python-infer
Options controlling which dependencies will be inferred for Python targets.
Backend: pants.backend.python
Config section: [python-infer]
Basic options
assets
--[no-]python-infer-assets
PANTS_PYTHON_INFER_ASSETS
[python-infer]
assets = <bool>
False
Infer a target's asset dependencies based on strings that look like Posix filepaths, such as those given to open
or pkgutil.get_data
. To ignore any false positives, put !{bad_address}
in the dependencies
field of your target.
assets_min_slashes
--python-infer-assets-min-slashes=<int>
PANTS_PYTHON_INFER_ASSETS_MIN_SLASHES
[python-infer]
assets_min_slashes = <int>
1
If --assets is True, treat valid-looking strings with at least this many forward slash characters as potential assets. E.g. 'data/databases/prod.db'
will be treated as a potential candidate if this option is set to 2 but not to 3.
conftests
--[no-]python-infer-conftests
PANTS_PYTHON_INFER_CONFTESTS
[python-infer]
conftests = <bool>
True
Infer a test target's dependencies on any conftest.py files in the current directory and ancestor directories.
entry_points
--[no-]python-infer-entry-points
PANTS_PYTHON_INFER_ENTRY_POINTS
[python-infer]
entry_points = <bool>
True
Infer dependencies on targets' entry points, e.g. pex_binary
's entry_point
field, python_awslambda
's handler
field and python_distribution
's entry_points
field.
imports
--[no-]python-infer-imports
PANTS_PYTHON_INFER_IMPORTS
[python-infer]
imports = <bool>
True
Infer a target's imported dependencies by parsing import statements from sources.
To ignore a false positive, you can either put # pants: no-infer-dep
on the line of the import or put !{bad_address}
in the dependencies
field of your target.
inits
--[no-]python-infer-inits
PANTS_PYTHON_INFER_INITS
[python-infer]
inits = <bool>
False
Infer a target's dependencies on any __init__.py
files in the packages it is located in (recursively upward in the directory structure).
Even if this is disabled, Pants will still include any ancestor __init__.py
files, only they will not be 'proper' dependencies, e.g. they will not show up in /home/josh/work/scie-pants/dist/pants dependencies
and their own dependencies will not be used.
If you have empty __init__.py
files, it's safe to leave this option off; otherwise, you should enable this option.
string_imports
--[no-]python-infer-string-imports
PANTS_PYTHON_INFER_STRING_IMPORTS
[python-infer]
string_imports = <bool>
False
Infer a target's dependencies based on strings that look like dynamic dependencies, such as Django settings files expressing dependencies as strings.
To ignore any false positives, put !{bad_address}
in the dependencies
field of your target.
string_imports_min_dots
--python-infer-string-imports-min-dots=<int>
PANTS_PYTHON_INFER_STRING_IMPORTS_MIN_DOTS
[python-infer]
string_imports_min_dots = <int>
2
If --string-imports is True, treat valid-looking strings with at least this many dots in them as potential dynamic dependencies. E.g., 'foo.bar.Baz'
will be treated as a potential dependency if this option is set to 2 but not if set to 3.
unowned_dependency_behavior
--python-infer-unowned-dependency-behavior=<UnownedDependencyUsage>
PANTS_PYTHON_INFER_UNOWNED_DEPENDENCY_BEHAVIOR
[python-infer]
unowned_dependency_behavior = <UnownedDependencyUsage>
error, warning, ignore
default:
ignore
How to handle imports that don't have an inferrable owner.
Usually when an import cannot be inferred, it represents an issue like Pants not being properly configured, e.g. targets not set up. Often, missing dependencies will result in confusing runtime errors like ModuleNotFoundError
, so this option can be helpful to error more eagerly.
To ignore any false positives, either add # pants: no-infer-dep
to the line of the import or put the import inside a try: except ImportError:
block.
Advanced options
None
Deprecated options
None
Related subsystems
None