enables the simple and easy invocation of Python code in VirtualEnv environments.
vpython
is a simple Python bootstrap which (almost) transparently wraps a Python interpreter invocation to run in a tailored VirtualEnv environment. The environment is expressed by a script-specific configuration file. This allows each Python script to trivially express its own package-level dependencies and run in a hermetic world consisting of just those dependencies.
When invoking such a script via vpython
, the tool downloads its dependencies and prepares an immutable VirtualEnv containing them. It then invokes the script, now running in that VirutalEnv, through the preferred Python interpreter.
vpython
does its best not to use hacky mechanisms to achieve this. It uses an unmodified VirtualEnv package, standard setup methods, and local system resources. The result is transparent canonical VirtualEnv environment bootstrapping. vpython
is also safe for concurrent invocation, using safe filesystem-level locking to perform any environment setup and management.
vpython
itself is very fast. The wheel downloads and VirtualEnvs may also be cached and re-used, optimally limiting the runtime overhead of vpython
to just one initial setup per unique environment.
For the standard case, employing vpython
is as simple as:
First, create and upload Python wheels for all of the packages that you will need. This is done in an implementation-specific way (e.g., upload wheels as packages to CIPD).
Once the packages are available:
vpython
to PATH
.python
to vpython
.Using vpython
offers several benefits to direct Python invocation, especially when vendoring packages. Notably, with vpython
:
sys.path
manipulation is needed to load vendored or imported packages.vpython
and built on fast and secure Google Cloud Platform technologies.VirtualEnv offers several benefits over system Python. Primarily, it is the
By using the same environemnt everywhere, Python invocations become reproducible. A tool run on a developer's system will load the same versions of the same libraries as it will on a production system. A production system will no longer fail because it is missing a package, or because it has the wrong version.
A direct mechanism for vendoring, sys.path
manipulation, is nuanced, buggy, and unsupported by the Python community. It is difficult to get right on all platforms in all environments for all packages. A notorious example of this is protobuf
and other domain-bound packages, which actively fight sys.path
inclusion. Using VirtualEnv means that any compliant Python package can trivially be included into a project.
CIPD is a cross-platform service and associated tooling and packages used to securely fetch and deploy immutable “packages” (~= zip files) into the local file system. Unlike “package managers” it avoids platform-specific assumptions, executable “hooks”, or the complexities of dependency resolution. vpython
uses this as a mechanism for housing and deploying wheels.
infrastructure package deployment system. It is simple, accessible, fast, and backed by resilient systems such as Google Storage and AppEngine.
Unlike pip
, a CIPD package is defined by its content, enabling precise package matching instead of fuzzy version matching (e.g., numpy >= 1.2
, and numpy == 1.2
both can match multiple numpy
packages in pip
).
CIPD also supports ACLs, enabling privileged Python projects to easily vendor sensitive packages.
A Python wheel is a simple binary distrubition of Python code. A wheel can be generic (pure Python) or system- and architecture-bound (e.g., 64-bit Mac OSX).
Wheels are prefered over eggs because they come packaged with compiled binaries. This makes their deployment simple (unpack via pip
) and reduces system requirements and variation, since local compilation is not needed.
The increased management burden of maintaining separate wheels for the same package, one for each architecture, is handled naturally by CIPD, removing the only real pain point.
This section contains recommendations for building or uploading wheel CIPD packages, including platform-specific guidance.
CIPD wheel packages are CIPD packages that contain Python wheels. A given CIPD package can contain multiple wheels for multiple platforms, but should only contain one version of any given package for any given architecture/platform.
For example, you can bundle a Windows, Linux, and Mac OSX version of numpy
and coverage
in the same CIPD package, but you should not bundle numpy==1.11
and numpy==1.12
in the same package.
The reason for this is that vpython
identifies which wheels to install by scanning the contents of the CIPD package, and if multiple versions appear, there is no clear guidance about which should be used.
Use the m
ABI suffix and the macosx_...
platform. vpython
installs wheels with the --force
flag, so slight binary incompatibilities (e.g., specific OSX versions) can be glossed over.
coverage-4.3.4-cp27-cp27m-macosx_10_10_x86_64.whl
Use wheels with the mu
ABI suffix and the manylinux1
platform. For example:
coverage-4.3.4-cp27-cp27mu-manylinux1_x86_64.whl
Use wheels with the cp27m
or none
ABI tag. For example:
coverage-4.3.4-cp27-cp27m-win_amd64.whl
vpython
can be invoked by replacing python
in the command-line with vpython
.
vpython
works with a default Python environment out of the box. To add vendored packges, you need to define an environment specification file that describes which wheels to install.
An environment specification file is a text protobuf defined as Spec
here. An example is:
# Any 2.7 interpreter will do. python_version: "2.7" # Include "numpy" for the current architecture. wheel { name: "infra/python/wheels/numpy/${platform}-${arch}" version: "version:1.11.0" } # Include "coverage" for the current architecture. wheel { name: "infra/python/wheels/coverage/${platform}-${arch}" version: "version:4.1" }
This specification can be supplied in one of three ways:
vpython
(-spec
).test_runner.py
, vpython
will look for test_runner.py.vpython
next to it and load the environment from there.vpython
will scan the main entry point for sentinel text and, if present, load the specification from that.VPYTHON_VENV_SPEC_PATH
environment variable. This is set by a vpython
invocation so that chained invocations default to the same environment.vpython
has several levels of caching that it employs to optimize setup and invocation overhead.
Once a VirtualEnv specification has been resolved, its resulting pinned specification is hashed and used as a key to that VirtualEnv. Other vpython
invocations expressing hte same environment will naturally re-use that VirtualEnv instead of creating their own.
Download mechanisms (e.g., CIPD) can optionally include a package cache to avoid the overhead of downloading and/or resolving a package multiple times.
vpython
is a natural replacement for python
in the command line:
python ./foo/bar/baz.py -d --flag value arg arg whatever
Becomes:
vpython ./foo/bar/baz.py -d --flag value arg arg whatever
The vpython
tool accepts its own command-line arguments. In this case, use a --
seprator to differentiate between vpython
options and python
options:
vpython -spec /path/to/spec.vpython -- ./foo/bar/baz.py
If your script uses implicit specification (file or inline), replacing python
with vpython
in your shebang line will automatically work.
#!/usr/bin/env vpython