82 lines
2.9 KiB
Python
82 lines
2.9 KiB
Python
"""Root package info."""
|
|
|
|
import logging
|
|
import os
|
|
import sys
|
|
import time
|
|
|
|
_this_year = time.strftime("%Y")
|
|
__version__ = '1.3.0dev'
|
|
__author__ = 'William Falcon et al.'
|
|
__author_email__ = 'waf2107@columbia.edu'
|
|
__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
|
|
"""
|
|
_root_logger = logging.getLogger()
|
|
_logger = logging.getLogger(__name__)
|
|
_logger.setLevel(logging.INFO)
|
|
|
|
# if root logger has handlers, propagate messages up and let root logger process them
|
|
if not _root_logger.hasHandlers():
|
|
_logger.addHandler(logging.StreamHandler())
|
|
_logger.propagate = False
|
|
|
|
_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',
|
|
]
|
|
|
|
# for compatibility with namespace packages
|
|
__import__('pkg_resources').declare_namespace(__name__)
|