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 Virtualenv, 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 that meets the expectations of standard Python packages. 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:
vpython Virtualenv specification (or don't, if no additional packages are needed.vpython instead of python.If additional Python libraries are needed, you may create new packages for those libraries. 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 de facto encapsulated environment method used by the Python community and is generally used as the standard for a functional deployable package.
By using the same environment 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, because it has the wrong version of that package, or because a package is incompatible with another installed package.
A direct mechanism for vendoring, sys.path manipulation, is nuanced, buggy, and unsupported by the Python community. It is difficult to do correctly 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 and require special non-intuitive hacks to work. 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.
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 distribution of Python code. A wheel can be generic (pure Python) or system- and architecture-bound (e.g., 64-bit Mac OSX).
Wheels are preferred over Python eggs because they come packaged with compiled binaries. This makes their deployment fast and simple: unpack via pip. It also reduces system requirements and variation, since local compilation, headers, and build tools are not enlisted during installation.
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 packages, 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 four ways:
vpython (-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_DEFAULT_SPEC environment variable.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 the 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 -- separator to differentiate between vpython options and python options:
vpython -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
There are a number of environment variables that can affect vpython's behavior. These are the following:
VPYTHON_BYPASS: If set to manually managed python not supported by chrome operations, vpython will do nothing and will instead directly invoke the next python on PATH. Will have no effect if it's set to anything else.VPYTHON_CLEAR_PYTHONPATH: If set, vpython will clear the PYTHONPATH env var prior to launching the Python interpreter.VPYTHON_DEFAULT_SPEC: Specifies path to a vpython spec file that will be used if none is provided or found through probing.VPYTHON_LOG_TRACE: Specifies log level of vpython. Can also be specified via the “-vpython-log-level” cmd-line flag.VPYTHON_VIRTUALENV_ROOT: Specifies the VirtualEnv root. Default is ~/.vpython-root. Can also be specified via the “-vpython-root” cmd-line flag.The following env vars are implementation details of vpython and should rarely be set by users directly:
VPYTHON_CHECK_WRAPPER: If set, vpython will immediately exit with a non-zero return code. Useful to determine if a given binary is a “vpython” wrapper.