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https://github.com/wassname/pytorch-lightning.git
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619f984c36
* Option to provide seed to random generators to ensure reproducibility I added small function in utilities which imports torch, numpy, python random and sets seed for all of the libraries to ensure reproducibility of results. * Apply recommendations from core contributors on seeding 1. Moved the seeding code to another file 2. Make deterministic as a parameter for trainer class 3. Add assertions for seeding numpy 4. Added warnings 5. torch.manual_seed should be enough for seeding torch * Revert "Apply recommendations from core contributors on seeding" This reverts commit a213c8e6882eec8a9e7408b9418926d2db7c5461. * Revert "Revert "Apply recommendations from core contributors on seeding"" This reverts commit 59b2da53c62878de7aab0aa3feb3115e105eea06. * Change in test, for correct seeding * Allow seed equal to 0 * Allow seed to be uint32.max * Added deterministic to benchmarks * Cuda manual seed as in benchmark seeding * Seeding should be done before model initialization * cuda manual_seed is not necessary * Fixing seed test_cpu_lbfgs On some seeds seems like lbfgs doesn't converge. So I fixed the seed during testing. * rebasing issue with old reproducibility.py * Improved documentation and ability to seed before initializing Train class * Change in docs * Removed seed from trainer, update for documentation * Typo in the docs * Added seed_everything to _all_ * Fixing old changes * Model initialization should be earlier then Trainer * Update pytorch_lightning/trainer/__init__.py From Example to testcode Co-authored-by: Jirka Borovec <Borda@users.noreply.github.com> * Fixing according to the contributors suggestions * Moving horovod deterministic to Trainer class * deterministic flag affects horovod docs update * Improved static typing * Added deterministic to test runners of horovod It is failing on some versions, not very predictable * static seeds for horovod tests * Change for reset_seed function in tests * Seeding horovod using reset_seed from tutils * Update pytorch_lightning/trainer/__init__.py * chlog * Update trainer.py * change "testcode" to "Example" in trainer init documentation * Update pytorch_lightning/trainer/seed.py, first line in comment Co-authored-by: Jirka Borovec <Borda@users.noreply.github.com> Co-authored-by: Jirka Borovec <Borda@users.noreply.github.com> Co-authored-by: Jirka <jirka.borovec@seznam.cz> Co-authored-by: William Falcon <waf2107@columbia.edu>
76 lines
2.8 KiB
Python
76 lines
2.8 KiB
Python
"""Root package info."""
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__version__ = '0.7.6rc1'
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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__ = "PyTorch Lightning is the lightweight PyTorch wrapper for ML researchers." \
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" Scale your models. Write less boilerplate."
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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 LightningModule
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from pytorch_lightning.trainer import Trainer
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from pytorch_lightning.trainer.seed import seed_everything
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from pytorch_lightning.callbacks import Callback
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from pytorch_lightning.core import data_loader
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__all__ = [
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'Trainer',
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'LightningModule',
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'Callback',
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'data_loader'
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'seed_everything'
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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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