import logging
import os
import sys
from argparse import ArgumentParser

from kerosene.configs.configs import RunConfiguration

sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)) + '/../../../')
import torchvision
from torchvision.transforms import Compose, ToTensor, Normalize

from kerosene.configs.parsers import YamlConfigurationParser
from kerosene.events import Event
from kerosene.dataloaders.factories import DataloaderFactory
from kerosene.events.handlers.console import ConsoleLogger
from kerosene.events.handlers.visdom.config import VisdomConfiguration
from kerosene.events.handlers.visdom.visdom import VisdomLogger
from kerosene.events.preprocessors.visdom import PlotAllModelStateVariables
from kerosene.training.trainers import ModelTrainerFactory
from tests.functionals.models import SimpleNet
from tests.functionals.distributed.mnist_trainer import MNISTTrainer


class ArgsParserFactory(object):

    @staticmethod
    def create_parser():
        parser = ArgumentParser(description='DeepNormalize Training')
        parser.add_argument("--use_amp", dest="use_amp", action="store_true", default=True)
        parser.add_argument("--amp-opt-level", dest="amp_opt_level", type=str, default="O2",
                            help="O0 - FP32 training, O1 - Mixed Precision (recommended), O2 - Almost FP16 Mixed Precision, O3 - FP16 Training.")
        parser.add_argument("--local_rank", dest="local_rank", default=0, type=int, help="The local_rank of the GPU.")
        return parser


if __name__ == '__main__':
    logging.basicConfig(level=logging.INFO)
    CONFIG_FILE_PATH = "config.yml"
    args = ArgsParserFactory.create_parser().parse_args()
    run_config = RunConfiguration(args.use_amp, args.amp_opt_level, args.local_rank)

    model_trainer_config, training_config = YamlConfigurationParser.parse(CONFIG_FILE_PATH)

    # Initialize the dataset. This is the only part the user must define manually.
    train_dataset = torchvision.datasets.MNIST('./files/', train=True, download=True, transform=Compose(
        [ToTensor(), Normalize((0.1307,), (0.3081,))]))
    test_dataset = torchvision.datasets.MNIST('./files/', train=False, download=True, transform=Compose(
        [ToTensor(), Normalize((0.1307,), (0.3081,))]))

    # Initialize loaders.
    train_loader, valid_loader = DataloaderFactory(train_dataset, test_dataset).create(run_config, training_config)

    # Initialize the loggers.
    if run_config.local_rank == 0:
        visdom_logger = VisdomLogger(VisdomConfiguration.from_yml(CONFIG_FILE_PATH))

    # Initialize the model trainers.
    model_trainer = ModelTrainerFactory(model=SimpleNet()).create(model_trainer_config, run_config)

    # Train with the training strategy.
    if run_config.local_rank == 0:
        trainer = MNISTTrainer(training_config, model_trainer, train_loader, valid_loader, run_config) \
            .with_event_handler(ConsoleLogger(), Event.ON_EPOCH_END) \
            .with_event_handler(visdom_logger, Event.ON_EPOCH_END, PlotAllModelStateVariables()) \
            .train(training_config.nb_epochs)
    else:
        trainer = MNISTTrainer(training_config, model_trainer, train_loader, valid_loader, run_config) \
            .train(training_config.nb_epochs)

227b45c6f243b68b6b43a2943e51de2d71291f78
<mask token>


class ArgsParserFactory(object):
    <mask token>


<mask token>

<mask token>


class ArgsParserFactory(object):

    @staticmethod
    def create_parser():
        parser = ArgumentParser(description='DeepNormalize Training')
        parser.add_argument('--use_amp', dest='use_amp', action=
            'store_true', default=True)
        parser.add_argument('--amp-opt-level', dest='amp_opt_level', type=
            str, default='O2', help=
            'O0 - FP32 training, O1 - Mixed Precision (recommended), O2 - Almost FP16 Mixed Precision, O3 - FP16 Training.'
            )
        parser.add_argument('--local_rank', dest='local_rank', default=0,
            type=int, help='The local_rank of the GPU.')
        return parser


<mask token>

<mask token>
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)) + '/../../../')
<mask token>


class ArgsParserFactory(object):

    @staticmethod
    def create_parser():
        parser = ArgumentParser(description='DeepNormalize Training')
        parser.add_argument('--use_amp', dest='use_amp', action=
            'store_true', default=True)
        parser.add_argument('--amp-opt-level', dest='amp_opt_level', type=
            str, default='O2', help=
            'O0 - FP32 training, O1 - Mixed Precision (recommended), O2 - Almost FP16 Mixed Precision, O3 - FP16 Training.'
            )
        parser.add_argument('--local_rank', dest='local_rank', default=0,
            type=int, help='The local_rank of the GPU.')
        return parser


if __name__ == '__main__':
    logging.basicConfig(level=logging.INFO)
    CONFIG_FILE_PATH = 'config.yml'
    args = ArgsParserFactory.create_parser().parse_args()
    run_config = RunConfiguration(args.use_amp, args.amp_opt_level, args.
        local_rank)
    model_trainer_config, training_config = YamlConfigurationParser.parse(
        CONFIG_FILE_PATH)
    train_dataset = torchvision.datasets.MNIST('./files/', train=True,
        download=True, transform=Compose([ToTensor(), Normalize((0.1307,),
        (0.3081,))]))
    test_dataset = torchvision.datasets.MNIST('./files/', train=False,
        download=True, transform=Compose([ToTensor(), Normalize((0.1307,),
        (0.3081,))]))
    train_loader, valid_loader = DataloaderFactory(train_dataset, test_dataset
        ).create(run_config, training_config)
    if run_config.local_rank == 0:
        visdom_logger = VisdomLogger(VisdomConfiguration.from_yml(
            CONFIG_FILE_PATH))
    model_trainer = ModelTrainerFactory(model=SimpleNet()).create(
        model_trainer_config, run_config)
    if run_config.local_rank == 0:
        trainer = MNISTTrainer(training_config, model_trainer, train_loader,
            valid_loader, run_config).with_event_handler(ConsoleLogger(),
            Event.ON_EPOCH_END).with_event_handler(visdom_logger, Event.
            ON_EPOCH_END, PlotAllModelStateVariables()).train(training_config
            .nb_epochs)
    else:
        trainer = MNISTTrainer(training_config, model_trainer, train_loader,
            valid_loader, run_config).train(training_config.nb_epochs)

import logging
import os
import sys
from argparse import ArgumentParser
from kerosene.configs.configs import RunConfiguration
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)) + '/../../../')
import torchvision
from torchvision.transforms import Compose, ToTensor, Normalize
from kerosene.configs.parsers import YamlConfigurationParser
from kerosene.events import Event
from kerosene.dataloaders.factories import DataloaderFactory
from kerosene.events.handlers.console import ConsoleLogger
from kerosene.events.handlers.visdom.config import VisdomConfiguration
from kerosene.events.handlers.visdom.visdom import VisdomLogger
from kerosene.events.preprocessors.visdom import PlotAllModelStateVariables
from kerosene.training.trainers import ModelTrainerFactory
from tests.functionals.models import SimpleNet
from tests.functionals.distributed.mnist_trainer import MNISTTrainer


class ArgsParserFactory(object):

    @staticmethod
    def create_parser():
        parser = ArgumentParser(description='DeepNormalize Training')
        parser.add_argument('--use_amp', dest='use_amp', action=
            'store_true', default=True)
        parser.add_argument('--amp-opt-level', dest='amp_opt_level', type=
            str, default='O2', help=
            'O0 - FP32 training, O1 - Mixed Precision (recommended), O2 - Almost FP16 Mixed Precision, O3 - FP16 Training.'
            )
        parser.add_argument('--local_rank', dest='local_rank', default=0,
            type=int, help='The local_rank of the GPU.')
        return parser


if __name__ == '__main__':
    logging.basicConfig(level=logging.INFO)
    CONFIG_FILE_PATH = 'config.yml'
    args = ArgsParserFactory.create_parser().parse_args()
    run_config = RunConfiguration(args.use_amp, args.amp_opt_level, args.
        local_rank)
    model_trainer_config, training_config = YamlConfigurationParser.parse(
        CONFIG_FILE_PATH)
    train_dataset = torchvision.datasets.MNIST('./files/', train=True,
        download=True, transform=Compose([ToTensor(), Normalize((0.1307,),
        (0.3081,))]))
    test_dataset = torchvision.datasets.MNIST('./files/', train=False,
        download=True, transform=Compose([ToTensor(), Normalize((0.1307,),
        (0.3081,))]))
    train_loader, valid_loader = DataloaderFactory(train_dataset, test_dataset
        ).create(run_config, training_config)
    if run_config.local_rank == 0:
        visdom_logger = VisdomLogger(VisdomConfiguration.from_yml(
            CONFIG_FILE_PATH))
    model_trainer = ModelTrainerFactory(model=SimpleNet()).create(
        model_trainer_config, run_config)
    if run_config.local_rank == 0:
        trainer = MNISTTrainer(training_config, model_trainer, train_loader,
            valid_loader, run_config).with_event_handler(ConsoleLogger(),
            Event.ON_EPOCH_END).with_event_handler(visdom_logger, Event.
            ON_EPOCH_END, PlotAllModelStateVariables()).train(training_config
            .nb_epochs)
    else:
        trainer = MNISTTrainer(training_config, model_trainer, train_loader,
            valid_loader, run_config).train(training_config.nb_epochs)

import logging
import os
import sys
from argparse import ArgumentParser

from kerosene.configs.configs import RunConfiguration

sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)) + '/../../../')
import torchvision
from torchvision.transforms import Compose, ToTensor, Normalize

from kerosene.configs.parsers import YamlConfigurationParser
from kerosene.events import Event
from kerosene.dataloaders.factories import DataloaderFactory
from kerosene.events.handlers.console import ConsoleLogger
from kerosene.events.handlers.visdom.config import VisdomConfiguration
from kerosene.events.handlers.visdom.visdom import VisdomLogger
from kerosene.events.preprocessors.visdom import PlotAllModelStateVariables
from kerosene.training.trainers import ModelTrainerFactory
from tests.functionals.models import SimpleNet
from tests.functionals.distributed.mnist_trainer import MNISTTrainer


class ArgsParserFactory(object):

    @staticmethod
    def create_parser():
        parser = ArgumentParser(description='DeepNormalize Training')
        parser.add_argument("--use_amp", dest="use_amp", action="store_true", default=True)
        parser.add_argument("--amp-opt-level", dest="amp_opt_level", type=str, default="O2",
                            help="O0 - FP32 training, O1 - Mixed Precision (recommended), O2 - Almost FP16 Mixed Precision, O3 - FP16 Training.")
        parser.add_argument("--local_rank", dest="local_rank", default=0, type=int, help="The local_rank of the GPU.")
        return parser


if __name__ == '__main__':
    logging.basicConfig(level=logging.INFO)
    CONFIG_FILE_PATH = "config.yml"
    args = ArgsParserFactory.create_parser().parse_args()
    run_config = RunConfiguration(args.use_amp, args.amp_opt_level, args.local_rank)

    model_trainer_config, training_config = YamlConfigurationParser.parse(CONFIG_FILE_PATH)

    # Initialize the dataset. This is the only part the user must define manually.
    train_dataset = torchvision.datasets.MNIST('./files/', train=True, download=True, transform=Compose(
        [ToTensor(), Normalize((0.1307,), (0.3081,))]))
    test_dataset = torchvision.datasets.MNIST('./files/', train=False, download=True, transform=Compose(
        [ToTensor(), Normalize((0.1307,), (0.3081,))]))

    # Initialize loaders.
    train_loader, valid_loader = DataloaderFactory(train_dataset, test_dataset).create(run_config, training_config)

    # Initialize the loggers.
    if run_config.local_rank == 0:
        visdom_logger = VisdomLogger(VisdomConfiguration.from_yml(CONFIG_FILE_PATH))

    # Initialize the model trainers.
    model_trainer = ModelTrainerFactory(model=SimpleNet()).create(model_trainer_config, run_config)

    # Train with the training strategy.
    if run_config.local_rank == 0:
        trainer = MNISTTrainer(training_config, model_trainer, train_loader, valid_loader, run_config) \
            .with_event_handler(ConsoleLogger(), Event.ON_EPOCH_END) \
            .with_event_handler(visdom_logger, Event.ON_EPOCH_END, PlotAllModelStateVariables()) \
            .train(training_config.nb_epochs)
    else:
        trainer = MNISTTrainer(training_config, model_trainer, train_loader, valid_loader, run_config) \
            .train(training_config.nb_epochs)
