add CLI test for examples (#2285)
* cli examples * ddp * CI * CI * req * tests * skip DDP Co-authored-by: William Falcon <waf2107@columbia.edu>
This commit is contained in:
Jirka Borovec
GitHub
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6673fc9a0b
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4e13e419ea
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@ -36,9 +36,11 @@ steps:
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- pip install -r ./requirements/base.txt --user -q
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- pip install -r ./requirements/devel.txt --user -q
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#- pip install -r ./requirements/docs.txt --user -q
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- pip install -r ./requirements/examples.txt --user -q
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- pip list
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- python -c "import torch ; print(' & '.join([torch.cuda.get_device_name(i) for i in range(torch.cuda.device_count())]) if torch.cuda.is_available() else 'only CPU')"
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- coverage run --source pytorch_lightning -m py.test pytorch_lightning tests benchmarks -v --durations=25 # --flake8
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- coverage run --source pytorch_lightning -m py.test pytorch_lightning tests -v --durations=25 # --flake8
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- python -m py.test benchmarks pl_examples -v --maxfail=2 --durations=0 # --flake8
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#- cd docs; make doctest; make coverage
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- coverage report
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- codecov --token $CODECOV_TOKEN # --pr $DRONE_PULL_REQUEST --build $DRONE_BUILD_NUMBER --branch $DRONE_BRANCH --commit $DRONE_COMMIT --tag $DRONE_TAG
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@ -4,13 +4,10 @@ Runs a model on the CPU on a single node.
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import os
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from argparse import ArgumentParser
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import numpy as np
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import torch
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import pytorch_lightning as pl
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from pytorch_lightning import Trainer, seed_everything
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from pl_examples.models.lightning_template import LightningTemplateModel
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pl.seed_everything(234)
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seed_everything(234)
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def main(args):
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@ -26,7 +23,7 @@ def main(args):
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# ------------------------
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# 2 INIT TRAINER
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# ------------------------
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trainer = pl.Trainer.from_argparse_args(args)
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trainer = Trainer.from_argparse_args(args)
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# ------------------------
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# 3 START TRAINING
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@ -34,7 +31,7 @@ def main(args):
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trainer.fit(model)
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if __name__ == '__main__':
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def run_cli():
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# ------------------------
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# TRAINING ARGUMENTS
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# ------------------------
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@ -44,10 +41,14 @@ if __name__ == '__main__':
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# each LightningModule defines arguments relevant to it
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parser = LightningTemplateModel.add_model_specific_args(parent_parser, root_dir)
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parser = pl.Trainer.add_argparse_args(parser)
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parser = Trainer.add_argparse_args(parser)
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args = parser.parse_args()
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# ---------------------
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# RUN TRAINING
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# ---------------------
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main(args)
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if __name__ == '__main__':
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run_cli()
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@ -4,18 +4,13 @@ Runs a model on a single node across multiple gpus.
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import os
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from argparse import ArgumentParser
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import numpy as np
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import torch
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import pytorch_lightning as pl
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from pytorch_lightning import Trainer, seed_everything
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from pl_examples.models.lightning_template import LightningTemplateModel
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SEED = 2334
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torch.manual_seed(SEED)
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np.random.seed(SEED)
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seed_everything(234)
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def main(hparams):
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def main(args):
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"""
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Main training routine specific for this project
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:param hparams:
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@ -23,17 +18,12 @@ def main(hparams):
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# ------------------------
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# 1 INIT LIGHTNING MODEL
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# ------------------------
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model = LightningTemplateModel(**vars(hparams))
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model = LightningTemplateModel(**vars(args))
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# ------------------------
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# 2 INIT TRAINER
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# ------------------------
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trainer = pl.Trainer(
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max_epochs=hparams.epochs,
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gpus=hparams.gpus,
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distributed_backend=hparams.distributed_backend,
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precision=16 if hparams.use_16bit else 32,
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)
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trainer = Trainer.from_argparse_args(args)
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# ------------------------
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# 3 START TRAINING
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@ -41,7 +31,7 @@ def main(hparams):
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trainer.fit(model)
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if __name__ == '__main__':
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def run_cli():
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# ------------------------
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# TRAINING ARGUMENTS
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# ------------------------
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@ -50,32 +40,16 @@ if __name__ == '__main__':
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root_dir = os.path.dirname(os.path.realpath(__file__))
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parent_parser = ArgumentParser(add_help=False)
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# gpu args
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parent_parser.add_argument(
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'--gpus',
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type=int,
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default=2,
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help='how many gpus'
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)
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parent_parser.add_argument(
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'--distributed_backend',
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type=str,
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default='dp',
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help='supports four options dp, ddp, ddp2, ddp_spawn, ...',
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choices=['dp', 'ddp', 'ddp2', 'ddp_spawn', 'ddp_cpu'],
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)
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parent_parser.add_argument(
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'--use_16bit',
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dest='use_16bit',
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action='store_true',
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help='if true uses 16 bit precision'
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)
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# each LightningModule defines arguments relevant to it
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parser = LightningTemplateModel.add_model_specific_args(parent_parser, root_dir)
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hyperparams = parser.parse_args()
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parser = Trainer.add_argparse_args(parser)
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args = parser.parse_args()
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# ---------------------
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# RUN TRAINING
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# ---------------------
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main(hyperparams)
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main(args)
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if __name__ == '__main__':
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run_cli()
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@ -4,31 +4,28 @@ Multi-node example (GPU)
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import os
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from argparse import ArgumentParser
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import numpy as np
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import torch
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import pytorch_lightning as pl
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from pytorch_lightning import Trainer, seed_everything
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from pl_examples.models.lightning_template import LightningTemplateModel
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SEED = 2334
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torch.manual_seed(SEED)
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np.random.seed(SEED)
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seed_everything(234)
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def main(hparams):
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def main(args):
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"""Main training routine specific for this project."""
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# ------------------------
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# 1 INIT LIGHTNING MODEL
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# ------------------------
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model = LightningTemplateModel(hparams)
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model = LightningTemplateModel(args)
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# ------------------------
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# 2 INIT TRAINER
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# ------------------------
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trainer = pl.Trainer(
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gpus=2,
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num_nodes=2,
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distributed_backend='ddp2'
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trainer = Trainer(
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gpus=args.gpus,
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num_nodes=args.num_nodes,
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distributed_backend='ddp2',
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max_epochs=args.max_epochs,
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max_steps=args.max_steps,
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)
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# ------------------------
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@ -37,15 +34,20 @@ def main(hparams):
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trainer.fit(model)
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if __name__ == '__main__':
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def run_cli():
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root_dir = os.path.dirname(os.path.realpath(__file__))
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parent_parser = ArgumentParser(add_help=False)
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# each LightningModule defines arguments relevant to it
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parser = LightningTemplateModel.add_model_specific_args(parent_parser, root_dir)
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hyperparams = parser.parse_args()
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parser = Trainer.add_argparse_args(parser)
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args = parser.parse_args()
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# ---------------------
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# RUN TRAINING
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# ---------------------
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main(hyperparams)
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main(args)
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if __name__ == '__main__':
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run_cli()
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@ -4,31 +4,28 @@ Multi-node example (GPU)
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import os
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from argparse import ArgumentParser
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import numpy as np
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import torch
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import pytorch_lightning as pl
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from pl_examples.models.lightning_template import LightningTemplateModel
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from pytorch_lightning import Trainer, seed_everything
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SEED = 2334
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torch.manual_seed(SEED)
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np.random.seed(SEED)
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seed_everything(234)
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def main(hparams):
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def main(args):
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"""Main training routine specific for this project."""
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# ------------------------
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# 1 INIT LIGHTNING MODEL
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# ------------------------
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model = LightningTemplateModel(hparams)
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model = LightningTemplateModel(args)
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# ------------------------
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# 2 INIT TRAINER
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# ------------------------
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trainer = pl.Trainer(
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gpus=2,
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num_nodes=2,
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distributed_backend='ddp'
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trainer = Trainer(
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gpus=args.gpus,
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num_nodes=args.num_nodes,
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distributed_backend='ddp',
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max_epochs=args.max_epochs,
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max_steps=args.max_steps,
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)
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# ------------------------
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@ -37,15 +34,20 @@ def main(hparams):
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trainer.fit(model)
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if __name__ == '__main__':
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def run_cli():
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root_dir = os.path.dirname(os.path.realpath(__file__))
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parent_parser = ArgumentParser(add_help=False)
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# each LightningModule defines arguments relevant to it
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parser = LightningTemplateModel.add_model_specific_args(parent_parser, root_dir)
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hyperparams = parser.parse_args()
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parser = Trainer.add_argparse_args(parser)
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args = parser.parse_args()
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# ---------------------
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# RUN TRAINING
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# ---------------------
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main(hyperparams)
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main(args)
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if __name__ == '__main__':
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run_cli()
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@ -0,0 +1,47 @@
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from unittest import mock
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import pytest
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import torch
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@pytest.mark.parametrize('cli_args', ['--max_epochs 1 --max_steps 3'])
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def test_cpu_template(cli_args):
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"""Test running CLI for an example with default params."""
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from pl_examples.basic_examples.cpu_template import run_cli
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cli_args = cli_args.split(' ') if cli_args else []
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with mock.patch("argparse._sys.argv", ["any.py"] + cli_args):
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run_cli()
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@pytest.mark.parametrize('cli_args', ['--max_epochs 1 --max_steps 3'])
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@pytest.mark.skipif(not torch.cuda.is_available(), reason="test requires GPU machine")
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def test_gpu_template(cli_args):
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"""Test running CLI for an example with default params."""
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from pl_examples.basic_examples.gpu_template import run_cli
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cli_args = cli_args.split(' ') if cli_args else []
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with mock.patch("argparse._sys.argv", ["any.py"] + cli_args):
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run_cli()
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# @pytest.mark.parametrize('cli_args', ['--max_epochs 1 --max_steps 3 --num_nodes 1 --gpus 2'])
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# @pytest.mark.skipif(torch.cuda.device_count() < 2, reason="test requires multi-GPU machine")
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# def test_multi_node_ddp(cli_args):
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# """Test running CLI for an example with default params."""
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# from pl_examples.basic_examples.multi_node_ddp_demo import run_cli
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#
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# cli_args = cli_args.split(' ') if cli_args else []
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# with mock.patch("argparse._sys.argv", ["any.py"] + cli_args):
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# run_cli()
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# @pytest.mark.parametrize('cli_args', ['--max_epochs 1 --max_steps 3 --num_nodes 1 --gpus 2'])
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# @pytest.mark.skipif(torch.cuda.device_count() < 2, reason="test requires multi-GPU machine")
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# def test_multi_node_ddp2(cli_args):
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# """Test running CLI for an example with default params."""
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# from pl_examples.basic_examples.multi_node_ddp2_demo import run_cli
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#
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# cli_args = cli_args.split(' ') if cli_args else []
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# with mock.patch("argparse._sys.argv", ["any.py"] + cli_args):
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# run_cli()
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