80 lines
2.8 KiB
Python
80 lines
2.8 KiB
Python
"""Root package info."""
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__version__ = '1.0.2'
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__author__ = 'William Falcon et al.'
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__author_email__ = 'waf2107@columbia.edu'
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__license__ = 'Apache-2.0'
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__copyright__ = 'Copyright (c) 2018-2020, %s.' % __author__
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__homepage__ = 'https://github.com/PyTorchLightning/pytorch-lightning'
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# this has to be simple string, see: https://github.com/pypa/twine/issues/522
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__docs__ = (
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"PyTorch Lightning is the lightweight PyTorch wrapper for ML researchers."
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" Scale your models. Write less boilerplate."
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)
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__long_docs__ = """
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Lightning is a way to organize your PyTorch code to decouple the science code from the engineering.
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It's more of a style-guide than a framework.
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In Lightning, you organize your code into 3 distinct categories:
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1. Research code (goes in the LightningModule).
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2. Engineering code (you delete, and is handled by the Trainer).
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3. Non-essential research code (logging, etc. this goes in Callbacks).
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Although your research/production project might start simple, once you add things like GPU AND TPU training,
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16-bit precision, etc, you end up spending more time engineering than researching.
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Lightning automates AND rigorously tests those parts for you.
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Overall, Lightning guarantees rigorously tested, correct, modern best practices for the automated parts.
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Documentation
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-------------
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- https://pytorch-lightning.readthedocs.io/en/latest
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- https://pytorch-lightning.readthedocs.io/en/stable
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"""
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import logging as python_logging
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_logger = python_logging.getLogger("lightning")
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_logger.addHandler(python_logging.StreamHandler())
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_logger.setLevel(python_logging.INFO)
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try:
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# This variable is injected in the __builtins__ by the build
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# process. It used to enable importing subpackages of skimage when
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# the binaries are not built
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__LIGHTNING_SETUP__
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except NameError:
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__LIGHTNING_SETUP__ = False
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if __LIGHTNING_SETUP__:
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import sys # pragma: no-cover
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sys.stdout.write(f'Partial import of `{__name__}` during the build process.\n') # pragma: no-cover
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# We are not importing the rest of the lightning during the build process, as it may not be compiled yet
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else:
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from pytorch_lightning.core import LightningDataModule, LightningModule
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from pytorch_lightning.callbacks import Callback
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from pytorch_lightning.trainer import Trainer
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from pytorch_lightning.utilities.seed import seed_everything
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from pytorch_lightning import metrics
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__all__ = [
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'Trainer',
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'LightningDataModule',
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'LightningModule',
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'Callback',
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'seed_everything',
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'metrics',
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]
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# necessary for regular bolts imports. Skip exception since bolts is not always installed
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try:
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from pytorch_lightning import bolts
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except ImportError:
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pass
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# __call__ = __all__
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# for compatibility with namespace packages
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__import__('pkg_resources').declare_namespace(__name__)
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