39 lines
1.6 KiB
Python
39 lines
1.6 KiB
Python
import time
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_this_year = time.strftime("%Y")
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__version__ = "1.6.0dev"
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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__ = f"Copyright (c) 2018-{_this_year}, {__author__}."
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__homepage__ = "https://github.com/PyTorchLightning/pytorch-lightning"
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__docs_url__ = "https://pytorch-lightning.readthedocs.io/en/stable/"
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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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__all__ = ["__author__", "__author_email__", "__copyright__", "__docs__", "__homepage__", "__license__", "__version__"]
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