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Version: 2.14 (deprecated)

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
pants.toml
[python-infer]
assets = <bool>
default: 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 a false positive, you can either put # pants: no-infer-dep on the line of the string or 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
pants.toml
[python-infer]
assets_min_slashes = <int>
default: 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
pants.toml
[python-infer]
conftests = <bool>
default: 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
pants.toml
[python-infer]
entry_points = <bool>
default: 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
pants.toml
[python-infer]
imports = <bool>
default: 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.

init_files​

--python-infer-init-files=<InitFilesInference>
PANTS_PYTHON_INFER_INIT_FILES
pants.toml
[python-infer]
init_files = <InitFilesInference>
one of: always, content_only, never
default: content_only

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 set to never or content_only, Pants will still always include any ancestor __init__.py files in the sandbox. 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.

By default, Pants only adds a "proper" dependency if there is content in the __init__.py file. This makes sure that dependencies are added when likely necessary to build, while also avoiding adding unnecessary dependencies. While accurate, those unnecessary dependencies can complicate setting metadata like the interpreter_constraints and resolve fields.

string_imports​

--[no-]python-infer-string-imports
PANTS_PYTHON_INFER_STRING_IMPORTS
pants.toml
[python-infer]
string_imports = <bool>
default: 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 a false positive, you can either put # pants: no-infer-dep on the line of the string or 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
pants.toml
[python-infer]
string_imports_min_dots = <int>
default: 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
pants.toml
[python-infer]
unowned_dependency_behavior = <UnownedDependencyUsage>
one of: error, warning, ignore
default: warning

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

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