refactoring setup

docs/source/conf.py

@@ -13,7 +13,6 @@
 # documentation root, use os.path.abspath to make it absolute, like shown here.
 
 # import m2r
-import builtins
 import glob
 import os
 import shutil
@@ -27,10 +26,13 @@
 
 FOLDER_GENERATED = 'generated'
 SPHINX_MOCK_REQUIREMENTS = int(os.environ.get('SPHINX_MOCK_REQUIREMENTS', True))
-if SPHINX_MOCK_REQUIREMENTS:
-    builtins.__LIGHTNING_SETUP__ = True
 
-import pytorch_lightning  # noqa: E402
+try:
+    from pytorch_lightning import info
+except ImportError:
+    # alternative https://stackoverflow.com/a/67692/4521646
+    sys.path.append(os.path.join(PATH_ROOT, "pytorch_lightning"))
+    import info
 
 # -- Project documents -------------------------------------------------------
 
@@ -79,13 +81,13 @@ def _transform_changelog(path_in: str, path_out: str) -> None:
 # -- Project information -----------------------------------------------------
 
 project = 'PyTorch Lightning'
-copyright = pytorch_lightning.__copyright__
-author = pytorch_lightning.__author__
+copyright = info.__copyright__
+author = info.__author__
 
 # The short X.Y version
-version = pytorch_lightning.__version__
+version = info.__version__
 # The full version, including alpha/beta/rc tags
-release = pytorch_lightning.__version__
+release = info.__version__
 
 # -- General configuration ---------------------------------------------------
 
@@ -176,8 +178,8 @@ def _transform_changelog(path_in: str, path_out: str) -> None:
 # documentation.
 
 html_theme_options = {
-    'pytorch_project': pytorch_lightning.__homepage__,
-    'canonical_url': pytorch_lightning.__homepage__,
+    'pytorch_project': info.__homepage__,
+    'canonical_url': info.__homepage__,
     'collapse_navigation': False,
     'display_version': True,
     'logo_only': False,
@@ -279,6 +281,7 @@ def _transform_changelog(path_in: str, path_out: str) -> None:
     'torch': ('https://pytorch.org/docs/stable/', None),
     'numpy': ('https://numpy.org/doc/stable/', None),
     'PIL': ('https://pillow.readthedocs.io/en/stable/', None),
+    'torchmetrics': ('https://torchmetrics.readthedocs.io/en/stable/', None),
 }
 
 # -- Options for todo extension ----------------------------------------------

pytorch_lightning/__init__.py

@@ -2,42 +2,17 @@
 
 import logging
 import os
-import sys
-import time
 
-_this_year = time.strftime("%Y")
-__version__ = '1.3.0dev'
-__author__ = 'William Falcon et al.'
-__author_email__ = '[email protected]'
-__license__ = 'Apache-2.0'
-__copyright__ = f'Copyright (c) 2018-{_this_year}, {__author__}.'
-__homepage__ = 'https://github.com/PyTorchLightning/pytorch-lightning'
-# this has to be simple string, see: https://github.com/pypa/twine/issues/522
-__docs__ = (
-    "PyTorch Lightning is the lightweight PyTorch wrapper for ML researchers."
-    " Scale your models. Write less boilerplate."
+from pytorch_lightning.info import (  # noqa: F401
+    __author__,
+    __author_email__,
+    __copyright__,
+    __docs__,
+    __homepage__,
+    __license__,
+    __version__,
 )
-__long_docs__ = """
-Lightning is a way to organize your PyTorch code to decouple the science code from the engineering.
- It's more of a style-guide than a framework.
 
-In Lightning, you organize your code into 3 distinct categories:
-
-1. Research code (goes in the LightningModule).
-2. Engineering code (you delete, and is handled by the Trainer).
-3. Non-essential research code (logging, etc. this goes in Callbacks).
-
-Although your research/production project might start simple, once you add things like GPU AND TPU training,
- 16-bit precision, etc, you end up spending more time engineering than researching.
- Lightning automates AND rigorously tests those parts for you.
-
-Overall, Lightning guarantees rigorously tested, correct, modern best practices for the automated parts.
-
-Documentation
--------------
-- https://pytorch-lightning.readthedocs.io/en/latest
-- https://pytorch-lightning.readthedocs.io/en/stable
-"""
 _root_logger = logging.getLogger()
 _logger = logging.getLogger(__name__)
 _logger.setLevel(logging.INFO)
@@ -50,32 +25,20 @@
 _PACKAGE_ROOT = os.path.dirname(__file__)
 _PROJECT_ROOT = os.path.dirname(_PACKAGE_ROOT)
 
-try:
-    # This variable is injected in the __builtins__ by the build
-    # process. It used to enable importing subpackages of skimage when
-    # the binaries are not built
-    _ = None if __LIGHTNING_SETUP__ else None
-except NameError:
-    __LIGHTNING_SETUP__: bool = False
-
-if __LIGHTNING_SETUP__:  # pragma: no-cover
-    sys.stdout.write(f'Partial import of `{__name__}` during the build process.\n')  # pragma: no-cover
-    # We are not importing the rest of the lightning during the build process, as it may not be compiled yet
-else:
-    from pytorch_lightning import metrics
-    from pytorch_lightning.callbacks import Callback
-    from pytorch_lightning.core import LightningDataModule, LightningModule
-    from pytorch_lightning.trainer import Trainer
-    from pytorch_lightning.utilities.seed import seed_everything
-
-    __all__ = [
-        'Trainer',
-        'LightningDataModule',
-        'LightningModule',
-        'Callback',
-        'seed_everything',
-        'metrics',
-    ]
+from pytorch_lightning import metrics  # noqa: E402
+from pytorch_lightning.callbacks import Callback  # noqa: E402
+from pytorch_lightning.core import LightningDataModule, LightningModule  # noqa: E402
+from pytorch_lightning.trainer import Trainer  # noqa: E402
+from pytorch_lightning.utilities.seed import seed_everything  # noqa: E402
+
+__all__ = [
+    'Trainer',
+    'LightningDataModule',
+    'LightningModule',
+    'Callback',
+    'seed_everything',
+    'metrics',
+]
 
 # for compatibility with namespace packages
 __import__('pkg_resources').declare_namespace(__name__)

pytorch_lightning/callbacks/progress.py

@@ -39,8 +39,7 @@
 
 class tqdm(_tqdm):
     """
-    Custom tqdm progressbar where we append 0 to floating points/strings to
-    prevent the progress bar from flickering
+    Custom tqdm progressbar where we append 0 to floating points/strings to prevent the progress bar from flickering
     """
 
     @staticmethod

pytorch_lightning/info.py

@@ -0,0 +1,35 @@
+import time
+
+_this_year = time.strftime("%Y")
+__version__ = '1.3.0dev'
+__author__ = 'William Falcon et al.'
+__author_email__ = '[email protected]'
+__license__ = 'Apache-2.0'
+__copyright__ = f'Copyright (c) 2018-{_this_year}, {__author__}.'
+__homepage__ = 'https://github.com/PyTorchLightning/pytorch-lightning'
+# this has to be simple string, see: https://github.com/pypa/twine/issues/522
+__docs__ = (
+    "PyTorch Lightning is the lightweight PyTorch wrapper for ML researchers."
+    " Scale your models. Write less boilerplate."
+)
+__long_docs__ = """
+Lightning is a way to organize your PyTorch code to decouple the science code from the engineering.
+ It's more of a style-guide than a framework.
+
+In Lightning, you organize your code into 3 distinct categories:
+
+1. Research code (goes in the LightningModule).
+2. Engineering code (you delete, and is handled by the Trainer).
+3. Non-essential research code (logging, etc. this goes in Callbacks).
+
+Although your research/production project might start simple, once you add things like GPU AND TPU training,
+ 16-bit precision, etc, you end up spending more time engineering than researching.
+ Lightning automates AND rigorously tests those parts for you.
+
+Overall, Lightning guarantees rigorously tested, correct, modern best practices for the automated parts.
+
+Documentation
+-------------
+- https://pytorch-lightning.readthedocs.io/en/latest
+- https://pytorch-lightning.readthedocs.io/en/stable
+"""

pytorch_lightning/setup_tools.py

@@ -16,7 +16,7 @@
 import re
 from typing import List
 
-from pytorch_lightning import __homepage__, __version__, _PROJECT_ROOT
+_PROJECT_ROOT = os.path.dirname(os.path.dirname(__file__))
 
 
 def _load_requirements(path_dir: str, file_name: str = 'requirements.txt', comment_char: str = '#') -> List[str]:
@@ -40,10 +40,10 @@ def _load_requirements(path_dir: str, file_name: str = 'requirements.txt', comme
     return reqs
 
 
-def _load_readme_description(path_dir: str, homepage: str = __homepage__, version: str = __version__) -> str:
+def _load_readme_description(path_dir: str, homepage: str, version: str) -> str:
     """Load readme as decribtion
 
-    >>> _load_readme_description(_PROJECT_ROOT)  # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
+    >>> _load_readme_description(_PROJECT_ROOT, "", "")  # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
     '<div align="center">...'
     """
     path_readme = os.path.join(path_dir, "README.md")

setup.py

@@ -16,31 +16,33 @@
 import os
 
 # Always prefer setuptools over distutils
+import sys
+
 from setuptools import find_packages, setup
 
 try:
-    import builtins
+    from pytorch_lightning import info, setup_tools
 except ImportError:
-    import __builtin__ as builtins
+    # alternative https://stackoverflow.com/a/67692/4521646
+    sys.path.append("pytorch_lightning")
+    import info
+    import setup_tools
 
 # https://packaging.python.org/guides/single-sourcing-package-version/
 # http://blog.ionelmc.ro/2014/05/25/python-packaging/
-PATH_ROOT = os.path.dirname(__file__)
-builtins.__LIGHTNING_SETUP__ = True
-
-import pytorch_lightning  # noqa: E402
-from pytorch_lightning.setup_tools import _load_readme_description, _load_requirements  # noqa: E402
+_PATH_ROOT = os.path.dirname(__file__)
+_PATH_REQUIRE = os.path.join(_PATH_ROOT, 'requirements')
 
 # https://setuptools.readthedocs.io/en/latest/setuptools.html#declaring-extras
 # Define package extras. These are only installed if you specify them.
 # From remote, use like `pip install pytorch-lightning[dev, docs]`
 # From local copy of repo, use like `pip install ".[dev, docs]"`
 extras = {
     # 'docs': load_requirements(file_name='docs.txt'),
-    'examples': _load_requirements(path_dir=os.path.join(PATH_ROOT, 'requirements'), file_name='examples.txt'),
-    'loggers': _load_requirements(path_dir=os.path.join(PATH_ROOT, 'requirements'), file_name='loggers.txt'),
-    'extra': _load_requirements(path_dir=os.path.join(PATH_ROOT, 'requirements'), file_name='extra.txt'),
-    'test': _load_requirements(path_dir=os.path.join(PATH_ROOT, 'requirements'), file_name='test.txt')
+    'examples': setup_tools._load_requirements(path_dir=_PATH_REQUIRE, file_name='examples.txt'),
+    'loggers': setup_tools._load_requirements(path_dir=_PATH_REQUIRE, file_name='loggers.txt'),
+    'extra': setup_tools._load_requirements(path_dir=_PATH_REQUIRE, file_name='extra.txt'),
+    'test': setup_tools._load_requirements(path_dir=_PATH_REQUIRE, file_name='test.txt')
 }
 extras['dev'] = extras['extra'] + extras['loggers'] + extras['test']
 extras['all'] = extras['dev'] + extras['examples']  # + extras['docs']
@@ -53,29 +55,35 @@
     # filter cpu only packages
     extras[ex] = [pkg for pkg in extras[kw] if not any(pgpu.lower() in pkg.lower() for pgpu in PACKAGES_GPU_ONLY)]
 
+long_description = setup_tools._load_readme_description(
+    _PATH_ROOT,
+    homepage=info.__homepage__,
+    version=info.__version__,
+)
+
 # https://packaging.python.org/discussions/install-requires-vs-requirements /
 # keep the meta-data here for simplicity in reading this file... it's not obvious
 # what happens and to non-engineers they won't know to look in init ...
 # the goal of the project is simplicity for researchers, don't want to add too much
 # engineer specific practices
 setup(
     name="pytorch-lightning",
-    version=pytorch_lightning.__version__,
-    description=pytorch_lightning.__docs__,
-    author=pytorch_lightning.__author__,
-    author_email=pytorch_lightning.__author_email__,
-    url=pytorch_lightning.__homepage__,
+    version=info.__version__,
+    description=info.__docs__,
+    author=info.__author__,
+    author_email=info.__author_email__,
+    url=info.__homepage__,
     download_url='https://github.com/PyTorchLightning/pytorch-lightning',
-    license=pytorch_lightning.__license__,
+    license=info.__license__,
     packages=find_packages(exclude=['tests', 'tests/*', 'benchmarks', 'legacy', 'legacy/*']),
-    long_description=_load_readme_description(PATH_ROOT),
+    long_description=long_description,
     long_description_content_type='text/markdown',
     include_package_data=True,
     zip_safe=False,
     keywords=['deep learning', 'pytorch', 'AI'],
     python_requires='>=3.6',
     setup_requires=[],
-    install_requires=_load_requirements(PATH_ROOT),
+    install_requires=setup_tools._load_requirements(_PATH_ROOT),
     extras_require=extras,
     project_urls={
         "Bug Tracker": "https://github.com/PyTorchLightning/pytorch-lightning/issues",