python-infer

imports

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.

string_imports

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

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.

assets

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

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.

inits

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 ./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.

conftests

Infer a test target's dependencies on any conftest.py files in the current directory and ancestor directories.

entry_points

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.

unowned_dependency_behavior

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.